International Journal of Engineering, Business and Enterprises Applications issue 11 vol.1 by iasir journals

ISSN (ONLINE): 2279-0039 ISSN (PRINT): 2279-0020

Issue 11, Volume 1 & 2 December-2014-February-2015

International Journal of Engineering, Business and Enterprises Applications

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: Germany, Australia, India, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, ijebea@gmail.com

PREFACE We are delighted to welcome you to the eleventh issue of the International Journal of Engineering, Business and Enterprises Applications (IJEBEA). In recent years, advances in science, engineering, and business processes have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. IJEBEA is publishing high-quality, peer-reviewed papers covering a number of topics in the areas of business process models, engineering and enterprise applications, knowledge engineering science, modeling and designing, control and deployment techniques, e-Commerce applications, B2B and B2C applications, Protocol management and channel management, Mobility, process, engineering, security and technology management, Semantic Web and interfaces, Enterprise applications for software and web engineering,

open-source

platforms,

Human

resource

management,

Operations

management, Organizational and management issues, Supply chain management, Strategic decision support systems, Cloud computing, Risk management, Information technology, Information retrieval systems, Aspect-oriented programming, e-Libraries and e-Publishing, Data mining and warehousing, Distributed AI systems and architectures, Bioinformatics and scientific computing, Knowledge and information management techniques, and other relevant fields available in the vicinity of engineering, business and enterprise applications. The editorial board of IJEBEA is composed of members of the Teachers & Researchers community who have expertise in a variety of disciplines, including business process models, software and technology deployments, ICT solutions, and other related disciplines of engineering, business and enterprise applications. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to engineering, business and enterprises applications. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the IJEBEA for entrusting us with the important job. We are thankful to the members of the IJEBEA editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this eleventh issue, we received 98 research papers and out of which only 39 research papers are published in two volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the fields of engineering, business and enterprises applications.

This issue of the IJEBEA has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in engineering, business and enterprise applications and may open new area for research and development. We hope you will enjoy this eleventh issue of the IJEBEA and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The International Journal of Engineering, Business and Enterprises Applications (IJEBEA), ISSN (Online): 2279-0039, ISSN (Print): 2279-0020 (December-2014 to February-2015, Issue 11, Volume 1 & 2). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune- 411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg, R.K.University,Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana, India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar, Punjab(India) Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Shriram K V, Faculty Computer Science and Engineering, Amrita Vishwa Vidhyapeetham University, Coimbatore, India. Prof. (Dr.) Sohail Ayub, Department of Civil Engineering, Z.H College of Engineering & Technology, Aligarh Muslim University, Aligarh. 202002 UP-India Prof. (Dr.) Santosh Kumar Behera, Department of Education, Sidho-Kanho-Birsha University, Purulia, West Bengal, India. Prof. (Dr.) Urmila Shrawankar, Department of Computer Science & Engineering, G H Raisoni College of Engineering, Nagpur (MS), India. Prof. Anbu Kumar. S, Deptt. of Civil Engg., Delhi Technological University (Formerly Delhi College of Engineering) Delhi, India. Prof. (Dr.) Meenakshi Sood, Vegetable Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka (India) Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur, India. Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur-313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India. Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women,s College, Gardanibagh, Patna, Bihar, India. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore, India. Prof. (Dr.) Sandhya Mehrotra, Department of Biological Sciences, Birla Institute of Technology and Sciences, Pilani, Rajasthan, India. Prof. (Dr.) Dr. Ravindra Jilte, Head of the Department, Department of Mechanical Engineering,VCET, Thane-401202, India. Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) ABHIJIT MITRA , Associate Professor and former Head, Department of Marine Science, University of Calcutta , India. Prof. (Dr.) N.Ramu , Associate Professor , Department of Commerce, Annamalai University, AnnamalaiNadar-608 002, Chidambaram, Tamil Nadu , India. Prof. (Dr.) Saber Mohamed Abd-Allah, Assistant Professor of Theriogenology , Faculty of Veterinary Medicine , Beni-Suef University , Egypt. Prof. (Dr.) Ramel D. Tomaquin, Dean, College of Arts and Sciences Surigao Del Sur State University (SDSSU), Tandag City Surigao Del Sur, Philippines. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011, India. Prof. (Dr.) Sandeep Gupta, Department of Computer Science & Engineering, Noida Institute of Engineering and Technology, Gr.Noida, India. Prof. (Dr.) Mohammad Akram, Jazan University, Kingdom of Saudi Arabia.

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Prof. (Dr.) Sanjay Sharma, Dept. of Mathematics, BIT, Durg(C.G.), India. Prof. (Dr.) Manas R. Panigrahi, Department of Physics, School of Applied Sciences, KIIT University, Bhubaneswar, India. Prof. (Dr.) P.Kiran Sree, Dept of CSE, Jawaharlal Nehru Technological University, India Prof. (Dr.) Suvroma Gupta, Department of Biotechnology in Haldia Institute of Technology, Haldia, West Bengal, India. Prof. (Dr.) SREEKANTH. K. J., Department of Mechanical Engineering at Mar Baselios College of Engineering & Technology, University of Kerala, Trivandrum, Kerala, India Prof. Bhubneshwar Sharma, Department of Electronics and Communication Engineering, Eternal University (H.P), India. Prof. Love Kumar, Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), India. Prof. S.KANNAN, Department of History, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India. Prof. (Dr.) Hasrinah Hasbullah, Faculty of Petroleum & Renewable Energy Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia. Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India. Prof. (Dr.) Bhargavi H. Goswami, Department of MCA, Sunshine Group of Institutes, Nr. Rangoli Park, Kalawad Road, Rajkot, Gujarat, India. Prof. (Dr.) Essam H. Houssein, Computer Science Department, Faculty of Computers & Informatics, Benha University, Benha 13518, Qalyubia Governorate, Egypt. Arash Shaghaghi, University College London, University of London, Great Britain. Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India. Prof. (Dr.) Anand Kumar, Head, Department of MCA, M.S. Engineering College, Navarathna Agrahara, Sadahalli Post, Bangalore, PIN 562110, Karnataka, INDIA. Prof. (Dr.) Venkata Raghavendra Miriampally, Electrical and Computer Engineering Dept, Adama Science & Technology University, Adama, Ethiopia. Prof. (Dr.) Jatinderkumar R. Saini, Director (I.T.), GTU's Ankleshwar-Bharuch Innovation Sankul &Director I/C & Associate Professor, Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India. Prof. Jaswinder Singh, Mechanical Engineering Department, University Institute Of Engineering & Technology, Panjab University SSG Regional Centre, Hoshiarpur, Punjab, India- 146001. Prof. (Dr.) S.Kadhiravan, Head i/c, Department of Psychology, Periyar University, Salem- 636 011,Tamil Nadu, India. Prof. (Dr.) Mohammad Israr, Principal, Balaji Engineering College,Junagadh, Gujarat-362014, India. Prof. (Dr.) VENKATESWARLU B., Director of MCA in Sreenivasa Institute of Technology and Management Studies (SITAMS), Chittoor. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009, India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University, Coimbatore-641003,Tamil Nadu, India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066 Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057 Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India.

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Prof. (Dr.)B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India. Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India . Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Engg., Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty, Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT ,Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India.

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Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale, Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman, Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi-835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS-38655, USA Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal-India Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu-India Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India.

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Prof. (Dr.) Meghshyam K. Patil, Assistant Professor & Head, Department of Chemistry, Dr. Babasaheb Ambedkar Marathwada University, Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) K. Ramesh, Department of Chemistry, C .B . I. T, Gandipet, Hyderabad-500075 Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics , Bhilai Institute Of Technology, Durg (C.G.) 491001 Prof. (Dr.) Y.P.Singh, (Director), Somany (PG) Institute of Technology and Management, Garhi Bolni Road, Delhi-Jaipur Highway No. 8, Beside 3 km from City Rewari, Rewari-123401, India. Prof. (Dr.) MIR IQBAL FAHEEM, VICE PRINCIPAL &HEAD- Department of Civil Engineering & Professor of Civil Engineering, Deccan College of Engineering & Technology, Dar-us-Salam, Aghapura, Hyderabad (AP) 500 036. Prof. (Dr.) Jitendra Gupta, Regional Head, Co-ordinator(U.P. State Representative)& Asstt. Prof., (Pharmaceutics), Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) N. Sakthivel, Scientist - C,Research Extension Center,Central Silk Board, Government of India, Inam Karisal Kulam (Post), Srivilliputtur - 626 125,Tamil Nadu, India. Prof. (Dr.) Omprakash Srivastav, Centre of Advanced Study, Department of History, Aligarh Muslim University, Aligarh-202 001, INDIA. Prof. (Dr.) K.V.L.N.Acharyulu, Associate Professor, Department of Mathematics, Bapatla Engineering college, Bapatla-522101, INDIA. Prof. (Dr.) Fateh Mebarek-Oudina, Assoc. Prof., Sciences Faculty,20 aout 1955-Skikda University, B.P 26 Route El-Hadaiek, 21000,Skikda, Algeria. NagaLaxmi M. Raman, Project Support Officer, Amity International Centre for Postharvest, Technology & Cold Chain Management, Amity University Campus, Sector-125, Expressway, Noida Prof. (Dr.) V.SIVASANKAR, Associate Professor, Department Of Chemistry, Thiagarajar College Of Engineering (Autonomous), Madurai 625015, Tamil Nadu, India (Dr.) Ramkrishna Singh Solanki, School of Studies in Statistics, Vikram University, Ujjain, India Prof. (Dr.) M.A.Rabbani, Professor/Computer Applications, School of Computer, Information and Mathematical Sciences, B.S.Abdur Rahman University, Chennai, India Prof. (Dr.) P.P.Satya Paul Kumar, Associate Professor, Physical Education & Sports Sciences, University College of Physical Education & Sports, Sciences, Acharya Nagarjuna University, Guntur. Prof. (Dr.) Fazal Shirazi, PostDoctoral Fellow, Infectious Disease, MD Anderson Cancer Center, Houston, Texas, USA Prof. (Dr.) Omprakash Srivastav, Department of Museology, Aligarh Muslim University, Aligarh202 001, INDIA. Prof. (Dr.) Mandeep Singh walia, A.P. E.C.E., Panjab University SSG Regional Centre Hoshiarpur, Una Road, V.P.O. Allahabad, Bajwara, Hoshiarpur Prof. (Dr.) Ho Soon Min, Senior Lecturer, Faculty of Applied Sciences, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia Prof. (Dr.) L.Ganesamoorthy, Assistant Professor in Commerce, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamilnadu, India. Prof. (Dr.) Vuda Sreenivasarao, Professor, School of Computing and Electrical Engineering, Bahir Dar University, Bahirdar,Ethiopia Prof. (Dr.) Umesh Sharma, Professor & HOD Applied Sciences & Humanities, Eshan college of Engineering, Mathura, India. Prof. (Dr.) K. John Singh, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India. Prof. (Dr.) Sita Ram Pal (Asst.Prof.), Dept. of Special Education, Dr.BAOU, Ahmedabad, India.

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Prof. Vishal S.Rana, H.O.D, Department of Business Administration, S.S.B.T'S College of Engineering & Technology, Bambhori,Jalgaon (M.S), India. Prof. (Dr.) Chandrakant Badgaiyan, Department of Mechatronics and Engineering, Chhattisgarh. Dr. (Mrs.) Shubhrata Gupta, Prof. (Electrical), NIT Raipur, India. Prof. (Dr.) Usha Rani. Nelakuditi, Assoc. Prof., ECE Deptt., Vignan’s Engineering College, Vignan University, India. Prof. (Dr.) S. Swathi, Asst. Professor, Department of Information Technology, Vardhaman college of Engineering(Autonomous) , Shamshabad, R.R District, India. Prof. (Dr.) Raja Chakraverty, M Pharm (Pharmacology), BCPSR, Durgapur, West Bengal, India Prof. (Dr.) P. Sanjeevi Kumar, Electrical & Electronics Engineering, National Institute of Technology (NIT-Puducherry), An Institute of National Importance under MHRD (Govt. of India), Karaikal- 609 605, India. Prof. (Dr.) Amitava Ghosh, Professor & Principal, Bengal College of Pharmaceutical Sciences and Research, B.R.B. Sarani, Bidhannagar, Durgapur, West Bengal- 713212. Prof. (Dr.) Om Kumar Harsh, Group Director, Amritsar College of Engineering and Technology, Amritsar 143001 (Punjab), India. Prof. (Dr.) Mansoor Maitah, Department of International Relations, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 21 Praha 6 Suchdol, Czech Republic. Prof. (Dr.) Zahid Mahmood, Department of Management Sciences (Graduate Studies), Bahria University, Naval Complex, Sector, E-9, Islamabad, Pakistan. Prof. (Dr.) N. Sandeep, Faculty Division of Fluid Dynamics, VIT University, Vellore-632 014. Mr. Jiban Shrestha, Scientist (Plant Breeding and Genetics), Nepal Agricultural Research Council, National Maize Research Program, Rampur, Chitwan, Nepal. Prof. (Dr.) Rakhi Garg, Banaras Hindu University, Varanasi, Uttar Pradesh, India. Prof. (Dr.) Ramakant Pandey. Dept. of Biochemistry. Patna University Patna (Bihar)-India. Prof. (Dr.) Nalah Augustine Bala, Behavioural Health Unit, Psychology Department, Nasarawa State University, Keffi, P.M.B. 1022 Keffi, Nasarawa State, Nigeria. Prof. (Dr.) Mehdi Babaei, Department of Engineering, Faculty of Civil Engineering, University of Zanjan, Iran. Prof. (Dr.) A. SENTHIL KUMAR., Professor/EEE, VELAMMAL ENGINEERING COLLEGE, CHENNAI Prof. (Dr.) Gudikandhula Narasimha Rao, Dept. of Computer Sc. & Engg., KKR & KSR Inst Of Tech & Sciences, Guntur, Andhra Pradesh, India. Prof. (Dr.) Dhanesh singh, Department of Chemistry, K.G. Arts & Science College, Raigarh (C.G.) India. Prof. (Dr.) Syed Umar , Dept. of Electronics and Computer Engineering, KL University, Guntur, A.P., India. Prof. (Dr.) Rachna Goswami, Faculty in Bio-Science Department, IIIT Nuzvid (RGUKT), DistrictKrishna , Andhra Pradesh - 521201 Prof. (Dr.) Ahsas Goyal, FSRHCP, Founder & Vice president of Society of Researchers and Health Care Professionals Prof. (Dr.) Gagan Singh, School of Management Studies and Commerce, Department of Commerce, Uttarakhand Open University, Haldwani-Nainital, Uttarakhand (UK)-263139 (India) Prof. (Dr.) Solomon A. O. Iyekekpolor, Mathematics and Statistics, Federal University, WukariNigeria. Prof. (Dr.) S. Saiganesh, Faculty of Marketing, Dayananda Sagar Business School, Bangalore, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’S SCHOOL, ATHANI, India Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering , Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, Punjab,India

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Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology, Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura-India Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai, 400103, India, Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, TamilNadu, India Prof. (Dr.) Har Mohan Rai, Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036, India. Prof. (Dr.) Aparna Sarkar, PH.D. Physiology, AIPT, Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP, India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher, Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. .

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Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, India. Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University, Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN. Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV),Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India. Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar, PhD(CS), M.Phil(CS), MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India. Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana), India. Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College, Govind Nagar,Kanpur208006, India. Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura, India. Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura, India. Prof. (Dr.) T Venkat Narayana Rao, C.S.E, Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India. Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India. Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Prof. (Dr.) Chitranjan Agrawal, Department of Mechanical Engineering, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur- 313001, Rajasthan, India. Prof. (Dr.) Rangnath Aher, Principal, New Arts, Commerce and Science College, Parner, DistAhmednagar, M.S. India. Prof. (Dr.) Chandan Kumar Panda, Department of Agricultural Extension, College of Agriculture, Tripura, Lembucherra-799210 Prof. (Dr.) Latika Kharb, IP Faculty (MCA Deptt), Jagan Institute of Management Studies (JIMS), Sector-5, Rohini, Delhi, India. Raj Mohan Raja Muthiah, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts. Prof. (Dr.) Chhanda Chatterjee, Dept of Philosophy, Balurghat College, West Bengal, India. Prof. (Dr.) Mihir Kumar Shome , H.O.D of Mathematics, Management and Humanities, National Institute of Technology, Arunachal Pradesh, India Prof. (Dr.) Muthukumar .Subramanyam, Registrar (I/C), Faculty, Computer Science and Engineering, National Institute of Technology, Puducherry, India. Prof. (Dr.) Vinay Saxena, Department of Mathematics, Kisan Postgraduate College, Bahraich â€“ 271801 UP, India. Satya Rishi Takyar, Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh (PB) Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India.

                                           

Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:                                                    

e-Commerce applications using web services B2B and B2C applications Advanced web service technologies including security, process management and QoS Surveillance technologies and security policies Security for protocol management Resource and channel management Mobility management Network Security management Technology management Information security management Semantic web for e-Business and e-Learning e-Learning design and methodologies Instructional design methodologies Content management and development Knowledge and information management techniques Enterprise Applications for software and web engineering Open-source e-Learning platforms Internet payment systems Techniques for B2B e-Commerce e-Business models and architectures Service-oriented e-Commerce Human resource management Business-oriented and consumer-oriented e-Commerce Development of e-Business and applications Supply chain management Strategic decision support systems Enterprise resource planning and e-Business Intranet and extranet business applications Enterprise-wide client-server architectures Information systems analysis and specification Strategic issues in distributed development Semantic web technologies and cloud computing Legal aspects of e-Government Risk management Methods and tools for e-Government e-Democracy and e-Voting Operations management Information technology Information retrieval systems Aspect-oriented programming e-Libraries and e-Publishing Intelligent tutoring systems Digital libraries for e-learning Web-based learning, wikis and blogs Social networks and intelligence Social science simulation Information retrieval systems Wired and wireless data communication networks Data mining and warehousing Distributed AI systems and architectures Bioinformatics and scientific computing Knowledge and information management techniques

TABLE OF CONTENTS International Journal of Engineering, Business and Enterprises Applications (IJEBEA) ISSN (Print): 2279-0020, ISSN (Online): 2279-0039 (December-2014 to February-2015, Issue 11, Volume 1 &2) Issue 11, Volume 1 Paper Code

Paper Title

Page No.

IJEBEA 15-104

Algorithm for Content Adaptation of Multimedia Information Grigor Mihailov, Teodor Iliev, Elena Ivanova

01-07

IJEBEA 15-106

Physical and Mechanical Property Evaluation of Some Clay Deposits in Mubi for Production of Glazed Roofing Tiles S.A. AKANJI, C. NATHAN, J. WADAI

08-14

IJEBEA 15-107

PRICE LIMITS AND INFORMATIONAL EFFICIENCY Tamir Levy and Joseph Yagil

15-26

IJEBEA 15-109

Evaluation of Effect of Policy Making Models on Organizational innovation Indices Alireza Booshehri, Iraj Masoomi Baran

27-32

IJEBEA 15-110

Student Acceptance of Web-based Learning for Universities in Thailand Rungsan Suwannahong, Terawat Piboongungon and Werayuth Charoenruengkit

33-35

IJEBEA 15-112

INTEGRATING E-SERVIVE WITH A OIL REFINERY E-COMMERCE USING DATA MINING T.Saranya, J.K. Anu Shakthi Priya, K.Poornima, K. Kumar

36-39

IJEBEA 15-113

Business Correspondent (BC) Model – A Bridge between Banks and Unbanked Dr. N. Sundaram, Mr. M. Sriram

40-44

IJEBEA 15-115

Fault Detection, Protection and Monitoring of Induction Motor Using Zigbee N.SOLAIYAMMAL, N.KANAGAPRIYA

45-47

IJEBEA 15-117

LITERATURE REVIEW ON FACTORS INFLUENCING DIVIDEND DECISIONS P.G.Thirumagal, Dr. S. Vasantha

48-52

IJEBEA 15-118

Metrics and Performance Measurement of Banks J. Priyankha, Dr .S .Vasantha

53-57

IJEBEA 15-119

E-Marketing Types, Practices, Emerging Trends and Technologies: State-of-the-Art Anuja Bokhare and Pravin Metkewar

58-63

IJEBEA 15-127

Automatic Video Annotation using ECGM S. Divya Meena, S. Vidhya meena

64-67

IJEBEA 15-132

Harnessing the power of Viral Marketing through Social Media–A study on IT industry Dr.Anita Venaik

68-72

IJEBEA 15-134

A Knowledge Management approach, for Developing Research Community among Universities Dhananjay S. Deshpande, P. R. Kulkarni, Pravin S Metkewar

73-77

IJEBEA 15-135

Brain Actuated Wheelchair Using Brain Wave Sensor Aswathy M

78-82

IJEBEA 15-138

Review on E-Learning Effectiveness Models A.Bindhu, Dr. Hansa Lysander Manohar

83-88

IJEBEA 15-143

Protocol Based On Round Trip Delay and Paths for Sensor Node Failure Detection S.Sam Perinba Nayagan

89-93

IJEBEA 15-147

AN EMBEDDED REAL TIME FINGER VEIN RECOGNITION SYSTEM FOR ATM Sonu.P.Sam

94-98

Issue 11, Volume 2 Paper Code IJEBEA 15-148

Paper Title Study & Performance of Coated Cutting Tool Suraj R. Jadhav, Aamir M. Shaikh

Page No. 99-104

IJEBEA 15-149

EFFICIENT IMPLEMENTATION OF RSA ON FPGA USING VERILOG A.Santham Bharathy ME (VLSI)

105-109

IJEBEA 15-150

A Hybrid Model for Stock Market Trend Analysis Ch. Vanipriya,Thammi Reddy.K

110-115

IJEBEA 15-161

Construct of Iran’s National Innovation System Based on Innovation and Foresight (Usage of Benchmarking and Delphi Method) Saeid Ghorbani Iraj, Iraj Masoomi Baran, Mahdi Jafari Nadooshan

116-122

IJEBEA 15-162

Architectural Structure of Geographic Information System Anoop Singh, Ramanjyot Kaur

123-127

IJEBEA 15-164

Managing the Mandate: The Emerging Tool in the Indian Political Scenario Amit Kumar, Prof. Somesh Dhamija, Dr. Aruna Dhamija

128-131

IJEBEA 15-165

Industrial Democracy: an essential part of a Business Seema Rani

132-135

IJEBEA 15-166

Socioeconomic influence on farmers in seri-business from Tamil Nadu Sunitha Rani. D and Jayaraju. M, Kannan. M and Ashok Kumar. K

136-140

IJEBEA 15-167

Design and Development of an Electric Vulcanizing Machine C. Nathan, N. Jones and J. Wadai

141-146

IJEBEA 15-172

Logistics Support for Agro business in context of the Supply Chain of Perishables Md. Kamruzzaman, Md. Amirul Islam

147-152

IJEBEA 15-177

A STUDY OF ONLINE SHOPPING CONSUMER BEHAVIOUR IN CHENNAI G.R.Shalini, K.S.HemaMalini

153-157

IJEBEA 15-178

Capital Budgeting in Practice: An Explorative Study on Bangladeshi Companies Shakila Yasmin

158-163

IJEBEA 15-180

Impact of Television Advertisements on Buying Patterns of Consumer Durables in Chennai -A Retailers’ Perspective K.S.HemaMalini, Dr.R.Venkatesh

164-167

IJEBEA 15-181

INTRODUCTION TO MOBILE AD-HOC NETWORK, ITS APPLICATIONS AND STACK Vijayendra Kushwaha, Imran Khan, Neelham Singh Parihar

168-170

IJEBEA 15-185

Human Machine Interface (HMI) For DC Motor Drives with Self Generator S. Natarajan, Dr. M. AntoBennet, M. Manimaraboopathy, S. Sankararnarayan, N. Srinivasan

171-178

IJEBEA 15-187

Analysis of the consumer benefits and factors of life insurance to rural region of Odisha Bidyadhar Padhi

179-184

IJEBEA 15-189

Brand Preference in Water Purifiers G.N. Prasaath

185-188

IJEBEA 15-191

An analysis on different experiential value sought by travel website users Mrs.Veto Datta, Dr.S.Vasantha

189-192

IJEBEA 15-192

A Study of the Software Development Using Agile Divya Prakash Shrivastava

193-197

IJEBEA 15-193

VIRTUAL ENTERPRISE AND THE FAST FOOD INDUSTRY: A CASE STUDY OF FAST FOOD OPERATORS IN EDO STATE ADANNA.E. ONONIWU

198-200

IJEBEA 15-195

Study the effects of job stress on resistance of employees against changes in the Gymnastics Federation of Tehran Fatemeh Kiani Nejad , Dr. Ahmadreza Kasraee

201-205

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Algorithm for Content Adaptation of Multimedia Information Grigor Mihailov, Teodor Iliev, Elena Ivanova Department of Telecommunication University of Ruse 8 Studentska Str. BULGARIA Abstract: The Universal Multimedia Access (UMA) addresses the delivery of multimedia resources under different and varying network conditions, diverse terminal equipment capabilities, specific user or author preferences and needs and usage environment conditions. UMA refers to the truly ubiquitous access to and consumption of multimedia content, aiming at guaranteeing unrestricted access to multimedia content from any device, through any network independently of the original content format with guarantees and efficiently and satisfying user preferences and usage environment conditions. Keywords: multimedia; network; UMA; adaptation; transformation. I.

Introduction

The variety of multimedia information is enormous nowadays. Everyone has a camera, a scanner or another device that almost instantly generates multimedia content. The most of the content author wishes to share its masterpiece. But the variety of terminals and networks may be a problem if he wants everyone to see his work with the best possible quality. This problem will increasingly grow as industry releases more enabling technologies. Late advances in Telecommunications are creating a set of technologies that will enable the development of an all-new set of services with completely different requirements, terminals, capabilities and networks through which terminals will access services in very different conditions. With the advances in Telecommunications, new protocols and networks are being standardized, providing larger bandwidth and better access (e.g., UMTS, broadband networks, LTE, etc.). The different characteristics of these networks may impose restrictions regarding the content delivery process, e.g. related to bandwidth, error protection, quality of service, etc. Networks can be very different in several aspects. [1] The recent advances in storage and acquisition technologies are driving the creation and dissemination of large amounts of rich multimedia content. As a result of these advances, there is a growing mismatch between the rich content that is available and the capabilities of the terminals and networks available to the users to access and process the content available. Today, when an author creates some multimedia content, he may have to create several variations of the same content, even with different layouts, different image resolutions and even different media types in order to reach the largest possible number of terminals. [2] This scenario describes todayâ&#x20AC;&#x2122;s situation of accessing multimedia content from any terminal (Fig. 1). When a terminal accesses content to which it was not designed for, the user experience is not so good. Figure 1: Accessing multimedia content from any terminal and any network. Laptops

Work stations

Smartphones Multimedia servers

Home computers Network

Intermediate servers

Network

Multimedia content

Tablets

Internet TV PDA

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Grigor Mihailov et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 01-07

The user may experience long delays if the content is adapted ignoring the characteristics of the involved parts making its visual presentation less impacting, etc. The scenario described misses a bridging element between all the components involved, which should take into account their characteristics and assure an efficient and consistent inter-working. An efficient way to access any information from any terminal, allowing the delivery of any content (or an adaptation of it) to the user should be provided. Terminals can be very different in terms of available memory, display colors, etc.; network access can also be very different in terms of bandwidth and so on; each piece of content may be designed with different bandwidth, memory, visualization, rendering, streaming, etc. requirements. The access to multimedia information by any terminal through any network is a new concept referred by the scientific community as Universal Multimedia Access. The objective of UMA technology is to make available different presentations of the same information, more or less complex e.g. in terms of media types, suiting different terminals, networks and user preferences. II.

Universal Multimedia Access

The purpose of UMA is to enable access to any multimedia content over any type of network with any device, anytime and anywhere. The initial motivation of UMA was to enable terminals with limited communication processing, storage and display capabilities to access rich multimedia contents. Some definitions of UMA are described in the following paragraph: [3]  The concept of UMA is to enable access to any multimedia content over any type of network, such as Internet, Wireless LAN or others, from any type of terminals with varying capabilities such as mobile phones, tablets, PDA, personal computers, and television sets;  UMA refers to the framework where information is accessed in a suitable form and modality under the current complex and dynamic usage environment such as devices, networks, terminals, preferences, personalization, and other factors of usage environment;  The primary function of UMA services is to provide the best QoS or QoE by selecting appropriate content formats, or adapting the content format directly, to meet the playback environment, or to adapt the content playback environment to accommodate the content;  Universal Multimedia Access: Any content should be available anytime, anywhere, even if after adaptation. This may require that content be transcoded form, for example, one bit rate or format to another or transcoded across modalities; e.g., text to speech. UMA concentrates on altering the content to meet the limitations of a user's terminal or network. The system of UMA allows any customer to reach any multimedia information. The main actors in UMA systems are as follows: [4]  Author: The people or organization who creates the multimedia contents;  Provider: The people or organization who provides multimedia content delivery service;  Consumer: The people or organization who consumes the multimedia contents. Professional broadcast programs could involve several Authors and Providers, while personal home video could be created and delivered by the same person. The main objective in UMA systems is to maximize the final result satisfaction of all these actors. To achieve UMA systems, multimedia contents must be adapted to the Consumer especially in terms of terminal and network conditions and user characteristics. For easier adaptation of the desired content to the Consumer, it is preferable to have descriptions to fill the gap between media format and terminal, network, user characteristics. Figure 2: Overview of the system of UMA. Provider User Author

Multimedia content Adaptation of information

Production

Adapted multimedia content

Content description

Service provider descriptions

User descriptions

Network characteristics

QoS

Equipment characteristics

Service provider preferences

User preferences

Restrictions

User environment

III.

Content Adaptation in UMA Systems

Different types of contents in various formats are delivered by many service providers via servers and networks to various terminals of various users. Therefore, a large number of parameters of the contents, service providers and usage environments need to be taken into consideration for adaptation.

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Grigor Mihailov et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 01-07

Figure 3 illustrates a diagram of content adaptation framework in UMA systems. The multimedia contents and all the descriptions are input into the adaptation engine, and the adaptation engine adapts the contents by selection and transformation based on all descriptions to form a multimedia content adapted to the consumer. Figure 3: Content adaptation framework in UMA systems. Description characteristics

Original multimedia content

Adaptation engine

Adapted multimedia content

A. Adaptation engines in UMA systems There are numerous ways to categorize adaptation type, for example, if: the adaptation should be processed in real-time or not; the target application is push or pull; the adaptation are performed automatically or manually; the adaptation process requires a lot of computation or not and the adaptation is in signal level, perception level or semantic level, and so on. The focus is on what type of multimedia contents the consumer accesses. The first point is if the data to access consists of just one source or many sources. The second point is if there are variations of each content created from a single source or not. Several alternative versions or options derived from a single source may exist (e.g. same program with different frame rate, resolution, number of colors, languages, etc.). [5] B. Transformation engine Figure 4 illustrates the diagram of the content adaptation process by transformation engine. The transformation engine transforms a single multimedia content into an adapted multimedia content on-the-fly. The descriptions support the adaptation process to provide the best experience to the user and to reduce calculation cost enough to achieve a real-time adaptation. One relevant application could be broadcast video content delivery to mobile users. [6] The advantages of content adaptation by transformation are that the storage cost is low because only a single content variation needs to be stored and that an accurate adaptation to every device can be performed considering the device capability in real-time. But the drawbacks are that the engine could have only limited operations that enable real-time or low delay processing, unless it would cause long delay or an engine with quite a lot of computational power would be necessary. Figure 4: Content adaptation process by transformation engine. Description characteristics

Original multimedia content

Transformation engine

Adapted multimedia content

Some content transformation examples are described in the following:  Adjustment of network capabilities (e.g. bandwidth, delay, error rate) and terminal capabilities (e.g. screen size, terminal power, memory, CPU, decoder) between the service provider and consumer;  Adjustment of content presentation (visualization) based on user preferences (e.g. preferred mode e.g. small image high frame-rate or large image low frame-rate, all content or summarized content), difficulties in vision or hearing and natural environments (e.g. location, time, weather, color temperature adjustment);  Content summarization (e.g. 1 hour news in 5 minutes) and highlight (e.g. goal scene in a football match). Convert the spatial resolution and color depth of the image/video e.g. VGA to QVGA, NTSC to PAL, 24bit to 8bit color depth, color to grayscale, etc. Convert the temporal resolution of the video e.g. 30fps to 24 bps (NTSC

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to PAL), 30 fps to 10 fps, etc. Coding parameter conversion frame-rate (e.g. 30 fps to 24 fps). The purpose is the best resolution to the client. [6, 7] C. Variation selection engine The variation selection engine selects the best variation from all variations delivered from a single multimedia content. The descriptions support the selection process to provide the best experience to the user. The most typical example of content adaptation by variation selection would be a web site preparing several variations for each type of terminal (PC, PDA, mobile phone) and delivering automatically the most appropriate one by analyzing environmental variables (bandwidth, delay, etc.) Figure 5 illustrates the diagram of the content adaptation process by variation selection engine: [8] The advantages of content adaptation by selection are that the adaptation process is very quick because the system just has to select the best variation and deliver it to the consumer. At the same time, the system needs just a small amount of computational resources for adaptation. Figure 5: The content adaptation process by variation selection engine. Description characteristics

Original multimedia content Modality 1 Modality 2 Modality 3

...

Modality selection engine

Modality m

Selected modalities

On the other hand, the main drawback is that the engine could have only limited variations and sometimes there would be no content that fits the user condition. It is possible to solve this problem by applying a transformation engine to the selected variation in order to increase the consumer’s experience. Other problems are that the storage cost and creation cost becomes higher. Furthermore, the management cost is high. If there are some changes on the original content, all of the variations also need to be changed, which requires quite a lot of time and power. [9] Variation selection examples are basically the same as those of transformation engine. The main difference is that the variations are created beforehand. What to select includes best spatial resolution, temporal resolution, modality, content length, coding format, coding parameters, presentation, content length, important spatial region, temporal region, color selection for the user, and so on. IV.

Layout Control for Balancing Video and Metadata

There are two aspects for determining the layout of how to present the video content and its metadata; the pane layout (horizontal, vertical, etc…) and the size of each pane (video pane, metadata pane). [10] There are many possibilities in how to present the video and the metadata in a fixed screen size. For example, locate the video content and fill all the rest of the screen with text metadata, fill thumbnails in redundant spaces, overlay some text over the video content to maximize the video content size, determine the area for presenting video and metadata not only in rectangular form but also in any form like triangular, circular, and so on. There are four possibilities shown in Figure 6 to simplify the problem; the video, the metadata to be located up, down, left, or right. The screen is simply divided into two parts, horizontally or vertically, and one part is assigned for video and the other for metadata. [11, 12] To optimize the content adaptation process, a quality metric to evaluate how well the content is adapted meeting the constraints on both the provider side (e.g. restrictions on scaling rate) and the consumer side (e.g. device capabilities, user preferences) is emerging. Figure 6: Possibilities simplifying the problem. Metadata Video content Video content

Metadata Horizontal А

Video content

Vertical А

Horizontal B

Metadata

Video content

Vertical B

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We define a value of the total adapted content to evaluate the fidelity of the overall adapted content compared with the original content. Thus, the optimization process can be described as determining the way of adaptation that maximizes the total content value meeting the given constraints. For expressing the balance of the size of each pane, we define a variable p as a normalized value of the video content pane size (Fig. 7). This value is normalized to be between 0 (whole screen is for metadata) and 1 (whole screen is for video). The balance of the video content pane and metadata pane can be expressed as p: 1− p in both horizontal and vertical layout case. [13] Figure 7: Normalized value of the video content pane size.

1-p

Metadata Vm(p)

Video content Vc(p)

Once the pane size to present the video and that of the metadata are determined, the total content value can be calculated by optimizing the content value of each modality in each given pane size. This means that the value of the total content V can be defined as a function of p. There are three parameters that affect the total content value. The video content value, the metadata value, and the preferred balance of them are the three parameters. The total content value function V(p) is modelled as follows: (1) V ( p)  f (Vc ( p), Vm ( p), w) (0  p  1) , where Vc(p) - value of the adapted video content; Vm(p) - value of the adapted metadata; w - importance of the video content (normalized). [14] The total content function V(p) evaluates the balance between the adapted video content and the adapted metadata and also takes into account the subjective importance of each modality for the consumer. Considering the video content value and metadata value, as both the video content pane size and the metadata pane size can be calculated from the screen size and the given p, the value of both modalities can also be expressed as a function of p. The total content value can be determined by calculating the maximum total content value for both horizontal and vertical layout and selecting the one with higher content value. The total content value V(p) becomes: (2) V ( p)  maxVH ( p),VV ( p) , where VH(p) - total content value in horizontal layout; VV(p) - total content value in vertical layout. Fig. 8 illustrates the video content pane size and metadata pane size for both horizontal and vertical layout. By applying the equation (2), the total content value for both layout cases, VH(p) and VV(p), can be modeled as: [15, 11] VH ( p)  f (c( pX , Y ), m((1  p) X , Y ), w) , (3) VV ( p)  f (c( X , pY ), m( X , (1  p)Y ), w) where (X, Y) - screen size; c(x, y) - maximum video content value in pane size (x, y) and m(x, y) - maximum metadata value in pane size (x, y). The value of Vc(p) and Vm(p) can be determined using the following conditions: if (V ( p)  V H ( p))

Vc ( p),Vm ( p)  c( pX , Y ), m((1  p) X , Y )

(4)

else

Vc ( p),Vm ( p)  c( X , pY ), m( X , (1  p)Y ) Figure 8: Video content pane size and metadata pane size for both horizontal and vertical layout. pX

(1-p)X

Metadata m((1-p)X,Y)

Metadata m(X,(1-pY)

(1-p)Y

Video content c(X,pY)

Video content c(pX,Y)

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Grigor Mihailov et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 01-07

Considering the browsing preferences, we categorize the approaches for optimizing content adaptation into three types. These approaches are based on which of the following are most important for the consumer; the total amount of the presented information, the preferred modality (video or text), or the layout of the whole content. The approach applied for the adaptation process is automatically determined by looking the browsing preferences, to be specific, the value p and w. [13, 11] A. Information-centric approach. (p = variable, 0 < w < 1) Information-centric approach is applied in case the user wants to have as much valuable information as possible regardless of the modality. In this approach, the video content and the metadata are balanced adaptively in a way that maximizes information throughput. This approach is applied when value p is flexible and value w ranges 0<w<1. B. Modality-centric approach. ( p = variable, w = 0 or 1 ) Modality-centric approach is applied when the user wants to browse mainly a single modality and the other modality is just optional for him/her. The typical case for this approach would be the case what is important for the user is just to watch a video content and he/she doesnâ&#x20AC;&#x2122;t care about metadata at all. This approach is applied when w = 0 or w = 1, and p is flexible. C. Layout-centric approach (p = const) Layout-centric approach is applied when the user wants to have video and metadata presented with a fixed balance and/or in a fixed position. A typical example would be when he/she feels relaxed when metadata is located under the video, or he/she always like to have the screen with two third with the video content and the rest with metadata. This approach is applied when a constant value p is given. Figure 9: Algorithm for determing the optimal layout and value p. Start

Initialization (p=1, Vmax=0, s=1/x)

Calculation of optimal values for video content and metadata. Calculation of VH(p)

Calculation of optimal values for video content and metadata. Calculation of VV(p)

Yes

VH(p) > VV(p)

V(p) = VH(p) Horizontal divide

V(p) = VV(p) Vertical divide

Yes

V(p) > Vmax

Vmax = V(p) pmax = p

Video area pmax

p=p-s

Metadata area (1-pmax)

Yes

p<0

End

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Grigor Mihailov et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 01-07

Figure 9 describes the detailed algorithm to find the optimal layout and value p. The total content value for every p is calculated and the value p which maximizes the total content value is selected as the optimal balance between video and metadata pane. V.

Conclusion

The paper presented an analysis of three approaches: Adaptation engines in Universal Multimedia Access systems, transformation engine and variation selection engine. The next part illustrates the necessity of simplify the problem, the two options the video, the metadata to be located up, down, left, or right, division into two parts, horizontally or vertically, and one part is assigned for video and the other for metadata. The mathematical expressions described in (2), (3), (4), determine the pane layout. The purpose multimedia information to be adapted is reached with the mathematical expressions and evaluation approach, modified algorithm. The algorithm determines the optimal layout of multimedia content and the value of the adaptive variable p, as normalized parameter of video. References [1]

Mihaylov Gr., Video transmission over LTE mobile network, Proceedings of the Scientific Student Session 2010, University of Ruse, Ruse, Bulgaria, pp. 59-63, 2010 in Bulgarian.

[2]

Kanev J., Sadinov S., Analysis of current methods and technologies for encoding, distribution and consumption of IPTV services, International Conference ICEST 2012, Sofia, Bulgaria, pp. 74-76, 2012

[3]

Kasutani E., Investigation Report on Universal Multimedia Access, Lausanne EPFL, 2006.

[4]

Kasutani E., New Frontiers in Universal Multimedia Access, Lausanne EPFL, 2006.

[5]

Andrew P., Tor H., Content Adaptation for a Streaming Environment Enabling Universal Multimedia Access, white paper.

[6]

Thang T. C., Jung Y. J., Lee J. W., Ro Y. M., Modality Conversion for Universal Multimedia Services, white paper.

[7]

Li-Qun C., Xing X., Xin F. ,Wei-Ying M., Hong-Jiang Z., He-Qin Z., A visual attention model for adapting images on small displays, Multimedia Systems, Springer-Verlag, 2003.

[8]

Thang T. C., Jung Y. J., Ro Y. M., Modality Conversion in Content Adaptation for Universal Multimedia Access, white paper.

[9]

Smith J. R., Interoperable Content-based Access of Multimedia in Digital Libraries, white paper.

[10]

Pereira F., Burnett I., Universal multimedia experiences for tomorrow, IEEE Signal Processing Magazine, 2003.

[11]

Raghuveer A., Kang N. O., Du D. H. C., Techniques for Efficient Streaming of Layered Video in Heterogeneous Client Environments, white paper.

[12]

Magalhães J. M., Universal Access to Multimedia Content Based on the MPEG-7 Standard, Master of Science Thesis, Universidade Técnica de Llisboa, 2002.

[13]

Önür Ö. D., Optimal Video Adaptation for Resource Constrained Mobile Devices Based on Utility Theory, Master of Science Thesis, Middle East Technical University, 2003.

[14]

Hellwagner H., Timmerer C., MPEG Standards Enabling Universal Multimedia Access, tutorial description, Klagenfurt University, 2006.

[15]

Dalei W., Song C., Haohong W., Cross-Layer Optimization for Video Summary Transmission over Wireless Networks, IEEE Journal on Selected Areas in Communications, Vol. 25, No. 4, 2007.

Acknowledgments The present document has been produced with the financial assistance of the European Social Fund under Operational Programme “Human Resources Development”. The contents of this document are the sole responsibility of “Angel Kanchev” University of Ruse and can under no circumstances be regarded as reflecting the position of the European Union or the Ministry of Education and Science of Republic of Bulgaria. Project № BG051PO001-3.3.06-0008 “Supporting Academic Development of Scientific Personnel in Engineering and Information Science and Technologies”.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Physical and Mechanical Property Evaluation of Some Clay Deposits in Mubi for Production of Glazed Roofing Tiles S.A. AKANJI*, C. NATHAN*, J. WADAI** *Department Of Mechanical Engineering, The Federal Polytechnic Mubi, P. M.B. 35, Mubi, Adamawa State, Nigeria. **Technical Skills Acquisition Center Mubi, Adamawa State, Nigeria. _______________________________________________________________________________________ Abstract: Five different clay samples were collected from different locations within Mubi Local Government Area of Adamawa State, Nigeria were mixed with 5% to 35% silica sand, which were later subjected to a compressive force to bind the mixture to produce ceramic tiles. The tiles were dried and heat treated using an electric furnace at a temperature of 1100°C for four (4) hours to produce unglazed ceramic tiles. The produced tiles are later glazed using water glass and further heated to a temperature of 1200°C for another four (4) hours to produce glazed ceramic tiles. The tiles produced were subjected to stress-strain analysis to determine their mechanical properties (compressive strength, and breaking load) and physical properties (water absorption, % shrinkage and % warpage). The results of the analysis for samples (A, B, C, and D) gave compressive strength (59.06%, 75.20%, 69.80%, and 52%) breaking load (420KN, 400KN, 410KN, and 320KN) % water absorption (8.10%, 12.10%, 10.20%, and 14.20%), % total shrinkage (6.27%,4.64%, 4.21%, and 3.51%), % warpage (0.48%, 0.21%, 0.43% and 0.02%) respectively. The results of the analysis produced were found to be within the allowable limit recommended by the American Society of Testing and Material (ASTM). ________________________________________________________________________________________ I. Introduction Tiles are thin slabs used for roofing, flooring, paving and making drains. They may be made of clays burnt in kilns (ceramic tiles) or concrete (cement tiles). Tiles can be classified according to usage into three kinds namely; roofing tiles, flooring or paving tiles (Singh, 1979). The manufacture of ceramic tiles involves careful preparation of clay (Peter, 1984), compressing moulding or extrusion process to be followed by drying and heating and finally the glazing operation (Adeyemi, 1987). The allowable recommended ceramic tile properties such as least compressive strength, maximum percentage water absorption water absorption and maximum percentage total shrinkage, etc are examined for floor, roof and wall ceramic tiles (Gillot, 1968). A mixture of clay and water has unique plastic properties which can be shaped and fired in the kiln (Samuel, 1995). At the sintering temperatures, the alkali flux gives rise to molten glass which partly dissolves the other oxide constituents and binds them together on cooling. Ceramic materials are relatively cheap and easily located across the country (Adeyemi, 1989). Nigeria fortunately is blessed abundantly with materials necessary for the production of tiles. As a nation that is determined to grow industrially, we need to encourage the dependence on our locally produced tiles. The aim of this work was to produce glazed ceramic tiles using clays from Mubi and investigate the effects of additives on the quality and mechanical properties of some selected clay soils. Also to examine the effects of varying compositions of clay/silica on the compressive strength, breaking load, % shrinkage, % water absorption and % warpage, on the tiles produced and compare them with the properties recommended by the American Society of Testing and Materials (ASTM). II. Materials and Method A. Sample Collection Base clay A, which is the Digil clay, was collected from Digil area of Mubi North. Base clay B, which is Vimtim clay, is collected from VimtIm area of Mubi North. Base clay C, which is Lokuwa clay is collected from Mubi I Primary School area of Lokuwa in Mubi North. Base clay E which is the works clay is collected from the works area in Mubi North. All the soil samples were collected from Mubi North Local Government Area of Adamawa State. The silica sand was collected from the River Yedzaram, Mubi, Adamawa State. B. Sample Preparation The clay samples A, B, C, D and E used for the production of tiles were collected as lumps and crushed into small sizes, dried and grounded into fine clay particle. The clays were sieved through a 500mm mesh and the silica sand was sieved through a 250mm mesh. In the production of tiles of varying silica contents for a typical base clay A, as the percentage silica additions were increased, the same corresponding percentage of base clay A were reduced by weight. For example, to produce tiles with sample A, the percentage composition mixes of silica, the silica was varied over range of 5% to 35% increment with corresponding decrease in the base clay A contents (Figure 1, x-axis of graph A). The percentage composition mixes by weight of the various clays investigated (Table 1) were thoroughly mixed in a dry condition before adding 15% to 20% water by weight to

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S.A. Akanji et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 08-14

the mixes. The quantity of water added is dependent on the types of clay compositions. The clay mixtures were then kneaded for about ten minutes until the mixed clays become plastic and workable. The clay mixtures were rolled into baIl shapes and stored in a warm dark place for between 4 to 7 days. The physical properties of the five clay samples and the silica sand were determined and presented in Table 1. C. Tiles Production The tempered clay samples were then moulded by pressing to produce tiles of 210mm square dimension to produce good tiles, drying process (Adeyemi, 1989), should be carefully undertaken to reduce excessive warping or even crack of the tiles. To achieve this, the freshly moulded tiles were covered with polythene for one day so as to allow initial uniform moisture drying followed by stiffening process, involving removal of the polythene for three days and finally open air drying for at least seven days. The dried tiles, arranged in piles, were fired to various temperatures soaking for specified hours and heating rates as given in Table 3. Using an electric heating furnace of maximum range of 1250°C. To produce biscuit ceramic tiles (unglazed). The unglazed ceramic tiles were arranged singly to avoid gluing during melting state and fired using the kiln furnace to a temperature of 110°C to obtain unglazed tiles. The tiles produced were later glazed and reheated to a temperature of 1200°C to produce glazed ceramic roof tiles. III. Determination of Some Physical Properties of the Clay The experimental procedure was conducted in accordance with the American Society of Testing and Materials (ASTM) standards (Priemon, 1985). The physical properties tested include the following: percentage water absorption, percentage shrinkage, percentage warpage, and grain fineness ratio. Table 1: Composition and Locations of Clays Investigated (% by weight) Constant Addictive of 15%, 20%, 25%, 30% and 35% silica sand. Base Clays Clay A Clay B Clay C Clay D Clay E

Locations in Mubi North L.G.A. Digil Vimtim Lokuwa Lamorde Works

Varying % Main Composition Silica + Clay A (55) Silica + Clay B (40) Silica + Clay (50) Silica + Clay D (55) Silica + Clay (70)

Constant % Additives Composition 20% clay B 22.5 clay C, 12.5% flux 20% clay A, 15% clay D 10% clay B, 15% clay A 10% clay C

Constant % addictives compositions are based on the clays composition mixtures for the manufacture of hard porcelain products (Grimehaw, 1971). Table 2: Step Firing Stages used to Produce the Biscuits Tiles S/N

Firing Stages

1. 2 3. 4. 5.

Water smoking Dehydration Oxidation Verification Cooling

0 - 200 550 880 1100 - 12.1200 To room temperature

Soaking Period (Hours)

Heating rate (°c/min)

Temperature (oC) 0.95 2.65 4.19 5.24 1.11

4 4 4 4 16

A. Percentage Water Absorption This parameter is measured to determine the durability of the products when exposed to environmental conditions. Each tile was initially weighed on a weighing scale and later submerged in a clean water at room temperature for twenty four hours (24 hrs). After this period, the tile specimen were removed, wiped off with a clean dry cotton rag and reweighed. The percentage water absorption, on dry basis was calculated using.

Where Ms = mass of saturated tiles specimen (kg) after cold water submersion, Mf = fired mass of tile specimen (kg). The recorded percentage water absorption was based on the average of percentage water absorption obtained for five tiles of the same composition. B. Percentage Shrinkage The percentage shrinkage properties were determine by measuring the original length of the given tile along the centre and re-measuring the lengths after drying and firing of the tiles using a vernier calliper. The percentage shrinkage were determined using the following relationships. The percentage total shrinkage was determined from percentage dry shrinkage and percentage fired shrinkage.

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Where: L0 = original length (mm) of the green tile L1 = fired length (mm) of the dried tile L2 = fired length (mm) of the fired tile C. Percentage Warpage Warping is the measurement of curvature (convex or concave) of deviation of the tiles surface from a true plane along the edges or the diagonals. The deviations were measured at the mid-length of an edge or a diagonal and expressed as percentage of the length of the edge or diagonal. The method was used by initially setting a dial indicator to zero at the mid-centre of the tiles and later moving the dial indicator lines on the tiles. The percentage warpage were calculated from

Where: W1 (mm) Amount of warpage D1 (mm) = the gauge length D. Grain Fineness Ratio The clay samples collected as lumps and crushed into small sizes, dried and grounded into fine clay particle and weighed on a weighing balance. The clay was then sieved through a 500Âľm mesh to obtain the fine grain particle size. The grain fineness ratio was found to be the ratio of the sieved fine clay particle to the mass of the grounded clay particles before sieving.

Where GFR = grain fineness ratio, MS is the mass of sieved clay particles in M and Mc is the mass of the grounded clay before sieving in M. The recorded percentage warpage was based on average percentage warpage of at least five tiles of the same composition. IV. Mechanical Property Tests The mechanical properties, compressive strength, breaking load of the tiles produced were tested as follows. i. Compressive Strength The tile specimen was placed on a hardened steel plate, load was gradually applied by a manually operated hydraulic press through an indicator or load applicator until there was a sign of crack. The compressive strength was calculated from

Where w = maximum load attained (Mpa) and A = area of load applicator (m2). The recorded compressive strength was an average value of at least five test specimens of a given tiles composition mixes. ii. Breaking Load The test method consists of supporting the tile specimens on the ends of the three cylindrical rods, arranged in an equilateral triangle form and applying a load until the tile specimen failed. The tiles strength was the load necessary to cause such tile failure. The tiles strength recorded was based on average values of at least five tiles of the same composition mixes. V. Results and Discussion Physical Properties Table 3 shows the physical properties for the various clay samples and silica sand used for the production of ceramic tiles. Shrinkage of the Glazed Ceramic Tiles Figure 1 shows the effect of increase in percentage of silica sand on the percentage shrinkage of glazed ceramic tiles.

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It was observed that products made from clay sample A have a slight decrease in the percentage shrinkage from 6.25, 5.9, 5.0, 4.9, and 4.1 with increase of the silica sand of 15%, 20%, 25%, 30%, and 35% respectively. While for tiles produced with clay sample B, the percentage shrinkage shows an initial increase in the value of 4.6, 4.7, 4.9 at 5%, 10% and 15% silica sand addition, then maintains a constant value of 4.9 with increase of silica sand from 15%, 20% and 25%. Sample C tiles indicate slight increase in percentage shrinkage of 4.2, 4.4, 4.7, 4.8, 4.9 with increase of silica sand addition from 5% to 35%. Clay sample D showed initial constant shrinkage value at 3.6% at 5%, 10%, and 15% silica sand addition before decreasing in value to 3.3% and 3.2% at 20% and 25% silica sand addition. Figure 1 shows that clay tiles A has the highest shrinkage value of 6.27%, while clay sample D indicate the lowest shrinkage value over the percentage silica sand addition. The shrinkage value of all the tiles produced were found to be within the shrinkage value recommended by the American Society of Testing and Materials(ASTM) values (15% max). Water Absorption of the Glazed Ceramic Tiles Figure 2 shows the percentage water absorption with percentage silica sand addition for all the samples investigated. Clay A indicates slight increase in percentage water absorption of 9.1%, 9.2%, 9.3%, 9.4% and 10% at 15%, 20%, 25%, 30% and 35% silica sand addition and gave the least percentage water absorption of 9.1% at 15% silica sand addition. Clay sample B shows decrease in water absorption with percentage silica sand addition of 12%, 11.8%, 11.7%, 11.6%, 11.5% at 5%, 10%, 15%, 20% and 25% silica sand addition respectively. Clay C tiles shows initial constant water absorption of 10.20%, at 5%, 10% and 15% silica sand addition then decrease to 10.10, and 10.50 at 20% and 25% silica sand addition respectively. Clay D tiles shows decrease in percentage water absorption from 14.20%, 13.9%, 13.2%,13%, 12.9%, at 5%, 10%, 15%, 20% and 25% respectively. Clay sample D gave the highest percentage water absorption of 14.20% at 5% silica sand addition, followed by clay sample B, 12.10% at 5% silica sand addition and least percentage water absorption by clay sample A, 9.1% at 5% silica sand addition. Clay sample A, with least percentage water absorption will withstand exposure to water than tiles D with highest percentage water absorption. The values of the water absorption of all the tiles were found to be within the allowable limit recommended by ASTM which is 16.0% max. Percentage Warpage of the Glazed Ceramic Tiles From Table 3, the percentage warpage for samples A, B, C, and D are 0.48, 0.21, 0.43 and 0.42 respectively. The percentage warpage obtained for all the samples investigated were found to be within the percentage warpage range (Gillot, 1970) of 2.1% as specified by ASTM standard. The variation in percentage warpage obtained may be due to the drying/firing methods applied. VI. Mechanical Properties Compressive Strength of the Glazed Ceramic Tiles Figure 3 shows compressive strength values with increase of silica percentage sand addition for ceramic tiles produced. Sample A tiles shows an initial constant of compressive strength of 59.06Mpa at 15%, 20%, and 25% silica sand addition them decrease to 48Mpa and 44Mpa at 30% and 35% respectively. Sample B tiles shows an increase of compressive strength of 40Mpa, 45Mpa, 59Mpa, 65Mpa and 75Mpa at 5%, 10%, 15%, 20% and 25% sand addition respectively. Clay sample C tiles shows a gradual decrease in compressive strength as the percentage of silica sand increases from 70Mpa, 65Mpoa, 49Mpa, 4lMpa and 36Mpa at 5%, 10%, 15%, 20% and 25% respectively. Clay D tiles shows increase in compressive strength of 3OMpa, 35Mpa, 42Mpa, 45Mpa and 52Mpa 5%, 10%, 15%, 20% and 25% silica sand addition respectively. Clay sample B gave the highest compressive strength of 75.2OMpa followed by clay sample C 69.8OMpa and least with clay sample D. All the clay samples have compressive strength value within the allowable value (48Mpa), recommended by the American Society of Testing and Materials. Breaking Load (KN) for the Glazed Ceramic Tiles Figure 4 shows a plot of breaking load with percentage silica sand addition. This figure indicates that there is increase in breaking load, as the percentage silica sand addition increase for all the tiles produced from all the four clay samples investigated. Clay sample A shows an increase in breaking load of value 280KN, 300KN, 320KN, 350KN, and 420KN at 15%, 20%, 25%, 30% and 35% respectively, with an increase of percentage silica sand addition. Clay sample B shows an initial constant value of breaking load 280KN, at 5% and 10% silica sand addition then increase to 320KN, 340KN, 400KN at 15%, 20%, 25% silica sand addition.

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Clay sample C shows an increase of breaking load of 260KN, 320KN, 340KN, 370KN, 410KN, at 5%, 10%, 15%, 20% and 25% silica sand addition respectively. Clay sample D shows an increase of breaking load from 240KN, 250KN, 260KN, 300KN, 320KN at 5%, 10%, 15%, 20% and 25% silica sand addition respectively. Clay sample A gave the highest breaking load of 420KN followed closely by tiles produced from clay sample C and B, 41OKN and 400KN respectively. Clay sample D gave the lowest breaking load of 320KN over the percentage silica sand addition. The breaking load for all the tiles produced were found to be within the allowable limit recommended by ASTM (260KN minimum). Optimum Tile Properties The best percentage composition mixed clay samples investigated to produce the tiles (glazed) with the optimum properties were determined based on the following properties maximum values of compressive strength, and breaking loads, minimum value of percentage total shrinkage and percentage warpage of the tiles produced. Table 4 showed the best percentage composition mixture for optimum properties of glazed ceramic tiles produced using the clay samples investigated were found to give values recommended by the American Society of Testing and Materials (ASTM). Table 3: Physical Properties of Clays Physical Properties

S/N

Clay A

Colour 1. 2. 3. 4.

Specific gravity Clay (%) Initial moisture content (%) Grain fineness ratio

content

Clay B

Reddish Brown 2.31

Creamy Grey 2.03

77.50

Clay C

Clay D

Clay E

Silica

White

2.00

2.01

Creamy White 2.62

77.50

41.25

40.10

46.10

3.00

2.56

1.28

1.29

19.70

18.50

6.67

86.50

69.86

83.83

89.73

64.92

65.4

Creamy 2.73

Table 4: Percentage composition for optimum properties of unglazed tiles Base Clay A B

D E

Composition of Mixture 55%, 20%, 25% Clay A, Clay B Silica 40% 22.5% 12.5% 25% Clay B Clay A Flux Silica 50% 20% 15% 15% Clay C Clay A Clay D Silica 55% 10% 15% 20% Clay D Clay B Clay A Silica 70% 20% 10% Clay E Clay D Silica

Compressive Strength (Mpa)

Breaking Load (1(N)

% Water Absorption

% Total Shrinkage

% War page

51.39

380.00

10.23

8.23

0.56

69.07

330.00

12.00

5.61

0.36

56.50

357.00

11.14

6.20

0.63

41.0

234.64

15.41

5.20

0.63

It cracked all over, it was therefore ruled out of the experiment

Table 5: Percentage (%) composition for optimum properties of glazed tiles Base Clay

Compressive Strength (mpa)

Composition of Mixture

55% 20% Clay A Clay B Silica

25%

40% 22.5% 12.5% Clay B Clay C Flux Silica

25%

50% 20% 15% Clay C Clay A Clay D Silica

15%

55% 10% 15% Clay D Clay B Clay A Silica

20%

ASTM allowable values

Breaking Load (KN)

% Water Absorption

% Total Shrinkage

% Warpage

59.06

420.00

9.10

6.27

0.48

75.20

400.00

12.10

4.64

0.21

69.80

410.00

10.20

4.21

0.43

52.00

320.00

14.20

3.0

0.42

48Mpa (Min)

260KN (Min)

16.0 (Max)

15% (Max)

2.1% (Max)

Page 12

S.A. Akanji et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 08-14

Fig. 1: Effect of percentage increase

Fig. 2: Percentage water absorption

of silica sand on the percentage

with percentage silica sand addition

total shrinkage of glaze tiles

for glazed tiles

Fig. 3: Compressive strength versus percentage silica sand addition for glaze tiles

Fig. 4: Breaking load versus percentage silica sand addition for glazed tiles

VII. Conclusion Increase of percentage silica sand addition within the percentage range investigated had the following effects on tiles properties. i. Decrease in percentage water absorption of all the tiles produced with percentage silica sand addition, except for slight increase of percentage absorption produced from clay sample A.

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S.A. Akanji et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 08-14

ii.

The breaking load of tiles produced increase for all four clay samples investigated, showing a positive effect of increase in silica sand addition. iii. The compressive strength increase (a positive effect) for tiles produced from base clays B and D, showing the effects of silica addition to these base clay samples. The percentage shrinkage decrease (a positive effect) for tiles produced from base clay A and D plus a slight increase (a negative effect) on tiles produced with clay samples B and C. Ceramic roof tiles produced by using best composition mixture for all the four clay samples were found to give properties (physical and mechanical) that are within the allowable limit recommended by the American Society of testing and Materials (see Table 5) References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Adeyemi, M.B. (1987). Experimental Determination of Extrusion Pressures and Properties of Lateritic Bricks made using a continuous Horizontal Extrusion Process. International Journal of Development Technology 5:39-47. Adeyemi, M.B. (1989). Effects of Extrusion Die Geometry on the Extrusion Pressure and Mechanical Properties of Clay Bricks made by Horizontal Extrusion Process. Nigerian Journal for Technology Development 1:122-127. Gillot, E.J. (1968). Clay in Engineering Geology. 1 Edition, Elsevier London. 40-48. Grimehaw, R.W. (1971). “The Chemistry of Clays” 1 Edition Ernest Benn Publishers, London. 36-47. Harry, O.B. (1969). “Nature and Properties of Soils”. 1 Edition Macmillan Publication Company New York. 16-22. Nathan, C. (2009). “Processing and Property Evaluation of Mubi Clay towards Unglazed Tiles Production” M. Eng Research Project Report Submitted to the Department of Production Engineering, University of Bemn, Bernn City Nigeria. Nathan, C. and Ibrahim, Y.T. (2001). Evaluation of some Clay Soils in Mubi for the Production of Unglazed Roofing Tiles. Nigerian Journal of Engineering Research and Development Vol.10, No.1. Peter, S.M. (1984). “Understanding Clay Recognition and Processing” Volunteer in Priemon, A.R. (Ed) (1985). “Construction” ASTM 4:05. Ryan, W. (1968). Properties of ceramic Raw Materials. 1 Edition. Devis Publishing House Ltd. New Hilvik. 51-53. Samuel, J.O. (1995). “Manufacturing of Ceramic Tiles using Firing Techniques” M.Eng. Research Project Submitted to the Department of Mechanical Engineering, University of form, Ilorin - Nigeria. Singh, S. (1979). Engineering Materials. l Edition. Vikas Publishing House Ltd.Delhi. 23-36. Stafford, C.E. (1980). Modern Industrial Ceramic. First Edition, Bubbs-Merill Company Publishers London. 16-3 8.

Page 14

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net PRICE LIMITS AND INFORMATIONAL EFFICIENCY Tamir Levy * and Joseph Yagil ** *Netanya Academic College School of Business 1 University St. Kiryat Yitzhak Rabin Netanya 42365, ISRAEL **Haifa University Faculty of Management Haifa, 31905, ISRAEL Abstract: Many of the empirical studies involving the estimation of the return-generating process (RGP) under conditions of a price-limit regime base their arguments on the equality of the aggregate market return during a k-day limit sequence and the aggregate theoretical return during those periods had the exchange not adopted a price-limit regime. Using an extension of Vives' (1995) model, we investigate whether this equality, which we call the Return Identity Proposition (RIP), actually holds. We demonstrate that the RIP may not hold when noise traders act in the markets. Simulation tests indicate that stocks for which noise trading is relatively high exhibit a higher number of limit hits, a larger number of limit days and a higher percentage of price reversals on the limit removal day. These results imply that while the RIP may be a practical tool for empirical studies of price limits, it may not hold when noise trading is present. Keywords: Keywords: Price limits; Return-generating process; Noise traders. I. Introduction Daily price limits are adopted by many securities exchanges in countries such as the USA, Canada, Japan and numerous countries in Europe and Asia, in order to increase the stability of the market. These limits confine the price of the financial asset during all trading stages of any trading day to a range, usually determined based on the previous day’s closing price. By so doing, exchanges artificially change the return-generating process (RGP) of the asset. Such changes pose a problem involving the estimation of the RGP without knowing the theoretical prices. Several suggestions have been made in the literature to solve the price-estimation problem. Roll (1984) discusses the relationship between the value and the price of frozen orange juice futures. He assumes the limits do not influence the RGP. Therefore, he proposes treating the aggregate rate of return during the limit period as if it were equal to the theoretical (equilibrium) aggregate rate of return.1 We term this proposition the Return Identity Proposition (RIP). The RIP was adopted (directly or indirectly) in subsequent studies, such as those by Sutrick (1993), Yang and Brorsen (1995), Kim and Rhee (1997), Park (2000), Kim (2001) and Wei (2002).2 Still, the basis of the proposition, beyond Roll's arguments, is lacking. Most researchers have not defined it, nor have they assigned it a specific name or explored its basis. As far as we know, the literature contains no theoretical discussion of the RIP. While prior empirical studies employed the RIP, theoretical studies have not investigated it. Thus, an attempt is made here to fill the gap by examining the conditions necessary for the existence of the RIP. The purpose of this paper, therefore, is to test the RIP, with the specific goal of investigating whether the market RGP equals the equilibrium RGP, or, equivalently, whether the RIP holds. The importance of the investigation lies in finding a theoretical basis for empirical studies of the price-limit phenomenon. The issue of the existence of the RIP may also help answer the question about the effect of price limits on the statistical properties of market returns. Theoretical studies of price limits include those by Brennan (1986), Subrahmanyam (1994), Kodres and O’Brien (1994), Chowdhry and Nanda (1998), Chou, Lin and Yu (2000, 2003), Harel, Harpaz and Yagil (2005 and 2010) and Levy and Yagil (2005).3 Empirical studies of price limits have investigated several issues and reached a number of significant findings. First, following an up-limit hit, the price usually continues to rise on the subsequent day (Park, 2000). In addition, stocks that have frequent limit hits have strong returns, high trading volumes, and receive more news coverage (Seasholes and Wu, 2007). While trading activity increases after either trading halts or price limits have been activated, volatility stays the same after trading halts but increases after price-limit hits are reached (Yong et al., 2008). Finally, Haun and Chou (2004) show that intraday price limits do not seem to have a strong influence on return dynamics. Another question dealt with in the literature is whether price limits have a cooling off effect that stabilizes prices once they approach a limit, or a magnetic effect that accelerates prices toward the limits (Arak and Cook, 1997;

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Tamir Levy et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 15-26

Fernandes and Rocha, 2007). Subsequent research has concluded that a strong cooling off effect prevails (Abad and Pascual, 2007). The empirical literature on price limits has also tested a variety of models to determine the most effective ones. Brennan's original model (1986) was broadened to two periods (Chou, Lin and Yu, 2000), and was also extended to investigate whether the imposition of spot price limits can further reduce the default risk (Chou, Lin and Yu, 2003). Other models include a censored stochastic volatility model for capturing important features of a return series censored by price limits (Hsieh and Yang, 2009), and a Censored-GARCH model with price limits used to estimate the return-generating process (Wei, 2002). Harel, Harpaz and Yagil (2005) developed a Bayesian forecasting model in the presence of return limits and provide some numerical predictions. An additional forecasting model is offered in Harel, Harpaz and Yagil (2010) who also applied it to a sample of futures contracts. Finally, research indicates that the near-limit model performs better than five other models proposed in the literature in terms of its ability to predict returns (Levy and Yagil, 2006). Related works include the literature on circuit breakers and trading halts. Despite the broad range of both theoretical and empirical studies of price limits, none of them has examined the RIP either theoretically or empirically. This study attempts to fill this gap. By extending Vives` (1995) model to the issue of price limits, we examine potential sources for the price distortion and find that when noise traders act in the market, the RIP may not hold. We demonstrate explicitly how the limits distort the return-generating process. These results imply that while the RIP is a practical tool for empirical studies of price limits, it may not hold when noise trading is present. In addition, our findings indicate that both the probability of price limits and the length of the limit sequence depend on the level of trading information. The organization of this study is as follows. Section 2 describes the basic model; Section 3 incorporates the pricelimit features into the basic model; Section 4 introduces numerical simulations and discusses the results; Finally, Section 5 provides a brief summary and concluding remarks. II. The Model This section describes the model we use to investigate the conditions under which the RIP exists. Several features of our price-limit model are borrowed from Vives’ (1995) model, which is not a price-limit model4. We will therefore begin our discussion with a general model for a no-price-limit market, and proceed later in Section 3 with a pricelimit model. A. General Model For a No-Price-Limit Market A.1 The Assets We consider an N+1-period exchange economy with long-term agents. Trading occurs over the first N periods. Following Vives, we assume there are two types of assets in the market, a safe asset and a risky asset. The safe asset pays an unitary return. The risky asset liquidates at the end of Period N+1, and has a random fundamental value V, where V is normally distributed with a mean

V and a variance of σV2 . Figure 1 describes the time line of the

model. Figure 1: The Time Line in Vives' (1995) Model Time 1

N+1

Periods

Risky Asset Liquidates A.2 The Traders We assume there are three types of traders in the market: informed traders, noise traders and competitive risk-neutral market makers.  Informed Traders There is a continuum of informed agents, indexed in the interval [0,1]. The informed traders are long-term informed agents, who maximize the expected utility of final wealth. Trader i has an exponential utility function, U, with a Constant Absolute Risk Aversion (CARA) coefficient, :

U WiN   e  WiN

for

>0.

(1)

The trader maximizes her utility from her wealth in Period N. Each Period t, Trader i is endowed with a small piece of information (a private signal) about the liquidation value of the risky asset, V. The private signal (Sit) equals:

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Tamir Levy et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 15-26

S it  V   it where

 it

(2)

is the error term. It is normally distributed with a zero mean and a variance of

are uncorrelated, and errors are also uncorrelated across agents and periods. with a mean

 2 . The terms V and  it

S it is therefore normally distributed

 

V and a variance of (  V2   2 ). The precision level of the signals   t    t

1

is the same across

agents in the same period but may be different across periods. Vives mentions that a trade occurs in Period t only if   t  0 . He also considers two informational conditions: the case of the concentrated arrival of information and the case of a constant flow of information. In the case of the concentrated arrival of information, the precision level of signals is positive in the first period only (that is,   t  0 for t =2,...,N). On the other hand, in the case of a constant flow of information the precision level of the signals is the same in all periods (   t In Period n, Trader i has a vector of private signals submits a demand schedule (a limit order)



  1 for all t).

Sin  Si1 ,..., Sin  available. Informed agent i in Period n



~ X in Sin , P n1 , indicating the position desired at every price P * n ,

contingent on the information available (the sufficient statistic for the private information and the sequence of past

P n1  P*1 ,..., P*n1 ). In Period t, informed agent i buys or sells according to whether her/his private * estimate of V ( S it ) is larger or smaller than the market estimate, P t . prices

Vives demonstrates that the demand function of the informed trader for the risky asset is:

Xt 

EV  P  | Sn , P Var V  P  | Sn , P

(3)

As in other information models, the demand increases with the difference between the value and the price of the risky asset, and decreases with both the coefficient of the risk aversion and the variance of the difference between the value and the price of the risky asset. Vives compares two trading models (“short-term” and “long-term”) in a dynamic model with asymmetric information. In the short-term model, informed traders have a short horizon and maximize the (expected) utility of the short-term return. In the long-term model informed speculators have long horizons and maximize the (expected) utility of consumption in the final period. In both models the quality of the information a speculator has at any point in time is the same.  Noise Traders Noise traders’ demand depends on the random variable, u n , which is normally distributed with a zero mean and a

 u2 . Expected trading volume increases with noise trading  u2 . In fact, as is usual in this type of

variance of u,

model, there is a trade because of the presence of noise traders and because informed agents have better information than risk-neutral market makers.  Market Makers Competitive risk-neutral market makers observe the noisy limit book schedule

B( P

n 1





~ )   X n 1 Sin1 , P * n 1 di  un 1  z n 1   P * n 1  , 1

and set the price efficiently: random variable

(4)

P*  E V | L , where  is a linear function of past and current prices, and the

z n 1 represents the net trading intensity of informed agents in period n.  can also be thought of as

the new information in the current price filtered from the net aggregate action of informed agents. A.3 Equilibrium Proposition 1 characterizes the unique linear equilibrium, with long-term informed agents maximizing the expectation of the utility of final wealth5: Proposition 1. At any linear equilibrium,

P* N 1  V , and for n = 1,...,N:

P * n  n Z n  1  n an P * n1

(5)

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Tamir Levy et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 15-26

with: n

n   u an   n , Z n  anV  u n ,  n   V   u  an ,  V   1V ,  U   1U , an   1   t 1

an   1  n , and 1  n an   0 where

an is the trading intensity of Period n informed agents.

Eq. (5) demonstrates two sources of price changes in the model: noise traders’ demand, ut, and informed traders’ precision level,   t . Recall that Vives considers two informational conditions: the case of the concentrated arrival of information and the case of a constant flow of information. In the case of the concentrated arrival of information, the precision level of signals is positive in the first period only (that is,   t  0 for t =2,...,N). On the other hand, in the case of a constant flow of information the precision level of signals is the same in all periods (   t If private information is received only in the first period,

  1 for all t).

an  0 , for n2, and there is no informed trading after

the first period. The equilibrium price-and-return expressions derived above allow us to investigate how a price-limit regime affects the equilibrium prices. Therefore, we will explore how each of the two sources of price changes in the model--noise traders’ demand ( u t ) and informed traders’ precision level ( τ ε )--affects the returns on the limit removal day t

(namely, the day the limit is removed, called here after the LRD) and on subsequent days after the limit has been removed. III. Price Limits in the General Model What type of price relationship should exist in a market regulated by daily price limits? A daily price-limit rule restricts all prices during each trading day to the previous day’s closing price plus (minus) an up (down) limit. More M

formally, let P t be Period t's market price rather than the equilibrium price ( P t ). The market price at any Period t is subject to up or down limits that equal L dollars, and is related to the equilibrium price as follows:

P M t 1  L if PM t 

P *t P

t 1

if  L if

P M t  P M t 1  L P M t 1  L  P*t  P M t 1  L P

P

t 1

(6)

L

where P t and P t are the market and equilibrium prices in Trading Period t, respectively, and P t 1 is the price in the last period. In a given Period t, if the equilibrium price of the risky asset is higher than the down limit

 L ) or lower than the up limit ( P M t 1  L ), the price limits will not be effective. On the other hand, if the M equilibrium price of the risky asset is lower than the down limit ( P t 1  L ) or higher than the up limit M ( P t 1  L ), trading stops, and a limited (market) price is determined. Trading restarts at a subsequent period in (P

t 1

which, once again, the market price is bounded by the down and up price limits. Theoretically, the price limit can exist only for J trading days. Actually, however, the risky asset's return is bounded by the price limits for k trading days, k ≤ J, and Day t+k+1 is the day the limit removal day. Given that the market and equilibrium prices are different, we distinguish between the expected theoretical *

(equilibrium) return ( R ) and the expected market return ( R ). When no price limits exist, the two returns are identical. However, when a limit mechanism is present, the two returns can be different. The Return Identity Proposition (RIP) asserts that the accumulated market return during these k+1 days equals the equilibrium return that would have been accumulated under no-limit conditions. The RIP is described by Proposition 2 below.

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Tamir Levy et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 15-26

Proposition 2. If the Return Identity Proposition (RIP) exists, the accumulated market return

 k 1 M    R t i  and the  i 0 

 k 1 *    R t i  in a k-day limit sequence become equal; i.e.,  i 0 

accumulated equilibrium return

P M t k 1  P*t k 1 and k 1

R

i 0 M i and

R

t i

R

t 1

 ...  R

t  k 1

R t R *

t 1

 ...  R

k 1

t  k 1

  R * t i ,

(7)

i 0

where R R i are the market and equilibrium returns in Period i, respectively. The results for a k-day limit sequence, discussed in the following section, are general, and apply to any sequence. 6 Figure 2 describes the time line of the model in the case of a limit regime. Figure 2: The Time Line under a Price-Limit Regime ...

...

n+k+1

Time Periods

N+1

Limit Move

K-Day Limit Risky Limit Removal Asset Sequence Day Liquidates A. The Price-Limit Sources In the following sections we will analyze the two cases of the information structure noted by Vives and cited in Section 2.1 above - the case of the concentrated arrival of information and the case of a constant flow of information. We will explore the causes for the limit move in each of the two cases. Eq. (5) identifies two sources of price changes in the model: noise traders’ demand, u t , and informed traders’ precision level,   t . Therefore, the limit move can stem from each of the two sources of price changes or from a combination of the two.  When information arrives in concentrated form, informed traders receive private information only in the first period (that is,   t  0 for t =2,...,N). As a result, the limits can stem from both sources of price changes noise traders’ demand, ut, and informed traders’ precision level,



 When there is a constant flow of information, informed traders receive private information during all periods, with an equal precision level of signals during every period (   t    1 for all t). As a result, the limits arise due only to the noise traders’ demand ( u t ), but not due to the informed traders’ precision level,



What effect do the two informational conditions have on the return-generating process? We will discuss that issue in the next sub-sections, using the assumption that informed traders receive positive signals in some Period t n

( Si

 0 ), which cause an up-limit move.

B. The Case of a Concentrated Arrival of Information The case of a concentrated arrival of information involves two sub-cases: (1) the case where the limits are due to both informed and noise trading; and (2) the case where the limits are due to informed trading only. We begin here with the first case. B.1 Price Limits Due to Both Informed and Noise Trading Suppose that in Period n the informed traders receive certain positive information (signal). In addition, the uninformed traders’ demand is positive. As a result, both types of traders buy the asset. In a no-limit condition the price increases to its equilibrium level in Period n. Since the precision level of the signals in Period n and the subsequent periods (Period n+1 and further) is equal to zero, the informed trader does not trade during the periods that follow Period n. Assuming the noise traders’ demand equals zero in any other period except Period n, there will be no trading following Period n. In the case of a limit regime, however, the situation is different. Suppose that both informed and noise trading (   n , un  0 ) in Period n caused a limit move for k periods, beginning in Period n. Suppose also that in the periods subsequent to Period n, informed and noise trading equal zero ( ut

   t  0 , t=n+1, n+2, …); i.e., the precision

level of the signals received by the informed traders is equal to zero. As a result, Period n's equilibrium price is higher than the market price, P n  P n  P n 1 , which in turn is greater than the equilibrium price in Period n-1, due to the up-limit move. Since the informed traders know what effect the information they received in Period n *

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Tamir Levy et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 15-26

should have caused, they take this information into consideration. As a result, in Period n+k+1 (the limit removal day), there will be a left over ( LOn k 1 ), equal to the difference between Period n+k's equilibrium price ( P and Period n+k's market price ( P

nk

)

). Since in the periods subsequent to Period n, informed and noise trading

n k

equals zero, Period n's equilibrium price ( P n ) and Period n+k's market price ( P given by:

LOn  k 1  P*n  P M n  k  n Zn'  1  n an P*n 1  P M n  k

n k

) are equal. The left over is .

(8)

In Period n+k+1, the left over causes a further increase in the market price of the risky asset, toward the equilibrium price. Note that we are dealing here with a case of an up-limit move. For the alternative case of a down-limit move, the left over in Eq. 7 will be negative. Since noise trading is uncorrelated across periods, the market price will be lower than the equilibrium price. The difference between the theoretical and the market prices remains,

P*nk 1  P*nk  P*n  P M nk 1  P M nk  P M n . This outcome is described by Result 1 below. Result 1:

If the limit move is due to both informed and noise trading as well as the concentrated arrival of information, there will be a gap between the equilibrium price and the market price during the limit removal period. Furthermore, the gap will remain constant over a period of time. In other words, the RIP will not hold in this case. B.2 Price Limits Due to Informed Trading Only In the previous case (in Section 3.2.1) the limits stem from two sources of price changes – noise traders' demand and informed trading. What happens, however, when only the informed trading triggers the limit move? Suppose the signals received by the informed traders are positive and that the precision level of the signals is positive in the first period only (that is,  1  0 and   t  0 for t =2,...,N). Assume further that the noise traders’ demand equals zero during all periods;

ut  0 for t =1,...,N. As a result, the price of the risky asset in Period n increases.

In the case of a no-limit regime, Period n+1's equilibrium price ( P to Period n's equilibrium price ( P same as in Period n,

n 1 )

does not change. In other words, it is equal

). The reason for such a pattern is that in Period n+1 the trading intensity is the

an  an 1 . Therefore, as Eq. (4) implies, no trading affects the equilibrium price.

On the other hand, in the case of a price-limit regime, the limit exists for k periods. Suppose the private information that the informed traders received in Period n caused a limit move in Period n. Given that the informed traders know what effect the concentrated arrival of information should have caused, there would be a left over ( LOn k 1 ), equal to the difference between Period n's equilibrium price ( P M

affect Period n+k+1's market price ( P

) and Period n+k's market price ( P

n k 1 ) and be given by Eq.

n k

), that will

(8) above. The effect of the left over

causes Period n+k+1's market price ( P n k 1 ) to increase to its Period n's equilibrium value ( P n ). This outcome is represented by Result 2 below. Result 2: If the limit move is due to informed trading and a concentrated arrival of information, the equilibrium price and the market price will be equal during the limit removal period, implying the existence of the RIP. C. The Case of a Constant Flow of Information In the preceding section (Section 3.2) we discussed the first type of informational situation – the case of a concentrated arrival of information. This section deals with the second kind of informational situation - the case of a constant flow of information. In such a case the informed traders receive positive signals during all periods, with an equal precision level of signals during every period (   t   1 for all t). We emphasize here that the flow of information is constant in every period, and thus its impact is identical. Assuming the information that the informed traders receive is positive, the risky asset's price will increase gradually from period to period. If in some Period n the noise traders' demand is positive, the price will increase further. The equilibrium price in Period n equals

P * n becomes: P*n  1  n1an1

P*n .

The derivative of Eq. (5) with respect to

P*n1

(9)

It then follows that there are two sub-cases regarding Period n+1's price. The first is the case where the term " 1  n 1an 1 " in Eq. (5) is positive, while in the second case, this term is negative. In the first case, when a limit regime exists, the price after the limit has been removed continues to increase. The subsequent increase stems from the fact that the noise traders' demand during the limit-move period causes a price increase that is lower than the magnitude of the increase that would have been attained, given the informed traders' information.

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In the second case where the term " 1  n 1an 1 " in Eq. (5) is negative, the price on the limit removal day decreases. The decrease stems from the fact that the noise traders' demand during the limit-move period causes the price to increase by more than it should have increased given the informed traders' information. We discuss here only the second case, where the term " 1  n 1an 1 " is negative, because the conclusion as to whether the RIP exists is the same in both cases. In the case of a price-limit regime, the limits restrict the risky asset's price to a certain limit, and therefore restrains the move. Since a constant flow of information has an equal effect on the price in every period, a limit move becomes possible only in the presence of a noise trader's demand. As a result, Period n's market price ( P

) becomes lower than the equilibrium price, and continues to increase

toward the equilibrium price ( P n ) during the k limit days. The sort of trading strategy employed on the limit removal day will depend on whether the traders are informed traders or noise traders. The former know what effect the constant flow of information should have caused. They therefore know that there is a left over ( LOn k 1 ), equal to the difference between Period n's equilibrium price *

( P n ) and Period n+k's market price ( P over is expressed by Eq. (8) above.

n k

) that will affect Period n+k+1's market price ( P

n k 1 ).

This left *

The left over causes Period n+k+1's market price ( P n k 1 ) to increase to its Period n's equilibrium value ( P n ). Since the price increases by more than what the informed traders expected, they will sell the risky asset. Unlike informed trading, noise trading is equal to a random variable u n , and is not correlated across periods, nor with the limit move. In other words, the noise traders do not change their demands when the limit is removed. To illustrate, differentiating Period n+1's price, given by Eq. (5), with respect to the uninformed traders’ demand ( u n ) produces zero ( P *n 1 un  0 ). Due to the constant flow of information, the informed trading is constant. As a result, the limit move can stem only from uninformed trading. Since the uninformed demand is a random variable, it is uncorrelated across periods. Consequently, when a limit regime is present, the market price will fall on the limit removal day, and the limit move will be described by Result 3 below: Result 3: If the limit move is due to noise trading, the limits will cause the RGP of the theoretical price and the RGP of the market price to deviate; consequently, the RIP will not hold. In the following sections, we will demonstrate Results 1, 2 and 3 described above employing numerical simulations that test the model. IV. Numerical Simulations The model’s main result derived above (and discussed in the next section) is that when there are informed traders who expect the limit move, the RIP will hold. We will first explain the empirical difficulties associated with testing the theoretical model, and then present numerical simulations. A. The Difficulties Associated With Testing the Model An empirical investigation of the RIP involves a comparison of the risky asset’s return under conditions of price limits with the return of the same asset under conditions of no limits. This comparison, however, is empirically problematic. Since, in any time period, securities exchanges adopt only one of the two regimes (limit or no-limit), it is impossible to obtain the two returns simultaneously. The theoretical model presented above raises a second estimation problem. The model is based on the private signal of informed traders. The measurement procedure for this variable is not simple. There is a major problem in estimating the private information each trader has. Most prior studies, including Vives' (1995), that investigated the information issue did not conduct an empirical test. Instead, they used numerical simulations to test the models presented in those studies. This approach is adopted here as well.7 B. Price Limits Due to Both Informed and Noise Trading in the Case of a Concentrated Arrival of Information When the effect of price limits is due to both informed and noise trading in the case of a concentrated arrival of information, Result 1 described above will follow implying that the RIP will not hold. To demonstrate the effect of price limits in this case, we have used the following simulation. To make Vives' no-limit model compatible with our limit model, most of the parameter values used in the simulation are identical to those of Vives (1995) and are as follows8:

  2.5,  2  1.05,  u2  0.05,  v2  1,

follows:  is the constant coefficient of absolute risk aversion; variance of the random variable

and V  1 . Recall that the notation is as  2 is the variance of the error term;  u2 is the

un on which the demand of noise traders’ depends and V is the random

fundamental value whose variance is

 V2 . For the price-limit case, we adopted a limit of 0.05 dollars (L=$0.05), and

for the Period n-1 equilibrium price we used the value of $0.509. We then assumed that a concentrated arrival of

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Tamir Levy et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 15-26

information had occurred in Period 1, with a trading intensity ( a n ) of 0.38, and a trading intensity of zero in Period 3. Furthermore, we assumed that ( an 1

 0 for n>2). Substituting these parameter values stated above in Eq. (5)

yields a 1-day limit sequence. In other words, only one period is bounded by a limit move. The results are essentially unchanged when the limit sequence exceeds one day; i.e., for an N-day limit sequence. In addition, we assumed that u2=1, and the noise trading of all other periods equals zero. As a result, the equilibrium prices of any period following Period 1 were equal to $0.93, whereas the market price was $0.55 in Period 2 and $0.57 in Period 3 and later. The simulation results are demonstrated in Figure 3, where the vertical axis represents the prices and the horizontal axis denotes the time period. Note that the main simulation results implied by Figure 3 remain unchanged even if we use other parameter values that are different than those that Vives (1995) employed here. As Result 1 implies, if the limit moves are due to both informed and noise trading and the concentrated arrival of information, the equilibrium and market prices will deviate. Furthermore, this deviation will remain over a period of time. Consequently, the RIP does not hold in this case. Figure 3: The Observed and Theoretical Prices in the Case of Price Limits due to Both Informed and Noise Trading and a Concentrated Arrival of Information

Notes: Figure 3 demonstrates the case of a limit move due to both informed and noise trading and a concentrated arrival of information. The vertical axis and the horizontal axis represent the price and the time period, respectively. LRD denotes the limit removal day. The results here are based on the numerical simulation in Section 4.2. They indicate that the equilibrium and market prices will deviate when the limit is removed and that the deviation will continue over time. Consequently, as implied by Result 1, the RIP does not hold in this case. C. Price Limits Due to Informed Trading Only and a Concentrated Arrival of Information In the case of a concentrated arrival of information, and price limits that are due to informed trading only, Result 2 will follow and the RIP will hold. To demonstrate the effect of price limits in this case, we have used a simulation similar to the one above. As in the preceding case (in Section 4.2), we used the following parameter values:

  2.5 ,  2  1.05 ,  u2  0.05 ,  v2  1 , V  1 ,

L = $0.05; and Period 1's equilibrium price equals 0.50

dollars. We then assumed that in Period 2 a concentrated arrival of information had occurred, with a 2 = 0.38 and an1  0 (n>2). The value of zero for the trading intensity (i.e., the “a” coefficient) was used for any other periods. As a result, Period 2's equilibrium price equals $0.57, whereas Period 2's market price equals $0.55. Period n+1's left over equals $0.02, causing Period 3's market price to be $0.57, which is identical to the equilibrium price of Period 3. The positive return on the limit removal day (LRD) stems from the informed traders' reaction. As demonstrated in Eq. (3), the value of the risky asset is greater than its price, E V  P  | Sn , P  0 . Therefore, the informed



traders will buy the asset ( X t



 0 ) on the LRD, and the price will continue to increase.

The simulation results are demonstrated in Figure 4, where the vertical axis represents the prices and the horizontal axis – the time period. As Result 2 implies, if the limit move is due to informed trading and the concentrated arrival of information, the equilibrium and market prices will be equal when the limit is removed, implying that the RIP will hold. Figure 4: The Observed and Theoretical Prices in the Case of Price Limits due to Informed Trading and a Concentrated Arrival of Information

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Notes: Figure 4 demonstrates the case of a limit move due to informed trading and a concentrated arrival of information. The vertical axis and the horizontal axis represent the price and the time period, respectively. LRD denotes the limit removal day. The results here are based on the numerical simulation in Section 4.3. The figure indicates that, as implied by Result 2, the equilibrium price will equal the market price on the LRD. Thus, the RIP holds. D. Price Limits Due to Noise Trading in the Case of a Constant Flow of Information When there is a constant flow of information and price limits that are due to noise trading, the RIP, as shown in Result 3 described above, will not hold. Here, too, we employed essentially the same parameter values used in the preceding two cases. We have assumed that a constant flow of information occurs, with an  an 1  0.38 (recall

a n 1 in the preceding two cases was 0). In addition, we assumed that un  1 , and the noise trading in all other periods equals zero. As a result, Period n's that the value of

equilibrium price equals $0.9242, whereas Period n's market price equals $0.65. In the subsequent periods, both prices continue to increase. As Result 3 predicts, the price limits reduce the overall price volatility in the entire sequence of N trading days, but increase the price volatility in the sequence that excludes the limit-move days. For the parameter values used in our simulation for the periods beginning in Period n, the variance of the equilibrium return is equal to 1.79%, whereas the variance of the market return is equal to 0.26%. In other words, the limits reduce the overall volatility. For the periods subsequent to the limit-move period, the variance of the equilibrium return is equal to 0.0008%, whereas the variance of the market return is equal to 0.0478%. In other words, the limits increase the price volatility in the periods subsequent to the limit-move day. The simulation results are demonstrated in Figure 5, where the vertical axis represents the price and the horizontal axis – the time period. As Result 3 predicts, if the limit move is due to noise trading and a constant flow of information, the equilibrium and market prices will not be equal when the limit is removed, and the RIP will not hold. Figure 5: The Observed and Theoretical Prices in the Case of Price Limits due to Noise Trading in the Case of a Constant Flow of Information

Notes: Figure 5 demonstrates the case of a limit move due to noise trading and a constant flow of information. The vertical axis and the horizontal axis represent the price and the time period, respectively. LRD denotes the limit removal day. The results here are based on the numerical simulation in Section 4.4. As implied by Result 3, the figure shows that when the limit is removed, the equilibrium price is not equal to the market price, implying that the RIP does not hold in this case.

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The negative return on the LRD stems from the informed traders' reaction. As demonstrated by Eq. (3), the value of the risky asset is lower than its price, E V  P  | Sn , P  0 . Therefore, the informed traders will sell the asset



( Xt



 0 ) on the LRD, and the price will drop.

E. Discussion Employing a simulation approach, we have explored above two information structures – the case of a concentrated arrival of information and the case of a constant flow of information – and two price-change sources – informed and uninformed trading. We have presented three results (summarized in Table 1) that follow from a positive signal that causes an up-limit move. We have shown that the basic condition under which the RIP exists is dependent upon the cause for the limit move. If, on one hand, the limit move stems from noise trading, the RIP will not exist, as outlined in Results 1 and 3 above. If, on the other hand, the limit move is due to informed trading, the RIP will exist, as stated by Result 2 above. These general results also apply to the opposite case (not discussed here) of a negative signal and a down-limit move. The essence of the results remains unchanged if a negative signal, which causes a down-limit move, is assumed. Table 1: The Three Main Results of the Study Limits Due to

Concentrated arrival of information

1 2

Informed traders’ Noise traders’ RIP precision level demand Does Not Exist √ √ √ N.A. Exists

Constant information

3 of

N.A.

Result

Information Structure

flow

√

Does Not Exist

Notes: Table 1 summarizes the existence of RIP in the circumstances described in Results 1, 2 and 3 discussed in Section 4.5. The sign "√" describes the causes for the limit. For example, the sign "√" under the column "informed traders’ precision level" means that the informed traders’ precision level causes the limit move when there is a concentrated arrival of information. N.A. (not applicable) denotes that there is no limit cause. For example, N.A. under the column "informed traders’ precision level" means that the informed traders’ precision level does not cause the limit move when there is a constant flow of information. Our numerical simulations have demonstrated that the characteristics of the limit sequence are different for limits that are due to noise trading in the case of a constant flow of information (Section 4.5) compared with the other two cases - the case of limits due to both informed and noise trading and a concentrated arrival of information (Section 4.3), and the case of limits due to informed trading and a concentrated arrival of information (Section 4.4). The length of the sequence and the probability of a price reversal increase in the case of price limits due to noise trading accompanied by a constant flow of information. In other words, the asset's price depends on the level of the information the traders have, leading to the following result: Result 4: As the level of noise trading increases, the length of the sequence and the number of reversals increase. V. Conclusions This study has investigated the impact of price limits on the equilibrium return of a risky asset. According to the Return Identity Proposition (RIP), there is an equality between the aggregate market returns during a k-day limit sequence and the aggregate theoretical returns during those periods. Therefore, price limits should not have an effect on prices. Several researchers adopt this approach and assume in their empirical works that the RIP holds. In light of the existence of only a few theoretical works on price limits, we have developed a model of asymmetric information that we use to examine the conditions under which the RIP may or may not exist. Based on Vives (1995), we have demonstrated three results that may occur. Result 1 indicates that if the limit move is due to both informed and noise trading as well as a concentrated arrival of information, there will be a gap between the equilibrium price and market price in the limit removal period. Furthermore, the gap will remain constant over a period of time, meaning that the RIP will not hold. According to Result 2, if the limit move is due to informed trading and a concentrated arrival of information, the equilibrium price and market price will be equal in the limit removal period, and the RIP will hold. According to Result 3, if the limit move is due to noise trading, the limits will cause the return generating process (RGP) of the theoretical price and the RGP of the market price to deviate, and the RIP will not hold in this case. To test these hypothesized results, we have conducted simulations based on Vives' (1995) parameter values. The results confirm our three results, even if different parameter values are employed.

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The findings indicate that stocks involving a higher level of noise trading also involve a higher frequency of the following characteristics: (1) limit days, (2) long limit sequences and (3) price reversals on the limit removal day (LRD). The findings also imply that the volatility of returns for both the LRD and the limit sequence is statistically higher for stocks involving a higher level of noise trading. For these stocks we also found that the return on the LRD is negative for an up-limit sequence involving a lower level of noise trading. These findings appear consistent with the simulation's results. One implication of the model developed here is that by adopting a price-limit regime, securities exchanges can cause a deviation of the return-generating process of the market return from the original process. Another implication of our results is that while the RIP can be a practical tool for use in empirical studies of price limits, it is also important to include cases where the RIP may not hold in these studies. Given the deviation of the RGP of the market return from the theoretical return, we suggest that future research attempt to determine which (price-limit) empirical studies are more appropriate—those attempting to estimate the market return or those attempting to estimate the theoretical return. Another potentially interesting future study might test the existence of the RIP when uninformed traders use a model of time variance (similar to a GARCH model) in formulating their expectations. Finally, we suggest that future research should focus on finding an empirical estimator for returns on the LRD, both in the presence and absence of the RIP. References Abad, D and Roberto, P., 2007, "On the Magnet Effect of Price Limits," European Financial Management, 13, 833-852. Admati, A. R., 1985, “A Noisy Rational Expectations Equilibrium For Multi-Asset Securities Markets,” Econometrica, 53, 629-657. Arak, M. and Cook, R.E, 1997, "Do Daily Price Limits Act as Magnets? The Case of Treasury Bond Futures," Journal of Financial Services Research, 12, 5-20. Blume, L., and Easley, D., 1992, “Evolution and Market Behavior,” Journal of Economic Theory, 58, 9-40. Brennan, J. M., 1986, “A Theory Of Price Limits In The Futures Markets,” Journal of Financial Economics, 16, 213-233. Brennan, J. M. and Cao, H. H., 1997, “International Portfolio Investment Flows,” Journal of Finance, 52, 1851-1880. Chance, M.D., 1994, “Futures Pricing And The Cost Of Carry Under Price Limits,” The Journal of Futures Markets, 14, 813-836. Chou, P.H., 1999, "Modeling Daily Price Limits," International Review of Financial Analysis, 8, 283-301. Chou, P.H., Lin, M.C. and Yu, M.T., 2000, “Price Limits, Margin Requirements, And Default Risk,” The Journal of Futures Markets, 20, 573602. Chou, P.H., Lin, M.C. and Yu, M.T., 2003, “The Effectiveness Of Coordinating Price Limits Across Futures And Spot Markets,” The Journal of Futures Markets, 23, 577-602. Chowdhry, B. and Nanda, V., 1998, “Leverage And Market Stability: The Role Of Margin Rules and Price Limits,” Journal of Business, 71, 179210. Fernandes, M. and Marco A. R., 2007, "Are Price Limits on Futures Markets that Cool? Evidence from the Brazilian Mercantile and Futures Exchange," Journal of Financial Econometrics, 5, 219-242. Grossman, S. and Stiglitz, J.E., 1980, “On The Impossibility Of Informationally Efficient Markets,” American Economic Review, 70, 393-408. Harel, A., Harpaz, G. and Yagil, J., 2005, “Forecasting Futures Returns In The Presence Of Price Limits,” The Journal Of Futures Markets, 25, 199-210. Harel, A., Harpaz, G. and Yagil, J., 2010, “A New Paradigm For Forecasting Security Returns in a Market Regulated by Price Limits", Review of Quantitative Finance and Accounting, 35, 113-121. Hsieh, P. H and Yang, J. J., 2009, "A Censored Stochastic Volatility Approach to the Estimation of Price Limit Moves," Journal of Empirical Finance, 16, 337–351. Huang, Y.S., Fu, T.W., and Ke M.C., 2001, "Daily Price Limits and Stock Price Behavior: Evidence from the Taiwan Stock Exchange," International Review of Economics and Finance, 10, 263-288. Kim, A. K. and Rhee , G.S., 1997, “Price Limit Performance: Evidence From the Tokyo Stock Exchange,” Journal of Finance, 52, 885-899. Kim, A. K., 2001, “Price Limits and Stock Market Volatility,” Economics Letters, 71, 131-136. Kim, Y.H., and Yang, J.J., 2008, "The Effect of Price Limits on Intraday Volatility and Information Asymmetry," Pacific-Basin Finance Journal, 16, 522-538. Kodres, L.E. and O’brien, P.D., 1994, “The Existence Of Pareto-Superior Price Limits,” American Economics Review, 84, 919-932. Kodres, L.E. and Pritsker, M., 2002, “A Rational Expectations Model Of Financial Contagion,” Journal of Finance, 57, 769-799. Lee, J.H. and Chou, R.K., 2004, "The Intraday Stock Return Characteristics Surrounding Price Limit Hits," Journal of Multinational Financial Management, 14, 485-501. Levy, T. and Yagil. J., 2005, “Observed Vs. Theoretical Prices Under Price Limit Regimes,” Journal Of Economics And Business, 57, 208-237. Levy, T. and Yagil. J., 2006, "An Empirical Comparison of Price-Limit Models," International Review of Finance, 6, 157-176. Morgan, I.G. and Trevor, R.G., 1999, “Limit Moves As Censored Observations Of Equilibrium Futures Price In GARCH Processes,” Journal Of Business And Economic Statistics, 17, 397-407. Naughton, T., 2004, "Are Price Limits Priced? Evidence from the Taiwan Stock Exchange," Journal of Emerging Market Finance, 3, 249-267. Park, H.W., 2000, “Examining Futures Price Changes And Volatility On Trading Day After A Limit-Lock Day,” The Journal of Futures Markets, 20, 445-466. Roll, R., 1984, “Orange Juice And Weather,” American Economic Review, 74, 861-880. Routledge, B.R., 1999, “Adaptive Learning In Financial Markets,” Review of Financial Studies, 12, 1165-1202. Seaholes, M. S. and Wu, G., 2007, "Predictable Behavior, Profits, and Attention," Journal of Empirical Finance, 14, 590-610. Subrahmanyam, A., 1994, "Circuit Breakers And Market Volatility: A Theoretical Perspective," The Journal Of Finance, 49, 237-254. Sutrick, K.H., 1993, “Reducing The Bias In Empirical Studies Due To Limit Moves,” The Journal of Futures Markets, 13, 527-543. Vives, X., 1995, “Short-Term Investment And The Informational Efficiency Of The Market,” Review of Financial Studies, 8, 125-160. Wang, J., 1993, “A Model Of Intertemporal Asset Prices Under Asymmetric Information,” Review of Economic Studies, 60, 249-282. Wang, J., 1994, “A Model Of Competitive Stock Trading Volume,” Journal of Political Economy, 102, 127-168. Yang, S. R. and Brorsen, B. W., 1995, “Price Limit As An Explanation Of Thin-Tailedness In Pork Bellies Futures Prices,” The Journal of Futures Markets, 15, 45-59. Yong H. K., Yagüe J. and Yang, J. J., 2008, "Relative Performance of Trading Halts and Price Limits: Evidence from the Spanish Stock Exchange," International Review of Economics and Finance, 17, 197–215. Wei, S.X., 2002, “A Censored-GARCH Model Of Asset Returns With Price Limits,” Journal of Empirical Finance, 9, 197-223.

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1. 2.

5. 6.

7. 8.

Endnotes The terms “theoretical” and “equilibrium” will be used here interchangeably. Sutrick (1993) investigates ways to reduce the estimation bias by estimating the equilibrium prices of assets traded under a price-limit regime. Yang and Brorsen (1995) support the hypothesis that price limits cause the thin-tailedness observed in the distributions of pork bellies futures. Kim and Rhee (1997) formulate and verify the delayed price discovery hypothesis using daily data from the Tokyo Stock Exchange (according to this hypothesis the limits slow down the price discovery process by preventing prices from effectively reaching their theoretical values). Park (2000) also documents the existence of the delayed price discovery hypothesis in futures markets by testing the effect of price limits on futures prices, using futures contracts traded on the Chicago Board of Trade (CBOT). Kim (2001) finds that price limits usually do not change volatility. Wei (2002) estimates the return-generating process using a censored-GARCH model. Brennan (1986) was the first to infer that price limits can serve as a partial substitute for margin requirements. Kodres and O’Brien (1994) claim that price limits partially insure implementation risk. Chowdhry and Nanda (1998) provide a result similar to that of Brennan (1986). Chou, Lin and Yu (2000) broaden Brennan’s (1986) model to two periods, and Chou, Lin and Yu (2003) extend Brennan’s (1986) model to investigate whether the imposition of spot price limits can further reduce the default risk. Harel, Harpaz and Yagil (2005 and 2010) develop forecasting models in the presence of return limits and provide some numerical predictions. Finally, Levy and Yagil (2005) discuss the S-shape relationship between observed and theoretical asset prices in markets governed by a price-limit mechanism. Grossman and Stiglitz's (1980) model, which analyzes information acquisition under conditions of asymmetric information, was extended by Vives (1995). Grossman and Stiglitz's model was first extended to a multiple-asset setting by Admati (1985) and has since been extended in a number of directions by others. Admati (1985) considers a continuum of investors who have diverse private information. Blume and Easley (1992) consider an infinitely repeated version of the model. Others have extended the model to a dynamic setting with either a single risky asset (Wang, 1993, 1994), or with multiple risky assets (Brennan and Cao, 1997). Routledge (1999) considers the adaptive learning of the traders. Kodres and Pritsker (2002) extend the model to explain financial market contagion. Proposition 1 corresponds to Vives’ Propositions 2.1 and 4.1. A sequence higher than a two-day limit is quite rare. For example, both Morgan and Trevor (1999) and Wei (2002) examined T-bills futures traded on the CME for the period from 1979 to 1982. Using the same sample, they observed 57 limit days, only 20 of which were two-day consecutive limit moves and none was beyond a two-day limit sequence. Numerical simulations have been used extensively in studies of asymmetric-information models (e.g. Grossman and Stiglitz, 1980) and in the price-limit literature (e.g. Chance, 1994). We also used an alternative set of parameter values and the main results remain unchanged. *

Changing Period n-1's equilibrium price ( P n 1 ) does not change the results. Though the effects of price limits have been tested empirically before (e.g., Hsieh and Yang, 2009) the RIP, as stated above, has not been investigated yet. Acknowledgments We would like to thank the seminar participants at the University of Haifa and Ben-Gurion University for their comments and suggestions on an earlier draft of the paper. Remaining errors are our responsibility. 9. 10.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Evaluation of Effect of Policy Making Models on Organizational innovation Indices Alireza Booshehri1 Ph.D. Technology Management, Assistant Professor, Malek Ashtar Industrial University, Tehran, IRAN. Iraj Masoomi Baran2 2 Ph.D.Candidate, Malek-e-Ashtar University, Tehran, IRAN. ___________________________________________________________________________________ Abstract: This study was performed with purpose of evaluating the effect of policy making models on organizational innovation indices of the Sanam public industrial company by way of which company directors can select the best policy making model for improving innovation indices. Statistical population in this study included all directors and supervisors of the Sanam industrial company, a number of 37 individuals. In this study, by designing a 50 itemed questionnaire, the validity and reliability of which was approved, level of influence of 4 policy making models (critical, incremental, rational and social) on organizational innovation indices (including human resources, creation of new knowledge, transfer and application of new knowledge, financial affairs and innovation outputs) was evaluated. Research hypotheses were tested using the single group student t and the Friedman coefficient. Research results showed that the logical and social policy making models had meaningful effect on all indices of innovation. Additionally, critical and incremental policy making models except for the index of knowledge creation were influential on other indices of innovation in a meaningful way. Also, the incremental, rational and social models have the most influence on the human resources index and the crisis model has the most influence on the financial affairs and innovation outputs indices. 1

Keywords: Policy, Policy Making Models, innovation, innovation Indices __________________________________________________________________________________________ I. Introduction Now a day, considering continuous changes and variety in needs of stakeholders, the topic of innovation has become a necessity in competitiveness of organizations to a limit that in some sources, innovation has been considered a criterion for company survival. Innovation has various kinds and among them organizational innovation has high importance. It leads to increased quality, decreased prices and faster presentation of products and services and needs to be blown into the body of the organization like a spirit and increase its survival. As a result, organizational innovation is one of the axial merits of a company in todayâ&#x20AC;&#x2122;s age. On another hand, just like some people are more successful and effective in the human world because of more creativity and innovation, in the organizational world as well, the situation is the same. Particularly in the present age and as time advances, considering changes, progress of sciences and technologies and creation of new needs and challenges, organizations have also become more complex and their management has become more difficult. Continuous learning, innovative use of processes, structures, resources, science and technology also play an effective role in company performance. This outlook leads to realization of innovation in all dimensions of the organization and gives the ability to behave more innovative compared to their competitive environment. In addition to the above considerations, other factors also affect organizational innovation. One of these factors is policy making with the purpose of solving problems facing company innovation. Organizations are among the most important detrimental systems in a country and are directly in connection with economy, creation of work, social welfare and helping national strength. Additionally organizations in regards with their innovative role creation in production, technological, economic and social areas have outlooks and goals. It is necessary that they be backed up by making decisions and implementing appropriate policies. As a result, in every organization decisions and policies made repeatedly for resolution of problems in the mentioned areas needs to be in this continuum. Also, every including innovation policy has effects on the collective it covers and if these influences are such that they lead to distance from outlooks and desired goals, it means inappropriateness or weakness in the relevant policy. Therefore, there is need for policies to be studied well before being made and the best policy for resolving each problem is acquired. Considering the above, it is

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appropriate that a comprehensive model for policy making gets selected so that undesirable effects are prevented. Considering the importance of the topic of policy making and attention needed to it in organizational innovative activities, the purpose of this study was determination of a policy making model appropriate for achieving innovative indices for better success of companies in the economic and social arena. Achieving this important goal requires getting to know policy making models and presenting the best model for the condition and structure of the organization. On this basis, in this study, it has been tried to evaluate the effect of each of the related models on organizational innovation indices in the Sanam public industrial company by studying policy making models and organizational innovation indices. II. Theory of Research  Policy Numerous definitions have been presented for policy including: policy is principles or rules for decision making and achieving logical consequences and a method for performing (Anderson, Chris, 2005). James Anderson, in another definition, introduces policy as the way of action for achieving a particular goal that is followed by the policy maker. The third definition on this issue is somewhat more conceptual and has been suggested by William Jenkins. According to him, policy is a collection of interrelated decisions with each other that are adopted by a policy maker or makers and refers to selection of goals and tools for reaching them under special conditions. It is of note that in the second definition, taking action and purpose are emphasized and in the third definition, inter related decisions are emphasized (Howlett M and Ramesh, 1995).  Various Policy Making Models In a classification presented by Jung (1986) for various policy making models, four main kinds have been introduced with the following titles: (Alvani, 2013) 1-Rational model 2-Incremental model 3-Critical model 4-Social model III. The Rational Model Overall, rationality refers to the belief that by using human mind, the world can be understood and interpreted (Heywood, 2002). The rational model assumes that humans have best behavior and obey the principle of scientific and experimental testing for discovering the truth and creation of novel knowledge (Taylor, 1987 and Weber, 1987). This definition of rationality is directly related to approving approaches in social science. General policy in this model notes that to reach maximum social benefit in use of scarce resources, the society needs to try. As a result, rational and logical policies are in policy models that while maximizing personal interests; minimize society’s cost in the framework of these scarce resources [effectiveness and taking advantage]. Therefore a policy is accepted only when first, cost of implementation is not more than benefits produced and secondly, among suggestions a choice is selected that in comparison to costs has the highest benefits (Dye, 2013). IV. The Incremental Model This model was presented by Lindblom and is really considered a budgeting method. In this model all choices and information are not involved in the decision making calculations and long term and comprehensive decisions are not made but short term ones. In this model, it is not attempted to seek all solutions. Goals are evaluated and reconsidered and primary purposes are obtained by compromise, adaptation and agreement. Additionally, this model pays attention to non economic issues and is not interested in complete and ideal rationality of directors (Lindblom, 1959). The incremental model is a more conservative method of decision making. This model is not predictive, because past programs and decisions are considered as criteria for new decision making and by acceptance of legitimacy of the present and past programs, continuation of past policies are agreed upon (Dye, 2013). V. The Critical Model Many organizations because of deficiency in resources and intense pressure for supply of increased services face crisis. Critical problems arise more from growth of social needs, budget deficiency, lack of quality necessary financial resources, incorrect decision making direction, decreased profit and dissatisfaction of clients and customers. In critical situations, policy makers do not pay much attention to inclusion of employees, clients and organizational teachings. Instead, they emphasize the importance of principles, regulations and present methods of the organization by implementing power and official and organizational discretion. Considering the three factors of time, level of awareness and severity of crisis, directors of the critical method endeavor to react only to crisis situations in a hasty and immediate way and try to preserve the present situation. This method of management leads to severe decrease in quality and level of activities of the organization (Gholipour, 2010).

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VI. The Social Model This model integrates the rational and incremental models to present a general decision making framework by attention to aspects of science and art and social characteristics. This model goes further than other models and by inclusion of social and philosophical considerations enables the director to resolve the difficulties and problems of the organization by better understanding of the truth of the management world. Along with use of principles and concepts of the rational and incremental models, it looks at higher and more comprehensive values in decision making. As a result, a social method policy maker integrates processes, beliefs and behaviors along with the other modelsâ&#x20AC;&#x2122; value system to expand and enrich models, opinions and actions of management in interpretation of company problems (Gholipour, 2010). VII. Innovation In European languages, innovation is taken from the latin root innovate which means building something new (Ramezani, Yablouni, 2006). Innovation means new ideas in work. In more precise terms, an innovation is successful that acquires help from new processes for creation and implementation of new methods that lead to advantage and improved quality of works (Agha Davoud, Hatami, Hakiminia, 2010). The first definition of innovation was presented by Joseph Schumpeter. According to his definition, innovation is reflected in outputs including the following items that lead to formation of a different task (Schumpeter, 1934): 1- New product or new quality of a product 2- Presentation of a new method of production 3- Creation of a new market 4- New method of provision 5- New organizational structure. Kanter defines innovation as the process of collecting any new and useful idea for solving a problem and believes that innovation includes formation of the idea, acceptance and implementation of new ideas in the process, products and services (Kanter, 2007). Crossan and Apaydin believe: Innovation is production, adaptation or advantage from a creation with increased value in the economic and social atmosphere such as recreating and extending products, services and markets, developing novel production methods and establishing modern management systems (Crossan & Apaydin, 2010). VIII. Indices of Evaluation for Innovation In 2000, the European Union announced indices of evaluation for innovation in four groups in response to world encompassment and science based economic changes and to attain its goals (promotion of innovation between member countries and being transformed into the most dynamic science based economy in the world)( OECD, 2005). 1) Human resources: level and quality of human resources are considered major determinants of creation and expansion of new knowledge in all of economy. 2) Creation of new knowledge: measures indices related to creation of knowledge, capacity and situation of countries. 3) Transfer and application of new knowledge: covers unofficial innovating activities such as new instruments for organization service and production systems, adaptation of innovations that have been developed by other enterprises and adaptation of new knowledge for company special needs. 4) Financial affairs and outputs of innovation: includes indices such as supply of risky capital, advanced technology, sale of innovation (Tabatabaian, 2006). IX. Research Hypotheses Considering the importance of policy making and also innovation in an organization, hypotheses of this study are: 1. The critical policy making model is effective on Sanam company innovation indices from view point of relevant directors. 2. The incremental model of policy making is effective on Sanam company innovation indices from view point of relevant directors. 3. The rational model of policy making is effective on Sanam company innovation indices from view point of relevant directors. 4. The social model of policy making is effective on Sanam company innovation indices from view point of relevant directors. X. Research Method The purpose of this study was applied and the method of data collection was descriptive-survey and regarding relationship between variables, it was correlation. Study variables included: policy making models (critical, incremental, rational and social) as the independent variables and innovation indices (human resources, creation of new knowledge, transfer and application of new knowledge and financial affairs and innovation outputs) as

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dependent variables under the influence of independent variables, the effects of which will be evaluated. Additionally, the statistical population of this research included all senior and middle directors and supervisors of the Sanam public industrial company, a total of 37 individuals. As a result, due to limitation of the statistical population, there was no need for sampling. The main tool for information gathering in this study was a 50 item questionnaire that was prepared by the researcher. For validity determination, a number of professors and experts were interviewed with this regard who confirmed the correctness and validity of the questionnaire. To evaluate the reliability of the questionnaire, the Cronbach alpha coefficient was calculated which 0.82 was. As a result, the research questionnaire had high reliability. In this study, before distribution of questionnaires, an explanatory session about the policy making models and indices of innovation was held for discussion by the directors so that they could complete the questionnaire with awareness of theoretical bases and the results of the research would gain better validity and precision. After collection of the questionnaires, research hypotheses were tested using the SPSS software. Hypothesis testing was performed using the single group student t test and next, using the Friedman coefficient, effect of policy making models on indices of innovation were ranked. XI. Results of the Study 1) Test of the first hypothesis: critical policy making model affects the organizational innovation indices. Considering Table (1), since the level of meaningfulness of effectiveness of the critical model on human resources, transfer and application of new knowledge and financial affairs and innovation outputs is less than 0.05, it can be concluded that the critical model has meaningful effect on human resources, transfer and application of new knowledge and financial affairs and innovation outputs. Table (1): Test of the first hypothesis Hypothesis

t statistics

Significance level

Situation of the hypothesis

Effect of the critical model on human resources

3.98

0.000

confirmed

Effect of the critical model on creation of new knowledge

2.21

0.089

Not confirmed

Effect of the critical model on transfer and application of new knowledge

3.16

0.001

confirmed

Effect of the critical model on financial affairs and innovation outputs

4.12

0.000

confirmed

2) Test of the second hypothesis: incremental policy making model affects the organizational innovation indices. Considering Table (2), since the level of meaningfulness of effectiveness of the incremental model on human resources, transfer and application of new knowledge and financial affairs and innovation outputs is less than 0.05, it can be concluded that the incremental model has meaningful effect on human resources, transfer and application of new knowledge and financial affairs and innovation outputs. Table (2): Test of the second hypothesis Hypothesis Effect of the incremental model on human resources Effect of the incremental model on creation of new knowledge Effect of the incremental model on transfer and application of new knowledge Effect of the incremental model on financial affairs and innovation outputs

t statistics 4.51

Significance level 0.000

Situation of the hypothesis Confirmed

1.98

0.11

Not confirmed

3.85

0.002

confirmed

3.41

0.03

confirmed

3) Test of the third hypothesis: rational policy making model affects the organizational innovation indices. Considering Table (3), since the level of meaningfulness of effectiveness of the rational model on human resources, creation of new knowledge, transfer and application of new knowledge and financial affairs and innovation outputs is less than 0.05, it can be concluded that the rational model has meaningful effect on human resources, creation of new knowledge, transfer and application of new knowledge and financial affairs and innovation outputs.

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Table (3): Test of the third hypothesis Hypothesis

t statistics

Significance level

Effect of the rational model on human resources Effect of the rational model on creation of new knowledge Effect of the rational model on transfer and application of new knowledge Effect of the rational model on financial affairs and innovation outputs

4.45

0.000

Situation of the hypothesis confirmed

4.12

0.000

confirmed

3.65

0.001

confirmed

3.45

0.002

confirmed

4) Test of the fourth hypothesis: social policy making model affects the organizational innovation indices. Considering Table (4), since the level of meaningfulness of effectiveness of the social model on human resources, creation of new knowledge, transfer and application of new knowledge and financial affairs and innovation outputs is less than 0.05, it can be concluded that the social model has meaningful effect on human resources, creation of new knowledge, transfer and application of new knowledge and financial affairs and innovation outputs. Table (4): Test of the fourth hypothesis Hypothesis Effect of the social model on human resources Effect of the social model on creation of new knowledge Effect of the social model on transfer and application of new knowledge Effect of the social model on financial affairs and innovation outputs

t statistics 4.22

Significance level 0.000

Situation of the hypothesis confirmed

3.15

0.01

confirmed

4.10

0.000

confirmed

3.67

0.001

confirmed

XII. Ranking of Level of Effectiveness of Policy Making Models on Innovation Indices Table (5) shows the following results: 1. The critical model has the most effect on the indices of financial affairs, innovation outputs and human resources and has the least effect on the index of creation of new knowledge. 2. The incremental model has the most effect on indices of human resources, transfer and application of new science and the least effect on the indices of financial affairs and innovation outputs. 3. The rational model has the most effect on indices of human resources, financial affairs and innovation outputs and the least effect on the index of creation of new knowledge. 4. The social model has the most effect on indices of human resources and transfer and application of new knowledge and the least effect on the index of creating new knowledge. Table (5): Ranking of level of effect of policy making models on innovation indices Model Critical

Incremental

Rational

Social

Organizational innovation index Human resources Creation of new knowledge Transfer and application of new knowledge Financial affairs and innovation outputs Human resources Creation of new knowledge Transfer and application of new knowledge Financial affairs and innovation outputs Human resources Creation of new knowledge Transfer and application of new knowledge Financial affairs and innovation outputs Human resources Creation of new knowledge Transfer and application of new knowledge Financial affairs and innovation outputs

Friedman coefficient 4.51 2.85 3.66 4.89 4.75 3.66 4.12 3.01 4.52 3.12 3.25 4.14 4.62 3.98 4.45 4.15

Rank 2 4 3 1 1 3 2 4 1 4 3 2 1 4 2 3

XIII. Discussion Now a day with complexity in competition, innovation is considered one of the major advantages of every organization in facing changes and now and future needs of stakeholders. In fact, company directors and policy makers have become aware of the issue that adopting correct innovative policies can guide the organization in facing with problems in a way that it can keep and improve its responsiveness and performance by using creativity, innovation, and competitive advantage. On this basis, in this study the effects of policy making models on indices of organizational innovation have been evaluated. Considering the results of the study, it can be concluded that the critical model has meaningful effect on the indices of human resources, transfer and application of new knowledge, financial affairs and innovation outputs. Additionally, this model has the most effect on indices of financial affairs and innovation outputs. Managers of the critical method by attention to

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three factors of time, level of awareness and severity of crisis try to respond only to critical situations in a hurried and immediate fashion and try to protect the present situation. With the advent of any small problem, it will be quickly solved. This method of management leads to great decrease in quality and level of company activities. As a result, this model does not consider the progress of the organization and tries to protect the present situation. Therefore, if directors select the critical policy making model, creation of knowledge will not increase much in the organization and the company’s added value of market production increases more. Additionally, the incremental model has meaningful influence on indices of human resources, transfer and application of new knowledge, financial affairs and innovation outputs. This model has the most effect on the index of human resources. In this model, all choices and information are not involved in decision making calculation. Instead, all choices are for short term purposes and long term and comprehensive decisions are not made. This model also attends non economic issues and gives a lot of importance to cooperation, adaptation and agreement in regards to goals. As a result, if policy making directors use this model, they will give importance to education and their employees in the areas of research, development and innovation and will have the most influence on the index of human resources. The rational model of policy making has meaningful effect on indices of human resources, creation of new knowledge, transfer and application of new knowledge and financial affairs and innovation outputs. Additionally, this model has the most effect on the index of human resources. From the view point of a rational manager, there is always a best way for solving the problems and difficulties of an organization. All possible solutions are searched for and the best is selected based on profitcost considerations. They consider one solution that appears the best for long term and do not pay attention to small short term problems and philosophical and social affairs. Therefore, all indices of innovation of an organization will increase based on this model. The social model of policy making has meaningful effect on the indices of human resources, creation of new knowledge, transfer and application of new knowledge and financial affairs and innovation outputs. Additionally, this model has the most influence on the index of human resources. The social model is formed by integration of the comprehensive rational and incremental models. This model by inclusion of social and philosophical considerations enables the director to resolve the problems of the organization by better understanding of truth. Additionally, considering the findings of this research, it is suggested that the directors of the Sanam public industrial company, for improving the index of human resources, use the three models of policy making of incremental, rational and social. In fact, from the view point of directors, special attention has been given to the index of innovation in human resources. Additionally, for improvement of the index of knowledge creation, the rational and social models of policy making are recommended and the critical and incremental models do not have an effect on this index. Also, for improvement in the index of transfer and application of new knowledge, the social and incremental models of policymaking are recommended and finally, to improve the index of financial affairs and innovation outputs, the critical model of policy making is recommended. References [1]

[2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

Agha Davoud, Seyed Rasoul; Hatami, Mahmoud; Hakiminia, Behzad (2010). “Evaluation of factors influential on organizational innovation among directors.” Specialization Social Sciences Journal of Azad Islamic University-Shoushtar Branch, Fourth year, Number 11.[in Persian] Alvani,Seid Mahdi &Sharifzadeh,Fattah,(2013). "Public Policy Making" Anderson, C(2005). "What's The Difference Between Policies And Procedures" Crossan, M. & Apaydin, M., 2010, A Multi-Dimensional Framework of Organizational Innovation: A Systematic Review of the Literature, Journal of Management Studies 47:6. Gholipour R (2010). “Process of policy making in Iran.” Center for the Islamic Parliament Council Research.[in Persian] Heywood, A. (2002); "Politics. Hampshire: Palgrave Macmillan" Howlett, M. Ramesh, M. Perl, A. (1995). "Studying Public Policy: Policy Cycles and Policy Decisions", Kanter, Rosabeth Moss, (2007), The Change Masters: Innovations for Productivity in the American Corporation, Historical Research Reference in Entrepreneurshin, University of Illinois. Lindblom, C. E. (1959); "The Science of Muddling Through"; Public Administration Review; 19.2, Pp. 79-88. Ramezani, Naser Ali; Yablouni, Hojatollah (2006). “Ten important factors of defeat of innovation.” Tadbir Monthly October; 160.[in Persian] R. Dye, Th. (2013), "Understanding Public Policy" 14th Edition, Schumpeter, J. A. (1934). Theory of Economic Development. Cambridge, MA: Harvard University Press. Tabatabaian, Seyed Habibollah; Pakzad, Mahdi Banab (2006). “Evaluation of systems of measuring innovation, presentation of a framework for evaluation of innovation in Iran.” Modarress Humanity Sciences Journal.[in Persian] Taylor, Fredrick; (1987) "The principles of Scientific Management"; in Louis Boone & Donald Bowen (Eds); The Great Writings in Management and Organizational Behavior; New York: Random House, Pp. 32-47. Weber, Max, (1987); "Legitimate Authority and Bureaucracy"; in Louis Boone & Donald Bowen (Eds); The Great Writings in Management and Organizational Behavior; New York: Random House, Pp. 5-18. OECD, 2005. Oslo Manual: guidelines for collecting and interpreting innovation data. 3rd edition, a joint publication of OECD and Eurostat.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Student Acceptance of Web-based Learning for Universities in Thailand Rungsan Suwannahong1, Terawat Piboongungon2 and Werayuth Charoenruengkit3 Rajamangala University of Technology Thanyaburi 39 Moo1, Klong6, Thanyaburi, Pathum Thani 12110 THAILAND Abstract: According to the revolution of the information technology that has been changing quickly. this paper is to study student acceptance of web-based learning for universities in Thailand. Population in this study is bachelor’s students selected from Faculty of Science and Technology in 10 universities with plenty of usages of web-based learning and have offered web-based learning system more than 1 year. Quantitative methods are employed and UTAUT theory is applied as technology acceptance model. Research findings are as follows: performance expectancy, effort expectancy, social influence, facilitating conditions and behavioral intention to use have relationship to usage behaviour with a model fit and regression weight significantly supporting hypotheses (p<0.05). Keywords: Web-based learning/UTAUT/SEM/Learning/Universities in Thailand I. Introduction According to the revolution of the information technology that has been changing quickly ([1],[8]), in Thailand, web-based learning has widely used for examples new staff training in the organization, knowledge sharing in community and student learning in university. Although, web-based learning is widely used in Thailand [7], National Science and Technology Development Agency reported that the percentage of visiting the education website in year 0210 still have a little quantity with 8.2% of all internet activities undertaken by individuals in Thailand [5]. The purpose of this study are 1) to determine the four influential factors and one moderator, namely, performance expectancy, effort expectancy, social influence, facilitating conditions, university policies, and how these factors influence usage behavior and behavioral intention to use web-based learning system, 2) to establish web-based learning adoption models, and 3) how to increase the usage of web-based learning systems. II. Literature review There are many theories or models of individual acceptance and technology adoption. The 8 popular theories or models are the Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT), Model of PC Utilization (MPCU), Social Cognitive Theory (SCT), Motivational Model (MM), and Combined TAM and TPB (C-TAM-TPB). Although each of these models have their strength, their abilities are limited and can only achieve in describe approximately 30-40% of the predictable in user behavior. In 2003 Venkatesh et al. presented the Unified Theory of Acceptance and Use of Technology (UTAUT) which consists of eight perspectives in the field of technology acceptance research. Venkatesh shown that 3 direct factors of behavioral intention to use a technology are performance expectancy, effort expectancy and social influence and 2 direct factors of technology use are behavioral intention and facilitating conditions and 4 moderators are gender, age, experience and voluntariness may have effect to the factors on behavioral intention and / or use behavior. It has achieved in describing about 60-70% of the predictable in user behavior.([9],[10]) III. Problem Statement The low usage of web-based learning in Thailand could be caused by several reasons. First of all, the users’ attitude toward web-based learning, users would prefer to participate with one another in traditional classroom environment rather than learning in web-based learning which left them feeling isolated. Secondly, the aspect of social influence on the students, where the users have no one to influence them or giving them advise on the advantage of using web-based learning. Thirdly, performance expectancy of individual that believes the system will help him or her achieving better learning performance. Next, the level of effort required to use the system. National Statistical Office Ministry of Information and Communication Technology stated that in year 2013 the proportion of households with the Internet connection is 23.5%. Therefore, student should spend more time and efforts to learn how use the system effectively. Finally, the quality and availability of networking facility, which provided by the universities to support their students to use the web-based learning systems, may not be sufficient for students to make uncomplicated connection to the system. IV. Results and Discussions The result of demographic data of respondent consists of four parts. Firstly, the proportion of responded was same between female and male (50.0%). Secondly, the majority of web-based learning responsible was the ages less

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than or equal to 19 years old (37.3%) and the ages more than or equal to 23 years old (34%). Thirdly, the majority has experience less than 1 year in using web-based learning (37.5%) and the experience more than 5 years (33.8%). Finally, the majority frequency of using web-based learning was between 3 to 5 times per week (33.8%) and using web-based learning more than 5 years (32.3%). Respondents find the web-based learning useful was Strongly Agree Level(mean=4.51) with an S.D. of 0.664, respondents accomplish tasks more quickly when using web-based learning was Strongly Agree Level (mean=4.45) with an S.D. of 0.761, respondents using web-based learning for increases productivity was Strongly Agree Level (mean=4.39) with an S.D. of 0.764, and respondents using web-based learning for increase chances of getting a score was Strongly Agree Level (mean=4.55) with an S.D. of 0.639. Respondents think that web-based learning would be clear and understandable was Strongly Agree Level (mean=4.73) with an S.D. of 0.491, easy to become skillful was Strongly Agree Level (mean=4.67) with an S.D. of 0.537, web-based learning easy to use was Strongly Agree Level (mean=4.75) with an S.D. of 0.457, and operate the web-based learning is easy also was Strongly Agree Level (mean=4.67) with an S.D. of 0.511. People who influence their behavior think that they should use was Strongly Agree Level(mean=4.63) with an S.D. of 0.678, people who are important to them think that they should use was Strongly Agree Level (mean=4.37) with an S.D. of 0.612, the senior management of the university has been helpful was Strongly Agree Level (mean=4.37) with an S.D. of 0.612, and the university has supported also was Strongly Agree Level (mean=4.42) with an S.D. of 0.587. Respondents have the resources necessary to use was Agree Level(mean=4.19) with an S.D. of 0.808, respondents have the knowledge necessary to use was Agree Level (mean=3.97) with an S.D. of 0.931, the webbased learning is not compatible with other systems was Agree Level (mean=4.09) with an S.D. of 0.741, and a specific person or group is available for assistance with web-based learning difficulties also was Agree Level (mean=4.07) with an S.D. of 0.777. Respondents intend to use the web-based learning in the next 6 months was Strongly Agree Level(mean=4.55) with an S.D. of 0.607, respondents predict they would use the web-based learning in the next 6 months was Strongly Agree Level (mean=4.32) with an S.D. of 0.747, respondents plan to use the web-based learning in the next 6 months was Strongly Agree Level (mean=4.42) with an S.D. of 0.612, assuming respondents had access to the web-based learning, they intend to use it was Strongly Agree Level (mean=4.23) with an S.D. of 0.571,.and given that respondents had access to the web-based learning, they predict that they would use it also was Agree Level (mean=4.16) with an S.D. of 0.492. Respondents do not evaluate costs and benefits of using the web-based learning before every use was Strongly Agree Level (mean=4.35) with an S.D. of 0.710, respondents carefully think about using the web-based learning before every use was Agree Level (mean=3.90) with an S.D. of 0.765, and respondents use of the web-based learning is automatic was Strongly Agree Level (mean=4.22) with an S.D. of 0.712. This study using Structural Equation Model for data analysis that requires all variables should be normal distribution, measured from skewness and kurtosis, and observe variables should have reliability; the Cronbachâ&#x20AC;&#x2122;s alpha value above 0.7 is a criterion for accepted reliability. Figure 1: SEM research model

The result of model fit testing showed as follow: Chi-Square=352.843, df=177, p-value= .000, GFI=0.928, AGFI=0.897, RMSR=0.027, RMSEA= 0.050 (PCLOSE=1.00), NFI=0.940, CFI=0.969 and Hoelter=237 (0.01)

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Table 11: Standardized regression weights for model BI BI BI UB UB PE4 PE3 PE2 PE1 EE4 EE3 EE2 EE1 FC4

<--<--<--<--<--<--<--<--<--<--<--<--<--<---

PE EE SI FC BI PE PE PE PE EE EE EE EE FC

Estimate 0.440 0.145 0.089 0.140 0.367 0.688 0.902 0.837 0.438 0.811 0.780 0.791 0.950 0.749

P *** *** * ** *** *** *** *** *** *** ***

FC3 FC2 FC1 SI4 SI3 BI1 BI2 BI3 BI4 UB1 UB2 UB3 BI5 UB1

<--<--<--<--<--<--<--<--<--<--<--<--<--<---

FC FC FC SI SI BI BI BI BI UB UB UB BI UB

Estimate 0.522 0.871 0.821 0.893 0.886 1.142 0.786 1.005 0.663 0.858 0.834 0.881 0.549 0.858

P *** *** *** *** *** *** *** *** *** ***

Note: *. Correlation is significant at the 0.05 level (2-tailed), **. Correlation is significant at the 0.01 level (2-tailed). ***. Correlation is significant at the 0.001 level (2-tailed). The discussion of the usage behavior on web-based learning for universities in Thailand is as followings: The performance expectancy found that the agreement is at strongly agreed. The respondents will have intention to use web-based learning when the web-based learning can increase their chances of getting a score; web-based learning is useful and helps them to do their tasks more quickly. The effort expectancy found that the agreement is at strongly agreed. The respondents will use web-based learning when function on web-based learning has easy to use, define to be clear and understandable, and ease to operate it. The social influence found that the agreement is at strongly agreed. The social influences have effect to usage on web-based learning of respondents only that they have supported from the university. The facilitating conditions found that the agreement is at agreed. University should provide facilities necessary for the learning of students in a learning system on the Web. Web-based learning system should be able to transfer data via the learning system and other. Universities should have a helper or a team or help menus are available on the web when learning difficulties occur. The behavioral intention found that the agreement is at strongly agreed. Behaviors of the students are likely to use the system to learn more in the future. The usage behavior found that the agreement is at strongly agreed. Students did not evaluate the benefits of using a web-based learning before every use. The student’s usage of web-based learning system is automated and students thinking carefully about the use of system before using the system. V. Conclusion The student’s adoption of web-based learning will be increase because their expectations that the web-based learning system to be able to help him to get a high score in examination. University facilities are enough to support them for using of the web-based learning system. Web-based learning system must be user friendly and free effort and should have a helper or a team or help menus are available on the web when learning difficulties occur. References [1] [2]

[3] [4] [5] [6] [7] [8]

[9] [10].

AbuShanab, E., Pearson, J. M., and Setterstrom, A. J., “Internet Banking and Customers’ Acceptance in Jordan: The Unified Model’s Perspective”, Communications of the Association for Information Systems, 26(1), pp. 493-524, 2010 Alenezi, A. R., Karim, A. M. A., and Veloo, A., “An Empirical Investigation into the Role of Enjoyment, Computer Anxiety, Computer Self-Efficacy and Internet Experience in Influencing the Students' Intention to Use E-Learning: A Case Study from Saudi Arabian Governmental Universities”, Turkish Online Journal of Educational Technology - TOJET, 9(4), pp. 22-34, 2010 Beek, M. V., “Virtual Learning in Michigan's Schools”, Midland: Mackinac Center for Public Policy, 2011 Pergola, T. M., and Walters, L. M., “Evaluating Web-Based Learning Systems”, Journal of Instructional Pedagogies, vol. 5, 1-117, 2011 National Science and Technology Development Agency, “Thailand ICT Indicators”, 2010 Ramayah, T., “The Role of Voluntariness in Distance Education Students' Usage of a Course Website”, Turkish Online Journal of Educational Technology - TOJET, vol. 9(3), pp. 96-105, 2010 Statistical Forecasting Bureau, “The 2013 ICT Indicators”, National Statistical Office Ministry of Information and Communication Technology, Tana Press.Co.,Ltd, 2013 Šumak, B., Polančič, G., and Heričko, M., “An Empirical Study Of Virtual Learning Environment Adoption Using UTAUT”, Proc. the International Conference on Mobile, Hybrid, and On-Line Learning Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05430017, 2010 Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D., “User acceptance of information technology: toward a unified view”, MIS Quarterly 27(3), pp. 425-478, 2003 Venkatesh, V., & Zhang, X., “Unified Theory of Acceptance and Use of Technology: U.S. Vs. China”, Journal of Global Information Technology Management, 13(1), 5, 2010.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net

INTEGRATING E-SERVICE WITH A OIL REFINERY E-COMMERCE USING DATA MINING T.Saranya1, J.K. Anu Shakthi Priya2, K.Poornima3 School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, INDIA. _______________________________________________________________________________________ Abstract: E-business is the relevance of information and communication technology (ICT) to carry out all the business activities. When any business go online, it has to decide which e-business model best suits their organization’s goals. A business model should describe on what grounds the organizations generates, distributes and confines values in profitable, societal, intellectual and in other circumstances. To select best appropriate business strategy we made use of association rule mining and also we integrated both e-business models and cloud computing features to overcome the general problems faced by online vending. Common trading involves consumer demands such as shipping, mobile access, customization, information and convenience. All these mentioned strategies are explained and illustrated with case study: Oil Refinery process. Keywords: E-commerce ,cloud computing, data mining, association rule ,oil refinery. __________________________________________________________________________________________ 1,2,3

I. Introduction In this day and age ‘e’ is gaining drive and if not the whole thing is made digitally possible. There are types of e-commerce and it will be chosen according to the type of bond involving diverse faces of commerce. Some of the types which being made use in this paper is as follows, B2B- Business to Business, B2C- Business to Consumer, C2B- Consumer to Business, B2I- Business to Internal. B2B: The commerce transactions between businesses i.e., between manufactures and wholesaler or retailer are illustrated by this type. The transaction degree will be comparatively higher than others and prime cause behind this is that the sequence of process involved in the production and distribution of a commodity have many B2B transactions of secondary elements or unprocessed stuffs. It also involves the circumstances of exchanging data and partnership. C2B: It is a type which is vice versa of B2C i.e., user proposes services or products to third party therefore company will compensate them. For example let us put forth the elance, it is one of the website to promote this type of business. In this it permitted vendor to publicize their proficiency and eventually bargain hunter to advertise their ventures. B2I: It is an emerging segment of electronic commerce types. Here it distinguishes the people’s online acquiring routines are alike whether they’re retailing for individual or for their industry use. B2C: As name implies this type of businesses supplies the end users with their goods or as service. Any business or organization which wishes to sell its products can make use of this business to consumer type of model for its best adoption. It not only deals with the online transactions but also the serves the e-banking, public sale in online, health related sites, travel services etc,. Data mining is one kind of technique which is used to extract interesting patterns or knowledge from large databases. There are lot of techniques that have been used to find such kind of knowledge, most of them resulting from statistics and machine learning. The main aim is to discover accurate knowledge from large datasets. The task performed by data mining depends on what kind of knowledge someone needs to mine. Data mining techniques are the consequence of a lengthy process of research. The kinds of tasks performed by data mining techniques are Regression, clustering, classification, association and prediction. II. Association Rule Mining In data mining, the association rule is introduced to find hidden patterns from large datasets and conclude useful result on how a subset of items influences the presence of another subset. Let T={T1,T2,T3.................Tn} be a universe of items and A={A1,A2,A3,.....................An} is a set of transactions. Then expression X--Y is an association rule where X and Y are item sets and X ∩ Y=Ф. Here X and Y are called antecedent and consequent of the rule respectively. The main feature of this rule is support and confidence. Support is a set of transactions in set A that contain both X and Y. Support(X=>Y) = Support (XUY) = P(XUY) Confidence is percentage of transactions in A containing X that also contain Y. Confidence (X=>Y) = Support (XUY) / Support(X) = P(Y/X).

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If support ≥ minsup and confidence ≥ minconf then the association rule is termed as strong association rule. There are two types of association rules named as positive association rules and negative association rules. Using Cloud Computing in E-commerce Application: In E-Commerce a new practice evolved as Cloud computing. The ecommerce and cloud computing has not yet stretched a advanced stage and still needs the test of practice. At present, there are still many glitches that requisite to be solved. They are policy security, supervisory issues of services, data refuge and privation of relevant laws and conventions. As procedure propagates, energy depletion and the atmosphere become more dissimilar, due to a countless variety of computing resources being offered, so grows managing complexity. IV. Case Study (oil refinery) Apriori Algorithm: Apriori, the lain word means “from what comes before”. It is a classical algorithm used to mine frequent patterns from huge amount of data. It follows bottom up strategy. This algorithm works upon categorical data. It follows breadth first search technique. Terms Used in Apriori Algorithm  Min_supp: Stands minimum support. It is used for searching frequent patterns that satisfy this threshold.  Min_conf: Stands Minimum confidence. It is used for finding the strong association rule that satisfy this constraint.  Frequent Itemset (Li): It is denoted by Li, where I means ith item. The item sets satisfies the minimum support (min_supp) threshold .  Apriori Property: This property is very useful for trimming irrelevant data. It states that any subset of frequent item set must be frequent.  Prune step: It is used to find frequent itemsets, for any (n-1)-itemsets that is not frequent cannot become subset of a frequent n-itemset . Oil Refinery System: The current paper shows the e business components used in the oil industry based on the largest companies worldwide. The top twenty companies in the worldwide oil industry based on their revenues are Permex,Ecopetrol,Suncor,Chesapeake,Surgutneftegas,Pertamina,Libya,Socar,Adnoc,Lukoil,Rosneft,Sinopec,Ec opetrol,Inpex corp,Woodside, Xcel energy,Holyfrontier,Nobel energy , Caltex, Osaka. TABLE-1 B2B Items X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20

Items X1 X2 X3 X4 X5 X6 X7

P1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1

Q1 0 0 0 0 0 0 0

Q2 1 1 1 1 1 0 0

P2 1 1 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 0 1

Q3 1 1 1 0 1 1 0

P3 1 1 0 0 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1

Q4 1 1 1 1 1 1 1

Q5 1 1 1 1 1 0 0

P4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0

Q6 1 1 1 1 1 1 1

Q7 0 0 0 0 0 0 0

P5 1 1 1 1 0 0 1 0 0 0 1 1 1 1 1 0 0 0 0 1 B2C Q8 0 0 0 0 0 0 0

Q9 1 1 1 1 1 0 0

P6 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1

Q10 1 1 1 1 1 1 1

P7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Q11 1 0 1 1 0 0 0

Q12 1 1 1 1 1 0 0

P8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0

Q13 1 1 1 1 1 1 1

Q14 1 1 1 1 1 0 1

P9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Q15 1 0 1 1 1 0 0

Q16 1 1 1 1 1 1 0

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T.Saranya et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 3639 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20

0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 1 1 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 1 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 1 1 0 0 0 0 0 0 0

0 1 1 1 1 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 1 1 0 0 0 0 0 0 0 0

0 1 0 1 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 1 0 0 0 1 0 0 0 0

C2B Items X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20

Items X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20

R1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

S1 1 1 1 1 1 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0

S2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

S3 1 1 1 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0

R2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 B2I S4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

S5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

R3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

S6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

S7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

S8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Note: X1-X20 are oil companies, including Pemex (X1), Ecopetrol (X2), Suncor (X3), Chesapeake (X4), Surgutneftegas (X5), Pertamina (X6), Libya (X7), Socar (X8), Adnoc (X9), Lukoil (X10), Rosneft (X11), Sinopec (X12), Ecopetrol (X13), Inpex Corp (X14), Woodside (X15), Xcel Energy (X16), HollyFrontier (X17), Nobel Energy (X18), Caltex (X19), and Osaka (X20). TABLE-2 Analysis of e-Business components using association rule. Company X1 X2

B2B Count 9 9

B2B Support 1 1

B2C Count 13 11

B2C Support 0.81 0.68

C2B Count 1 1

C2B Support 0.33 0.33

B2I Count 7 7

B2I Support 0.87 0.87

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T.Saranya et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 3639 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20

8 8 8 7 8 9 6 9 8 7 8 6 7 7 4 5 3 4

0.88 0.88 0.88 0.78 0.88 1 0.66 1 0.88 0.78 0.88 0.66 0.78 0.78 0.44 0.55 0.33 0.44

13 12 12 8 6 5 7 4 5 3 3 4 2 0 2 0 1 0

0.81 0.75 0.75 0.5 0.37 0.31 0.43 0.25 0.31 0.18 0.18 0.25 0.12 0.0 0.12 0.0 0.06 0.0

0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0.0 0.0 0.0 0.66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

7 7 7 7 7 6 6 6 5 6 5 5 5 5 5 5 5 5

0.87 0.87 0.87 0.87 0.87 0.75 0.75 0.75 0.62 0.75 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.62

Analysis of e-Business components using association rule: In Table-1, the companies and e-business components are listed.1 in table refers that a particular company implements that particular components and 0 refers that component does not implement. In table-2, the support of particular components implemented by certain companies are calculated. V. Conclusion and Future Work Now a days several organizations were in the necessity to obtain benefits of electronic business and thus IT has exaggerated oil industry in abundant means. As far as oil industry is concerned the greatest benefit that technology offers to make process safer and the major focus will be on rate minimization and prevention of shortages at gas location so this can be made by energy security which engrosses the running of operations and proficient utilization of oil. This energy security will reduce the disequilibrium between the supply and demand. Almost all the applications like e-commerce, medicine, clustering, etc uses the Data mining concepts and techniques. In this paper association rule algorithm has been used which profits our mentioned area in many ways and some of the notable advantages are the improved efficiency of process and reduced I/O costs. Through the discovery it is understood that, the assimilation of e-commerce application model based on cloud computing guarantees huge volume of data storage, outstanding speed rate of computing, and cloud’s own characteristic as its allocation and resource sharing. So it will pave a new way for even growth of e-commerce. Even though this is in the near beginning point of discovery, few problems are not well carried out yet. Whichever way, e-commerce application model based on cloud computing will not prevent its rapidity to progress. As the cloud computing increasingly used, e-commerce will undoubtedly marshal in a new-fangled era of cloud computing. VI. References [1] [2] [3] [4] [5]

[6] [7] [8]

Jelena Zascerinska, Andreas Ahrens, “ E-business applications in Engineering education” in international Conference . Aakif Nazeer Khan and M. Hussain , “Application of Semantic Web in e-Business and Telecommunication“ in International Conference on Advanced Computer Control. Myint Myint Khaing, Nilar Thein, ”An Efficient Association Rule Mining For XML Data” in SICE-ICASE International Joint Conference 2006. Z. Yang, W. H. Tang, A. Shintemirov, and Q. H. Wu, “Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers” . Idheba Mohamad Ali O. Swesi, Azuraliza Abu Bakar, Anis Suhailis Abdul Kadir, “Mining Positive and Negative Association Rules from Interesting Frequent and Infrequent Itemsets ” in proceeding of 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012. Security Attack Mitigation Framework for the CloudEsha Datta ,Neeraj Goyal , Indian Institute of Technology On Evaluating and Publishing Data Concerns for Data as a Service Hong-Linh Truong and Schahram Dustdar Distributed Systems Group, Vienna University of Technology Software Development for Cloud: An Experiential Study Marimuthu Cand K. Chandra Sekaran,Department of Computer Science and Engineerin,National Institute of Technology, Karnataka, India.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Business Correspondent (BC) Model – A Bridge between Banks and Unbanked Dr. N. Sundaram1, Mr. M. Sriram2 Professor of Commerce1, Research Scholar2 School of Social Sciences and Languages1, 2 VIT University, Vellore – 632 014, Tamil Nadu, INDIA _____________________________________________________________________________________ Abstract: This study investigates the achievements and difficulties of Business Correspondent (BC) models, which acts as a bridge between banks and unbanked, provides banking products and services to the unbanked population at doorstep. The study was conducted in Vellore and Katpadi village blocks of Vellore district, Tamil Nadu, with villages more than 2000 unbanked population. In the study area, eight nationalized bank branches were involved in appointing BC models and thirty villages were identified as unbanked; each village has one BC model. The achievements of BC models to transform unbanked to banked were found to be consistent in the study area. Technological glitches and settlement of transacted accounts on the same day were found to be major difficulties faced by the models in providing banking products and services to the unbanked. Keywords: achievements; Business Correspondent (BC) models; difficulties; unbanked; Vellore district ______________________________________________________________________________________ I. Introduction The evolution of financial system must emphasize to include financial partaking from all segments of the nation [1]. Many researchers affirm that enriched financial system features the disadvantaged and underprivileged people to access financial system, especially rural, who are about 70 % in India [2]. Government of India is spending more than 55 % of its total national expenditure for developing the financially underdeveloped regions but still, many people are standing far away from the banking and financial products and services such as savings, credit, loans, payments and insurance [3]. If the people do not have money to meet their basic needs because of illiteracy and unemployment, it will pave the way to inequality of income and poverty, which are evils for human beings in the economy [4]. Thus augmented access to banking system and its allied services will thrust the living standard of the people to economic growth. A. Financial Inclusion – A Brief Outline Financial inclusion is the delivery of banking and financial services at affordable costs to low-income sections and underprivileged sectors of the society. Around 2.5 billion working-age adults globally have no access to the types of formal financial services rendered by regulated financial institutions like banks. Out of these un-served working-age adults, 2.2 billion lives in Asia, Africa, the Middle East and Latin America. The prime objective of the financial inclusion policy is to provide banking and banking and financial services to the entire humankind without discrimination [5]. In India, the Khan Commission was set up by the Reserve Bank of India (RBI) in the year 2004 to initiate and look upon the financial inclusion. In the mid-term review of the policy i.e., 200506, the recommendations of the commission was incorporated. It was reported that the banks were pressurized by the RBI, in order to achieve greater financial inclusion by making basic ‘no-frills’ account available to all the people of the nation. The financial inclusion was first featured in India during the year 2005, brought in by Mr. K. C. Chakrabarthy, the chairman of Indian Bank during that period. After that initiation, the first village in India, where all the households were financially included was Mangalam, located in Puducherry district. After this success, the commercial banks were permitted by the RBI to make use of intermediaries, to provide banking and financial products and services in the year 2006. The intermediaries considered by the commercial banks were Non-Governmental Organizations, Micro-Financial Institutions, Self Help Groups and other civil society organizations. Those intermediaries were used as business correspondent models by the commercial banks and were asked to start 100 % financial inclusion campaign in different regions of the nation. As the result of the campaign, it was announced that 100 % financial inclusion were achieved in many of the districts among states and union territory such as Kerala, Himachal Pradesh and Puducherry [6]. The Government of India (GOI) and the RBI took concerted efforts for more than five decades to promote financial inclusion, where it was considered as one of the important objectives of the nation. The key efforts includes nationalization of commercial, co-operative and regional rural banks, building up of robust branch network of scheduled commercial banks, lead bank scheme, introduction of mandated priority sector lending targets, opening of zero balance accounts, formation of self-help groups and appointing business correspondent models by the banks and permit them to deliver banking and financial products and services to the people at door step [7]. By leveraging the Information and Communication Technology (ICT), the RBI, through the ‘vision for the year 2020’, has

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planned to open around 600 million new customers’ accounts and provide banking and financial products and services through financial inclusion drive. B. Business Correspondent (BC) Model – A Bridge Between Bank and Unbanked It is the commitment of the financial institutions to heed all financial transactions of the people in safe manner. Banks are the most reliable financial institution rendering banking and financial products and services in effective manner. There are more than 82,000 banks and branches established to serve Indian people but the people especially in rural region are found without savings account [8]. The reason is lack of accessibility, where many rural people said that they do not have enough money to save and do not find bank near their residence. To make proper access to bank, its branch must be opened in rural region which is not an easy task and needs to complete lot of structured formalities and many regions in the country are villages which consist of only around 1,000 to 2,000 peoples per village, and they do not have a formal financial institution to make financial transactions [9]. To transpire these vulnerable groups from this problem, the RBI designated Business Correspondent (BC) models from January, 2006 to connect banks and unbanked population. The institutions were given priority to act as models on inception. But it was found to be tough job for those institutions to reach each individual at doorstep. Accordingly, individuals such as retired teachers, bank and Government employees, ex-service man, individual shop owners, petrol pump owners and other individuals qualified up to secondary level (10th Standard) can act as BC model, which makes the people to avail active and wide range of financial and banking products and services at doorstep with the way to economic opportunities [10]. II. Review of Literature In the light of above debate, financial inclusion will be more effective in the nation if the banks are tied up with BC models [11]. To make people connected to the models, it is better to handle technological tactics that facilitate financial inclusion efficiently by using smart card, biometrics and hand-held devices for disbursement of salaries and wages so that the population with reliable source of income would be saved regularly and thereby increasing financial inclusion [12]. Through Government social schemes, the wage earners can use this facility. Banks are setting financial goals to make the unbanked into the boundary of financial inclusion. An Indian study focused on the financial exclusion and its causes, consequences and corrective measures with BC models efforts. There are certain factors such as that determine the BC models efforts and challenges to include the people into banking system [13]. The key cause for financial exclusion is financial illiteracy among the people. The knowledge about banking system must be provided to people by the banks, but due to over population, it is not possible to reach the people directly and hence BC models serves people on banks’ behalf, providing service with financial knowledge. Rural people, especially unemployed are hesitated by bank to apply credit because of lack of source for repayment. So they are seeking unauthorized financial institutions for credit and suffering to repay with high rate of interest [14]. An Indian study was conducted by [15] to know whether BC models were helping the banks to open bank accounts at the doorstep. The study highlights that students and unemployed people were used as BC models. But the rule of the Reserve Bank was to appoint BC models with minimum qualification upto secondary level. It was the task of the concerned authorities to strengthen the financial system which will delegate the work to the right person and the service will reach the appropriate people. The Reserve Bank paid attention to promote the significance of financial inclusion through BC models in central province of India. BC models were made to involve in that drive and they experienced the plan as success with viability. The models were found to be dedicated with commitment to reach financial inclusion to the greater extent [16]. Business Correspondent models were mostly found as individuals adopted by the banks to make financial transactions because of the only expense called commission, rather installing a branch in rural region and spending money on operating and administrative costs [17]. These models do not only open bank account to the people, they also provide services such as loan, insurance and remittances, which makes the safety and entrepreneurial prosperity among the people, leads to increase in living standard and economic growth. In the view of the banks and people, [18] stated that BC models were found to be the robust mediate to connect them financially. On the other way models face issues and challenges while handling financial inclusion drive. They found technical issues on hand – held devices and lack of internet facility as well. The support from the bank employees were found to be less which mortify the models to work on financial inclusion to needy people. [19] imparted that these models should be considered as vital element of business strategy to attain greater financial inclusion. The GOI and the RBI should reinforce the policies implemented towards banks in relation with BC models so that they can play a better role in the financial arena to financially include the weaker section. III. Objective of the study This study was carried out to comprehend the achievements and difficulties of the BC models in Katpadi and Vellore village blocks of Vellore district. IV. Methodology Descriptive research design is found to be suitable for this study because it portrays the facts found among the BC models in the study area. Non – probability sampling design and Convenience sampling technique is used for this study because the respondents were identified deliberately from the study area. To collect the data, the

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banks of Katpadi and Vellore village blocks in the Vellore district were initially identified, and then the officials of the banks were requested to provide the information about BC models being appointed. The contacts of the models collected from the bank officials were exercised to check their availability and the data were collected through semi – structured questionnaire. Total of 30 BC models were chosen as sample from Katpadi and Vellore village blocks of Vellore district. This study uses percentage analysis and One – Way ANOVA to test the significant difference between and within the variables. V. Results and discussions A. Achievements of BC models in the study area As per the RBI statute to appoint BC models, the demographic profile was designed by the researcher, which is annexed as III. This study was conducted among 30 BC models, taking care of thirty different unbanked villages of Katpadi and Vellore village blocks of Vellore district, appointed by eight banks such as Canara Bank (CB), Central Bank of India (CBI), Corporation Bank (CORP), Indian Bank (IB), Indian Overseas Bank (IOB), Syndicate Bank (SB), State Bank of India (SBI) and Union Bank of India (UBI). The respondents (BC models) of this study were asked about their attainment on hauling the unbanked region into bank realm through opening of bank accounts in the study area. Percentage method was used to describe the information collected from the models. From the total of five village blocks under Katpadi and Vellore blocks, the number of bank accounts opened by models were found to be consistent on the whole (Please refer Annexure IV). When each village block was taken into account, Anaicut village was found holding maximum number of unbanked population, showed an increasing trend on number of bank accounts opened by the models. On identifying the utmost and least number of accounts opened in each village, a village named as Thirumani located in K.V. Kuppam village block unveiled the highest percent of bank accounts opened (97.33 %) and Serkadu village in Katpadi village block divulges the least of 9 %. Remaining villages showed a consistent raise in percentage. B. Difficulties faced by BC models in connecting banked with unbanked As per the information extracted through this study, it was clear that more unbanked people got benefitted through these models. However, to reach the financial inclusion to these people, models faced difficult situations, which were found in Annexure I. Among these, technological glitches were found as difficulty for 13 models out of 30. They said that the hand – held machines such as bio-metric scanners and smart card readers were faulty and those products should be troubleshooted. It was indicated by 9 % of the models that the banks instructed them to settle transacted accounts on the same transacted day, which was not possible for respondents resided in remote areas and the information provided by the bank regarding financial transactions and its procedures were not understandable by the models, which created a biased situation to the models to reach the customer with incomplete information. These difficulties were correlated with demographic profile of the respondents using One – Way ANOVA to find out the significant difference within and between these groups. The result (Please refer Annexure II) showed that there was a significant difference within and between all the variables of two groups, hence significantly confirmed that the difficulties were faced by the models to reach financial inclusion in the study area. VI. Recommendations Taking into account the results and discussions, BC models are contemplated as the integral part of financial system. The models should be trained to overhaul the devices used for financial transaction so that they need not want to depend upon technological troubleshooters all the time. The hesitant to financial inclusion must be counseled by financial literates, about benefits of savings through models so that they can be included into financial access criteria. As rural regions are inhabited mostly by illiterates, the models should make use of regional language and construct the idea of financial inclusion in the minds of the people. The very key purpose of appointing BC models by the banks is to look after the regions that are remote and inaccessible, where the models should approach door by door in the village. So it is not possible for them to submit their transactions to the bank on the same day. The banks should provide a reasonable time for models to settle the financial transactions. VII. Conclusion The crisis of the global economy in spite of its roots in financial system of developing countries is likely shifting its focus on financial inclusion policies. This study unveils that the BC models are fortifying the unbanked rural people into the policy of financial inclusion consistently. The Indian Prime Minister Mr. Narendra Modi made an all-out campaign on August 15, 2014 towards promoting financial inclusion policy throughout the nation and planned for covering over 20 Crore households into financial inclusion by the year 2017. Through this, the models will get a great opportunity to serve the unbanked people at wider range. In the mean time, it is the huge responsibility of the Reserve bank and Indian Government to pull out the difficulties faced by the models, which will make them to serve maximum people that lead to increase living standards of the people and economic growth as well. The Scope of this study is limited to village blocks of Vellore and Katpadi in Vellore district of Tamil Nadu, India. Further scope is possible by extension of study to neighbor blocks, comparison between districts, states and countries as well.

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References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]

Kumar, Nitin. "An empirical analysis of financial inclusion across population groups in India." The IUP Journal of Bank Management 10, no. 1 (2012): 97-111. Bhanot, Disha, Varadraj Bapat, and Sasadhar Bera. "Studying financial inclusion in north-east India." International Journal of Bank Marketing 30.6 (2012): 465-484. Mohan, Rakesh. "Economic growth, financial deepening, and financial inclusion." (2006). Levine, Ross. "Financial Development and Economic Growth: Views and Agenda." World Bank policy research working paper 1678 (1999). Beck, Thorsten, and Patrick Honohan. Finance for all?: Policies and pitfalls in expanding access. Vol. 41792. World Bank Publications, 2008. Sarma, Mandira. Index of financial inclusion. Indian Council for Research on International Economics Relations, 2008. Agarwal, Vikas, Sachin Gupta, Shalini Kapoor, Sumit Mittal, and Dinesh Pandey. "Reaching the Masses through a Rural Services Platform." In SRII Global Conference (SRII), 2011 Annual, pp. 440-447. IEEE, 2011. Collins, Daryl, Jonathan Morduch, Stuart Rutherford, and Orlanda Ruthven. Portfolios of the poor: how the world's poor live on $2 a day. Princeton University Press, 2009. Khan, H. R., and R. N. Dash. "Financial Inclusion: A Study of Business Correspondents in Orissa." CAB Calling (2007): 8-12. Rath, B., M. Ramji, and A. Kobishyn. "Business Correspondent Model: A Preliminary Exploration." (2009). Natu, Anant Jayant, Aashish Bansal, Amrita Kurian, Gurinder Pal Singh Khurana, and Tanushree Bhushan. Linking Financial Inclusion with Social Security Schemes. No. 22. Centre for Microfinance Working Paper Series, 2008. Ambrosius, Christian, Barbara Fritz, and Ursula Stiegler. "Remittances for Financial Access: Lessons from Latin American Microfinance." Development Policy Review 32, no. 6 (2014): 733-753. Shobana Vasudevan. “Suitability of Business Correspondent Model for Financial Inclusion in Slums in Mumbai. “ Indian Institute of Banking and Finance (2008): 425-469. Barman, Deepak, Himendu P. Mathur, and Vinita Kalra. "Role of microfinance interventions in financial inclusion: A comparative study of microfinance models." Vision: The Journal of Business Perspective 13, no. 3 (2009): 51-59. Ravichandran and Alkhathlan. "Financial Inclusion-A Path towards India's Future Economic Growth." Khalid, Financial Inclusion-A Path towards India's Future Economic Growth (March 4, 2009) (2009). Mahajan, Shrikrishna S., and Natha Kalel. "An Assessment of Potential Financial Inclusion of Slum Dwellers Through Business Correspondent Model."IUP Journal of Bank Management 12, no. 4 (2013). Sivasubramanian, M., and MI Saifil Ali. "Self Help Groups are Powerful Entrepreneurial Hub at the Grassroots level of India." Journal of Contemporary Research in Management 7, no. 4 (2013). Kumar, Neha, Akhil Mathur, and Siddhartha Lal. "Banking 101: Mobilizing Financial Inclusion in an Emerging India." Bell Labs Technical Journal 17, no. 4 (2013): 37-41. Kumar, R. Magesh, and C. Samuel Joseph. "Financial inclusion:--An overview of various business models in India." JIMS8M: The Journal of Indian Management & Strategy 19, no. 2 (2014): 30-37. Swamy, Vighneswara. "Financial Inclusion, Gender Dimension, and Economic Impact on Poor Households." World Development 56 (2014): 1-15.

Annexure Table I: Difficulties faced by BC models in connecting banked with unbanked S. No. Difficulties faced by BC models in connecting banked with unbanked 1 Lack of clarity on information from bank officials 2 Concern about frequent receipts and payments 3 Hesitation of customers to financial inclusion 4 Settlement of transacted accounts on the same day 5 Technological glitches Total Source: Primary Data

Respondents 1 2 5 9 13 30

Percent 3.33 6.67 16.67 30.00 43.33 100.00

Table II: One – Way ANOVA Demographic profile x Difficulties Sum of Squares cf Mean Square F Sig. Between Groups 26.170 4 6.543 12.394 0.000 Age Within Groups 13.197 25 0.528 Total 39.367 29 Between Groups 1.569 4 0.392 3.036 0.036 Gender Within Groups 3.231 25 0.129 Total 4.800 29 Between Groups 33.514 4 8.378 19.419 0.000 Educational Within Groups 10.786 25 0.431 Qualification Total 44.300 29 Between Groups 42.601 4 10.650 24.732 0.000 Occupation Within Groups 10.766 25 0.431 Total 53.367 29 Between Groups 24.923 4 6.231 24.816 0.000 Income Within Groups 6.277 25 0.251 Total 31.200 29 Source: Compilation of Primary Data; f – Frequency; % - Percent; cf - cumulative frequency

Table III: Demographic profile of the respondents Demographic Profile Age 22-35 36-45 46-55

9 11 6

30.00 36.67 20.00

30.00 66.67 86.67

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N. Sundaram et al., International Journal of Engineering, Business and Enterprise Applications, 11(1), December 2014-February 2015, pp. 40-44 56-65 2 6.67 93.33 66-75 2 6.67 100.0 Gender Male 24 80.00 80.00 Female 6 20.00 100.00 Educational qualification level Secondary 10 33.33 33.33 Higher Secondary 8 26.67 60.00 Diploma 7 23.33 83.33 Under Graduate 3 10.00 93.33 Post Graduate 2 6.67 100.00 Occupation Unemployed 4 13.33 13.33 Local Shopkeeper 7 23.33 36.67 Retired 2 6.67 43.33 Insurance Company agent 12 40.00 83.33 Farmer's Club member 5 16.67 100.00 Income Less than Rs. 5,000 6 20.00 20.00 Rs.5,001 to Rs.10,000 11 36.67 56.67 Rs.10,001 to Rs.15,000 9 30.00 86.67 Rs.15,001 to Rs. 25,000 3 10.00 96.67 Above Rs. 25,000 1 3.33 100.00 Total of each demographic profile 30 100.00 Source: Primary Data; f â&#x20AC;&#x201C; Frequency; % - Percent; cf - cumulative frequency

Table IV: Number of bank accounts opened through BCs in Katpadi and Vellore village blocks of Vellore district Population Number of Account (Villages) opened Elavambadi CB 3894 1177 (30.23) Marudavallipalayam CB 2145 1200 (55.94) Kilkothur IOB 3767 1128 (29.94) Ongapadi IOB 2032 1486 (73.13) Madayapattu IB 3527 1766 (50.07) Anaicut Melarasampattu IB 3468 1737 (50.09) Nemandapuram IB 2773 1369 (49.37) Palampattu IB 2047 586 (28.63) Peenjamandai IB 6140 3031 (49.36) Vannanthangal IB 2964 1075 (36.27) Tippasamudram SBI 2488 818 (32.88) Kammasamudram CB 2719 1250 (45.97) Kathalampattu SBI 3648 870 (23.85) Kaniyambadi Kil Arasampattu SBI 2605 459 (17.62) Nanjukondapuram SBI 2404 1474 (61.31) Palampakkam SBI 2130 2000 (93.90) Gugaiyanallur CB 2752 655 (23.80) Serkadu CB 2999 270 (9.00) Katpadi Karnampattu CB 2745 362 (13.19) Vanjur IOB 3175 2287 (72.03) Thandalamkrishnapuram UBI 2631 1924 (73.13) Panamadangi CBI 3430 1641 (47.84) Thirumani SB 3031 2950 (97.33) K. V. Kuppam Kilmuttukur UBI 3431 2174 (63.36) Velampattu UBI 2104 745 (35.41) Veppur UBI 3571 1603 (44.89) Pulimedu CORP 2920 1124 (38.49) Thellur CORP 6303 2146 (34.05) Vellore Kuppam IOB 2157 1497 (69.40) Anpundi SBI 3089 1938 (62.74) Source: Primary Data; Note: Figures in the parentheses represent percentage of accounts opened by population of villages

S. No.

Village blocks (More than 2000 unbanked population)

Villages

Bank

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Fault Detection, Protection and Monitoring of Induction Motor Using Zigbee 1

N.SOLAIYAMMAL, 2N.KANAGAPRIYA Embedded System Technologies, Department of Electrical and Electronics Engineering Nehru institute of engineering and technology, Anna University Nehru Gardens,T.M palayam Coimbatore-641105 India Abstract : Protection of an induction motor (IM) against possible problems, such as overvoltage, over current, overload, over temperature, and under voltage, occurring in the course of its operation is very important, because it is used intensively in industry as an actuator. IMs can be protected using some components, such as timers, contactors, voltage, and current relays. This method is known as the classical method that is very basic and involves mechanical dynamic parts. Moreover, the voltages, the currents, the speed, and the temperature values of the motor, and the problems occurred in the system, are monitored and warning messages are shown on screen. Motor Current Signature Analysis (MCSA) is being the most widely used method to identify faults in Induction Motors. Keywords: Induction motor (IM), Peripheral interface controller(PIC), Motor Current Signature Analysis (MCSA) I. Introduction The induction motors are most widely used motors in industrial, commercial and residential sectors because of enormous merits of these over other types of available electrical motors. The early detection of these deteriorating conditions in incipient phase and its remove correction is very necessary for the prevention of any external fault failure of induction motors reducing repair costs and motor outage time. Fault detection using analytical methods is not always possible because it requires a per feet knowledge of the motor model. The various types of faults have been considered. Three phase instantaneous voltages and currents are utilized in proposed approach. Simulated fault current and voltage data have been used for testing of trained network. II. Block diagram This project is mainly used to find and rectify the faults of three phase induction motor using PIC (peripheral interface controller) Microcontroller Fig. 1: Block diagram of fault detection and protection of induction motor using zigbee

In this project able to find the following various types of external fault : 1.Overload 2. Single,two,three phase over voltage (19-39V)

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3.Single ,two,three phase under voltage (19-39UV) 4. Unequal single,two phase angle displacement (19-A) 5. Unequal two phase angles displacement (29-A) 6. Single,two phase open circuit <(l9-29C) In this project to find the stator and rotor faults, some methods are followed. After finding faults should give protection against that fault is very important. To control and measure each voltage, current value here used control unit and voltage & current measurement unit. III. Induction Motor Tests A.

Experimental Setup and Simulink Model

The steady-state operating characteristics of a three-phase induction motor are often investigated using a perphase equivalent circuit as shown in Fig. In this circuit, and represent stator resistance and leakage reactance, respectively; and denote the rotor resistance and leakage reactance referred to the stator, respectively. B.

No-Load Test

The no-load test on an induction motor is conducted to measure the rotational losses of the motor and to determine some of its equivalent circuit parameters. In this test, a rated, balanced ac voltage at a rated frequency is applied to the stator while it is running at no load, and input power, voltage, and phase currents are measured at the no-load condition.The input block (terminal Tm) is the mechanical torque at the machineâ&#x20AC;&#x2122;s shaft. This torque is set to be zero to simulate the no-load condition. These measurements enable the approximate computation of the sum of the magnetizing reactance and the stator leakage reactance as follows.

Blocked-Rotor Test

The blocked-rotor test on an induction motor is performed to determine some of its equivalent circuit parameters. In this test, the rotor of the induction motor is blocked, and a reduced voltage is applied to the stator terminals so that the rated current flows through the stator windings. The input power, voltage, and current are measured. For some design-class induction motors, this test is conducted under a test frequency, usually less than the normal operating frequency so as to evaluate the rotor resistance appropriately. IV. Equivalent Circuit Parameters of Induction Motor Tested The equivalent circuit under blocked-rotor condition, the following expression achieves the desired approximation

Comparision of Equivalent Circuit Parameters

To illustrate the effectiveness of the proposed simulation models, one compares the equivalent circuit parameters determined by simulations with those obtained from hardware experiments.The resulting parameters are presented in Table I. Table 1: Simulation results of the induction motor tests for motor

No-load test BlockedRotor test

Ia (A) 9.220

Ib (A) 9.231

Ic (A) 9.226

Va (V) 121

Pa (W) 34.8

(VAR)

(rad/s)

1105

188.5

15.74

15.72

26.59

212.58

360.50

0.00

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V. Result and Analysis Protection of an induction motor (IM) against possible problems, such as overvoltage, over current, overload, over temperature, and under voltage .Here we used Alarm and reset to stop the motor at any failure. Fig. 3: Simulation results of motor

VI. Conclusion The proposed PLC-controlled protective relay deals with the most important types of these failures, which are summarized as the phase lost, the over/undercurrent, the over/under voltage, the unbalance of supply voltages, the overload, the unbalance of phase currents, the ground fault, and the excessive repeated starting. If any fault is observed during operation of the motor, the motor is stopped. When an undefined fault occurs, the motor stops without giving any description. In this case, the fault can be described and found by the operator. The test has been found successful in detecting the faults and in recovering them. VII. [1] [2] [3] [4] [5] [6] [7]

References

M. Peltola, “Slip of ac induction motors and how to minimize it,” ABBDrives Press Releases Techical Paper, ABB, New Berlin, 2003, pp. 1–7. I. Colak, H. Celik, ˙I. Sefa, and S. Demirbas, “On line protection system for induction motors,” Energy Convers. Manage., vol. 46, no. 17, pp. 2773–2786, 2005. A. Siddique, G. S. Yadava, and B. Singh, “A review of stator fault monitoringtechniques of induction motors,” IEEE Trans. Energy Convers.,vol. 20, no. 1, pp. 106–114, Mar. 2005. Y. Zhongming and W. Bin, “A review on induction motor online faultdiagnosis,” in 3rd Int. Power Electron. Motion Control Conf. (PIEMC2000), vol. 3, pp. 1353–1358. M. E. H. Benbouzid, “Bibliography on induction motors faults detectionand diagnosis,” IEEE Trans. Energy Convers., vol. 14, no. 4, pp. N. Tandon, G. S. Yadava, and K. M. Ramakrishna, “A comparison of somecondition monitoring techniques for the detection of defect in inductionmotor ball bearings,” Mech. Syst. Signal Process., vol. 21, no. 1, pp. 244–256, Jan. 2007. F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “AI techniques ininduction machines diagnosis including the speed ripple effect,” IEEETrans. Ind. Appl., vol. 34, no. 1, pp. 98–108, Jan./Feb. 1998. The Authoritative Dictionary of IEEE Standards Terms (IEEE 100) (seventh edition ed.). Piscataway, New Jersey: IEEE Press. 2000. ISBN 0-7381-2601-2

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net LITERATURE REVIEW ON FACTORS INFLUENCING DIVIDEND DECISIONS 1

P.G.Thirumagal, Assistant Professor 2Dr. S. Vasantha, Professor 2School of Management Studies, Vels University, PV Vaithiyalingam Road, Pallavaram, Chennai, Tamilnadu, INDIA. 3 S. Suresh, Assistant Professor, Department of Management Studies, Rajalakshmi Engineering College, Thandalam, Chennai, Tamilnadu, INDIA.

___________________________________________________________________________ Abstract: Dividend decisions determinants is one of the most controversial topic in the field of finance. Dividend policy as an instrument to communicate information on the present and future prospects of the firm and it has impact on the market value of the firm. The various factors affecting the dividend decisions of the firm are varying under different sectors in different circumstances. This study provides the theoretical empirical review of different factors influencing dividend payout. Major factors from different studies have been combined together to create a new model. This model has to be tested under different circumstances. Keywords: Dividend decisions, Factors, Firm size, Earnings, Profitability __________________________________________________________________________________________ I. Introduction One of the important area in finance is dividend decisions. The harder we look at the dividend picture the more it seems like a puzzle, with pieces that just do not fit together (Black 1976). Even Brealey and Myers (2005) said that dividend policy as one of the top ten most difficult unsolved problems in financial economics. In the developed markets, researchers give more importance to dividend than emerging markets. Since the development of joint stock companies, dividend policies gained significance in financial literature. A large number of financial and non-financial determinants of corporate dividend policy have been discussed in the work of Lintner (1956). II. Review of Literature In the past five decades, several theories were propounded for dividend. Theoretical and empirical results were contradictory to each other. Miller and Modigliani (1961) assumed the markets were perfect so, dividends were irrelevant and it had no influence on the stock price of the company. Many researchers provided empirical evidence that if the markets are imperfect, it affect the share price of the company. In bird in the hand theory, it was explained that investors preference towards dividend is more than retained earnings so that the firms should plan for huge payout ratio in order to maximize the share price. (Gordon, 1956; Lintner, 1956; Fisher, 1961; Walter, 1963; Brigham and Gordon, 1968). Gordon and Walter (1963) present the bird in the hand theory which says that investors always prefer cash in hand rather than a future promise of capital gain due to minimizing risk According to tax preference theory (Brennan, 1970; Elton and Gruber, 1970; Litzenberger and Ramaswamy, 1979; Litzenberger and Ramaswamy; Kalay, 1982; John and Williams, 1985; Poterba and Summers, 1984; Miller and Rock, 1985; Ambarish et al., 1987) dividends are taxed heavily than capital gain. Only when stocks are sold, capital gains are taxed but dividends are taxed directly. This made the investors to prefer for capital gain than dividend which in turn lead to low payout ratio for the companies. The agency theory of Jensen and Meckling (1976) is based on the conflict between managers and shareholder and the percentage of equity controlled by insider ownership should influence the dividend policy. Miller and Scholes (1978) find that the effect of tax preferences on clientele and conclude different tax rates on dividends and capital gain lead to different clientele. According to signalling theory, it was revealed that the information asymmetry between managers and outside shareholders allows managers to use dividends as a tool to signal private information about a firmâ&#x20AC;&#x2122;s performance to outsiders (Aharony and Swary, 1980; Asquith and Mullins, 1986; Kalay and Loewenstein, 1985; Healy and Palepu, 1988). Bhattacharya (1980) and John Williams (1985) dividends allay information asymmetric between managers and shareholders by delivering inside information of firm future prospects. Easterbrook (1984) gives further explanation regarding agency cost problem and says that there are two forms of agency costs; one is the cost monitoring and other is cost of risk aversion on the part of directors or managers. Transaction cost and residual theory indicates that firms incurring large transaction costs will be required to reduce dividend payouts to avoid the costs of external financing (Mueller, 1967; Higgins, 1972; Crutchley and Hansen, 1989; Alli et al., 1993; Holder et al., 1998). A different explanation, which received little consideration prior to the 1980s, relates dividend policy to the effect of agency costs (La Porta et al., 2000). Agency costs, in

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this case, are costs incurred in monitoring company management to prevent inappropriate behaviour. Large dividend payouts reduce internal cash flows, forcing managers to seek external financing, and thereby, making them liable to capital suppliers, thus, reducing agency costs (Rozeff, 1982; Easterbrook, 1984; Lloyd, 1985; Crutchley and Hansen, 1989; Dempsey and Laber, 1992; Alli et al., 1993; Moh’d et al., 1995; Glen et al., 1995; Holder et al., 1998; Saxena, 1999). Life Cycle Theory explanation given by the Lease et al. (2000) and Fama and French (2001) is that the firms should follow a life cycle and reflect management’s assessment of the importance of market imperfection and factors including taxes to equity holders, agency cost asymmetric information, floating cost and transaction costs Catering theory given by Baker and Wurgler (2004) suggest that the managers in order to give incentives to the investor according to their needs and wants and in this way cater the investors by paying smooth dividends when the investors put stock price premium on payers and by not paying when investors prefer non payers. Empirical Review Many researchers have analyzed different markets to identify the factors that influence the dividend policy of the companies. There are several literature on determinants of dividend policy is related to Lintner (1956) seminal work. Then the model is extended by the Fama and Babiak (1968). The following are the list of factors which influence the dividend decision of a firm. The empirical review have been segregated based on the impact of the factor in the review. Beta: Alli, Khan and Ramirez (1993) find that dividends do not convey information regarding a firm’s future cash flows. They report that at beta, firm’s capital expenditure and financial slack are inversely related to the dividend payout. Liquidity: Kanwal and Kapoor (2008) examine the dividend policies of companies in the information technology sector in India. They explore various factors such as profitability, cash flows, corporate tax, sales growth and growth opportunities that have an impact over the dividend policies of such companies. They report that only cash flows indicating liquidity and beta indicating risk are the foremost determinants. Thus over the years different strands of research have emerged in the area of dividend policy both in India and abroad Profitability: A number of factors have been identified in previous empirical studies to influence the dividend payout ratios of firms including profitability, risk, cash flow, agency cost, and growth (see Higgins, 1981; Rozeff, 1982; Lloyd et al., 1985; Pruitt and Gitman, 1991; Jensen et al., 1992; Alli et al., 1993; Collins et al., 1996; D’Souza, 1999). In Indian case Reddy (2006) show that the dividends paying firms are more profitable, large in size, and growing. The corporate tax or tax preference theory doesn’t appear to hold true in Indian context The financial literature documents that a firm’s profitability is a significant and explanatory variable of dividend policy (Jensen et al., 1992; Han et al., 1999; Fama and French, 2000). However, there is a significant difference between dividend policies in developed and developing countries. This difference has been reported by Glen et al. (1995), showing that dividend payout rates in developing countries are approximately two-thirds of those in developed countries. Moreover, emerging market corporations do not follow a stable dividend policy; dividend payment for a given year is based on firm profitability for the same year. Profitability (PROF) is the ratio of net profits to the amount of money that shareholders have put into the company. ROE has been used in several studies as a proxy for firm profitability (Aivazian et al., 2003, ap Gwilym et al., 2004.) and is calculated as follows: PROF = (Net profit/shareholder’s equity)*100. This creates the assumption that the dividend ratio per year is based on firm earnings for the same year. Amidu and Abor (2006) find dividend payout policy decision of listed firms in Ghana Stock Exchange is influenced by profitability, cash flow position, and growth scenario and investment opportunities of the firms. Profits have long been regarded as the primary indicator of a firm’s capacity to pay dividends. Pruitt and Gitman (1991), in their study report that, current and past years’ profits are important factors in influencing dividend payments. Al Kuwari (2009) too found a significantly positive relationship between the two. Bose and Husain (2011) examined the determinants of Indian dividend Policy in case of five sectors i.e. Software, Finance, Steel, Electrical machinery and Pharmaceutical. The results disclosed that majority of firms increased their dividend payment due to increase in profits and decrease their dividend payment due to decrease in profits. The Lintner‟s model failed to explain the asymmetric dividend policy behavior of Indian firms. Naceur et al. (2006) find that the high profitable firms with more stable earnings can manage the larger cash flows and because of this they pay larger dividends. Moreover, the firms with fast growth distribute the larger dividends so as attract to investors. Appannan and Sim (2011) concluded that the Profit-after-tax that has the strongest relationship with dividend per share. The debt-to- equity ratio and past dividend per share were the important determinants of dividend payment. Earnings: The empirical analysis by Adaoglu (2000) shows that the firms listed on Istanbul Stock Exchange follow unstable cash dividend policy and the main factor for determining the amount of dividend is earning of the firms. Eriotis (2005) reports that the Greek firms distribute dividend each year according to their target payout ratio, which is determined by distributed earnings and size of these firms. Baker et al. (1985) also find that a major

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determinant of dividend payment was the anticipated level of future earnings. Eriotis (2005) reports that the Greek firms distribute dividend each year according to their target payout ratio, which is determined by distributed earnings and size of these firms. Rao and Sarma (1971) conducted an empirical study to enquire into the determinants of dividends of public and private limited enterprises. Their efforts revealed that the basic Lintner model was adequate for explaining the dividend behaviour in the case of majority of the industries such as coal mining, sugar, jute textiles, chemicals and cement industries. Bhat and Pandey (1994) supported the Linter‟s findings and revealed that Indian managers maintained an uninterrupted record of dividend payments and also try to avoid abrupt changes in their dividend policies. Musa (2009) analyzed the dividend behavior by using Parsimonious multiple regression model developed by Musa (2005). The empirical results revealed that the five metric variables have significant impact and nonmetric variables have insignificant impact on the dividend policy of Nigerian firms. However, three of the variables-current earnings, previous year dividend and cash flow have been found to be significant in the model. Okpara and Godwin Chigozie (2010) aimed at investigating the factors determining dividend pay-out policy in Nigeria. The results showed that three factors-earnings, current ratio and last year‟s dividends impact significantly on the dividend payout and dividend yield in Nigeria. Raj kumar & Pawan kumar jha (2012) analysed the determinants of equity dividend in Indian IT sector which revealed that net profit after tax, cash flow and the amount of depreciation charged have significant impact on the equity dividend. Myers (2004) finds strong support for earnings, profit margin, institutional ownership and debt-equity ratio on the dividend decision. Kevin (1992) analyzes the dividend payment behavior of 650 Indian companies during eptember 1983 to August 1984 and finds that profitability and earnings of the firms are the two foremost actors determining dividends. Firm size: There is highly significant association between the decision to pay dividends and size of the firm, profitability, growth, leverage, cash balance and history of dividends. (DeAngelo et al. (2004). Research by Lloyd, Jahera, and Page (1985), and Vogt (1994) indicates that firm size plays a role in explaining the dividend-payout ratio of firms. They find that larger firms tend to be more mature and thus have easier access to the capital markets, which reduces their dependence on internally generated funding and allows for higher dividend-payout ratios. The hypothesized relationship between firm size and dividend-payout ratios is positive. Firm size (SIZE) is measured as a natural logarithm of total assets. This is due to the fact that large firms will pay large dividends to reduce agency costs (Ghosh and Woolridge, 1988; Eddy and Seifert, 1988; Redding, 1997). Eddy and Seifert (1988), Jensen et al. (1992), Redding (1997), and Fama and French (2000) indicated that large firms distribute a higher amount of their net profits as cash dividends, than do small firms. Several studies have tested the impact of firm size on the dividend-agency relationship. Lloyd et al. (1985) were among the first to modify Rozeff's model by adding “firm size” as an additional variable. They considered it an important explanatory variable, as large companies are more likely to increase their dividend payouts to decrease agency costs. Their findings support Jensen and Meckling’s (1976) argument, that agency costs are associated with firm size. Holder et al. (1998) revealed that larger firms have better access to capital markets and find it easier to raise funds at lower costs, allowing them to pay higher dividends to shareholders. This demonstrates a positive association between dividend payouts and firm size.The positive relationship between dividend payout policy and firm size is also supported by a growing number of other studies (, Eddy and Seifert, 1988; Jensen et al., 1992; Redding, 1997; Holder et al., 1998; Fama and French, 2000; Manos, 2002; Mollah 2002; Travlos et al., 2002; Al-Malkawi, 2007). Growth: In investigating the determinants of dividend policy of Tunisian stock Exchange Naceur et al. (2006) find that the high profitable firms with more stable earnings can manage the larger cash flows and because of this they pay larger dividends. Moreover, the firms with fast growth distribute the larger dividends so as attract to investors. D’Souza (1999) however shows a positive but insignificant relationship in the case of growth Higgins (1972) shows that payout ratio is negatively related to a firm’s need for funds to finance growth opportunities. Rozeff (1982), Lloyd et al. (1985), and Collins et al. (1996) all show a significantly negative relationship between historical sales growth and dividend payout. Higgins (1981) indicates a direct link between growth and financing needs: rapidly growing firms have external financing needs because working capital needs normally exceed the incremental cash flows from new sales. Growth rate is measured as the growth rate of sales ( Rozeff, 1982; Lloyd et al., 1985; Jensen et al., 1992; Alli et al., 1993; Moh’d et al., 1995; Holder et al., 1998; Chen etal., 1999; Sexsena, 1999; Manos, 2002; Travlos, 2002). Thus, growth rate has been identified in this study by Annual Sales Growth. Overall literature portrays a negative as well as a positive relationship between the dependent variable and sales growth. Growth was inversely related to dividend payout and was found to be significant .The main conclusion were that dividend decisions are better explained by Lintner’s model with current profit and lagged dividend as explanatory variable

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Corporate tax: Omet (2004) comes to the same conclusion in case of firms listed on Amman Securities Market and further the tax imposition on dividend does not have the significant impact on the dividend behaviour of the listed firms. Corporate tax has been taken as an explanatory variable with the expected negative association with dividend payout by Anil and Kapoor (2008) in their study on IT sector of India and found it to be insignificant, consistent with Reddy’s conclusion. It would be interesting to note the effect of corporate tax in Pakistan Mahira Rafique (2012) empirically tested the determinants of dividend payout of non financial firms listed in KSE100 index. The results shown that corporate tax and firm’s size have significant relationship with dividend payout. Narasimhan and Asha (1997) look at the changes in dividend tax regime proposed in the Indian Union Budget of 1997-98 and analyze the impact of dividend tax on a firm’s dividend decision. Financial leverage: A growing number of studies have found that the level of financial leverage negatively affects dividend policy (Jensen et al., 1992; Agrawal and Jayaraman, 1994; Crutchley and Hansen, 1989; Faccio et al., 2001; Gugler and Yurtoglu, 2003; Al-Malkawi, 2005). Their studies inferred that highly levered firms look forward to maintaining their internal cash flow to fulfil duties, instead of distributing available cash to shareholders and protect their creditors. However, Mollah et al. (2001) examined an emerging market and found a direct relationship between financial leverage and debt-burden level that increases transaction costs. Thus, firms with high leverage ratios have high transaction costs, and are in a weak position to pay higher dividends to avoid the cost of external financing. To analyze the extent to which debt can affect dividend payouts, this study employed the financial leverage ratio, or ratio of liabilities (total short-term and longterm debt) to total shareholders’ equity. Al Kuwari (2009) too found a significantly negative relationship between the two. The proxy used for financial leverage is Debt to Equity Ratio as used in all these studies. Asif, Rasool and Kamal (2011) examined the relationship between dividend policy and financial leverage of listed companies of Pakistan during the period of 2002-2008 by using extended Lintners (1956) model. The results showed the negative relationship between dividend payout and financial leverage while dividend yield showed the positive relationship between dividend yield and dividend per share. Amithabh Gupta & Charu Banga (2010) analysed companies from BSE 500 index and found that leverage and liquidity were the factors which influence the dividend decisions of the companies. Cash flow: Alli et al. (1993) reveal that dividend payments depend more on cash flows, which reflect the company’s ability to pay dividends, than on current earnings, which are less heavily influenced by accounting practices. They claim current earnings do not really reflect the firm’s ability to pay dividends. The proxy used for earnings is the ratio of company’s operating earnings before interest and tax (EBIT) to total assets. Brittain (1966) studied the tax structure and corporate dividend policy over a period of 1919-1960. The results indicated that the capacity of a firm to pay dividends has been better explained in terms of cash flows as a variable, i.e., profits after taxes plus depreciation as against the Lintner‟s profits net of taxes, as it reflected true earnings. Capital Expenditure: There is a negative relationship between agency cost and market risk with dividend payout but not between dividend payout and investment opportunities (D’Souza , 1999) History of dividends: Naeem and Nasr (2007) observed the determinants and trends of dividend policies. Results of their study show that Pakistani companies are either reluctant to pay dividends or pay very low amount as dividends and their current dividend decisions depend on previous year dividends and Profitability Ratio. Ownership structure: Husam et.al.(2007) examined the determinants of corporate dividend policy in Jordan using Tobit specifications. The results suggested that the proportion of stocks held by insiders and state ownership significantly affected the amount of dividends paid. Size, age, and profitability of the firm seemed to be determinant factors of corporate dividend policy in Jordan Kania and Bacon (2005) find that variables such as sales growth, expansion and insider ownership have a negative impact on dividend decision but institutional ownership has an inverse relation with dividend payout, which is contrary to the existing literature Other factors include financial slack, age of the firm, no, of common stockholders, agency cost, cash balance and risk. III. Determinants of Dividend decisions There are many factors which influence the dividend decision of the companies. Based on theoretical and empirical review, the following factors have been identified as significant. These factors have been used for different sectors of the companies across different countries. It has to be tested under Indian conditions. IV Conclusion Many researchers have done the study on factors influencing dividend decisions under different circumstances. Some of the researchers have found that earnings and firm size are the major factors which influence the dividend decisions, while some others found that ownership structure, leverage, cash flow, corporate tax were

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the major factors. Others have found that beta, corporate tax, investment opportunities were not influencing the decisions. Major factors used in different studies were combined together to find its impact under Indian circumstances. Age of the Firm

Beta

Financial Slack

Profitability

Earnings

No of Common Stockholders

Financial Leverage

Liquidity

Risk

Dividend Payout Ratio Firm Size

Cash flow

Growth

Capital Expenditure

Cash Balance Ownership structure

History of dividends

Corporate Tax

Agency cost

Source: (Author’s Compilation) References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]

Allen, F., & Michaely, R. 2003, ‘Payout Policy. Handbook of Economic and Finance’ pp. 37–45. Angelo, D., Harry, DeAngelo, L., Douglas, & Skinner. 2004, ‘Are Dividends Disappearing? Dividend Concentration and the Consolidation of Earnings’, Journal of Financial Economics, pp. 425–456 Baker, K., Saadi, S., & Gandhi, D. 2007, ‘The Perception of Dividends by Canadian Managers: New Survey evidence’, International Journal of Managerial Finance, Vol. 13 No. 1, pp. 70–91. Baker, M., & Jeffrey, W. 2004, ‘A Catering Theory of Dividends’, Journal of Finance, pp. 1125–1165 Brealey, R., & Myers, S. 2005, Principles of Corporate Finance. London: McGraw-Hill, pp. 155–167. Asif, Rasool and Kamal, “Impact of Financial Leverage on Dividend Policy: Empirical Evidence from Karachi Stock Exchange-Listed Companies”, African Journal of Business Management, Vol. V (4), 2011, 1312-1324. Brittain, J. A., “The Tax Structure and Corporate Dividend Policy”, American Economic Review, (Papers and Proceedings), Vol. XXXXXIV, (3), 1966. Bhattacharya, Sudipto, “Imperfect Information, Dividend Policy, and “the Bird in the Hand” Fallacy, Bell Journal of Economics, 10, 1979, 259-270. Bhat, R, and Pandey I. M., “Dividend decision: a study of manager‟s perceptions”, Decision, 21, 1994, 1- 2. Bose, S. and Husain, Z., “Asymmetric Dividend Policy of Indian Firms: An Econometric Analysis”, International Journal of Applied Economics and Finance, Vol. V (3), 2011. Darling, P.G., “The Influence of Expectations and Liquidity on Dividend Policy”, Journal of Political Economy, 1957, 209-224. Eriotis, Nikolaos, “The Effect of Distribution earnings and Size of the Firm to its Dividend Policy”, International & Economics, Vol. IV (167), 2005. Fama, E.F. and Babilak, H., “Dividend policy: An empirical analysis”, Journal of American Statistical Association, 63, 1968, 1132-1161. Husam – Aldin Nizar and Al-Malkawi, “Determinants of Corporate Dividend Policy in Jordon: An Application of the Tobit Model”, Journal of Economic and Administrative Sciences Vol. XXIII (2), 2007, 44-70. Lintner, John V., “Distribution of Incomes of Corporations among Dividends, Retained Earnings, and Taxes”, American Economic Review, Vol. XXXXVI (2), 1956, 97-133. Musa, I., “The Dividend Policy of firms quoted on the Nigerian Stock Exchange: An Empirical Analysis”, African Journal of Business Management, Vol. III (10), 2009, 555-566. Naceur, S.B., Goaied, M and Belanes, A., “In the Determinants and Dynamics of Dividend Policy”, International Review of Finance, 6 (1-2), 2006, 1-23. Naeem, S., and Nasr, M., “Dividend Policy of Pakistani Firms: Trends and Determinants”, International Review of Business Research Papers, Vol. III (3), 2007, 242-254. Okpara and Godwin Chigozie, “A Diagnosis of the Determinants of Dividend PayOut Policy in Nigeria: A Factor Analytical Approach”, American Journal of Scientific Research, Issue 8, 2010, 57-67 Rao, G.N. and Sarma (Y.S.R.), “Dividends and retained earnings of public and Pvt. Ltd. Enterprises in India: 1955-56 to 196566: An econometric analysis”, RBI Bulletin, 25, June 1971, Sexena A.K., “Determinants of Dividend Payout Policy: Regulated versus Unregulated Firms”, 1999, www.westiga.edu Sujata Kapoor, “Determinants of Dividend Payout Ratios- A Study of Indian Information Technology sector”, International Research Journal of Finance and Economics, 15, 2008.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Metrics and Performance Measurement of Banks J. Priyankha M. Phil. Research Scholar, School of Management Studies, Vels University, Chennai, Tamil Nadu, INDIA. Dr .S .Vasantha Professor, School of Management Studies, Vels University, Chennai, Tamil Nadu, INDIA. Abstract: The growth of banks is not possible unless there is customer’s satisfaction or maybe the net assets held by these banks, efficiency of their management or the networks of each bank both in private as well as the public sector bank. This paper is designed to outline and discuss the most performance, and efficiencyrelated metrics for measuring the performance of the banks. Magretta and Stone (2002) suggests that metrics and performance measurement are the critical elements in translating an organization’s mission, or strategy, into reality Rajan (2005) argues, evaluating the true nature of bank performance is a very complicated task, since it requires disentangling the part of the performance which is the result of a genuine value creation from the part which is the result of higher (and not easily observable) risks. Indeed, higher returns can always be obtained by taking more risks, and if risks are hidden or underestimated, risk-taking may seem value-enhancing as long as risks have not materialised. However, the financial crisis revealed the real risks taken by the banks through the huge losses borne by their shareholders. Key words: Financial performance, Banking, Ratio analysis, Metrics

I. Introduction According Gran Josh “Banking is a business of accepting deposit and lending money. It is carried out by financial intermediaries, which performs the functions of safe guarding the deposits providing loans to the public. Banks have control over the circulation of money which is important in the economic development of nation. (Spathis and Doumpos 2002) The report used multi criteria methodology for classifying Greek banks and to know the profit efficiency in large and small banks. (Duncan and Elliott 2004) They showed the financial performance is related to the relationship between the output of the system and input of the production. (Sura 2006) used dividend as a notable device by the banks. (Chaudhary and Sharma 2011)They have made the comparative analysis of public and private banks and have come to the conclusions that the cost has been reduced due to modern technology. (Das and Drine 2011) (Michael Aldrich 20th august 1941- 19th may 2014) was responsible for Telebanking and Teleworking. The banking sectors have promoted online transactions on a large scale. All online transactions can be done with a debit or credit cards. The outcome of the economic reforms which is visible now in India started in early nineties. Banking industry in India is constantly growing at a faster rate .There has been noticeable changes in transactions through ATMs, internet, mobile banking etc. After the passing of banking law bills by the Indian parliament in 2012(Amendment) the banking industry has changed significantly. The Reserve Bank of India is allowed to issue new licenses for bigger number of banking in the country. The reserve Bank of India has right to spot bad loans and take actions or check the borrowers. II. Financial performance Financial performance is a process of generating revenue with the help of the assets that is available in the bank at the preliminary stage. It can also be stated as the level of performance of the banks in a particular period which is being stated as the profit incurred by the bank. Financial performance applies to all the work performed by the banks within the period of time and exhibit the success or profit. It is the financial activity that is being performed in the institution. This can also be used to compare with other bank performances. With the help of the financial position that is being exhibited the customers are the managers can find out the performance of the banks over a period of time or for a given period. III. Review of literature (Avkiran, 1995) says financial performance of banks and other financial institution is the combination of financial ratio analysis, benchmarking, measuring performance against budget or mix of these methodologies Avkiran (1995) and Dietrich (1996) Different aspect of the DuPont financial ratios appears to be applicable to the banks and other financial institutions. Financial Markets Department (2000) affirmed that ratio analysis is a reflection of the true state of affairs of the performance of any business. Notwithstanding the usefulness of financial ratio analysis in providing useful insight to an entities performance it does have some important limitations as an analytical tool in bank performance analysis. Aarma et al (2003) indicated that banks

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performance analysis is an important issue in the conditions of transition economies due to the key role played by the financial sector in a successful transition. An array of performance indicators is necessary to expose the different aspects of the performance of a bank as in Gibson and Cassar (2005). The concept of prudent banking is normally built around these indicators. Financial analysis is crucial in estimating the effect of any sector reforms or institutional restructuring. Using data for Taiwan Province of China, Lin, Penm, Garg, and Chang (2005) study the direct effects of capital regulations and capital requirements. More specifically, they study three areas: (i) the relation between capital adequacy and the bank insolvency risk index, (ii) the relation between capital adequacy and financial performance, and (iii) the interaction and relationship between the insolvency risk of banks and financial performance. Chawla, A.S.(2006) analysed the trends in the profitability of public sector and private sector banks . The performance of new sectors banks was made by Sanjay J. Bhayani (2006) on a comparative study by using the CAMEL model. (CAMEL stands for capital adequacy, asset quality management efficiency, earnings performance and liquidity ).The study covered 4 leading private sector banks- ICICI, HDFC, UTI and IDBI for a period of 5 years from 2000-01 to 2004-05.It was found that the aggregate performance of IDBI Bank was best among all the banks, followed by UTI .(Nimalathasan, 2008) compares financial performance of banking sector by using CAMELS rating system The performance of Malaysian Islamic bank carried out by using financial ratios(Samad and Hassan). EVA (Economic Value Added) is modern financial measurement tool that determines if a business is earning more than its true cost of capital (Gabriela et al, 2009).The performance of Malaysian Islamic bank carried out by using financial ratios(Samad and Hassan).The south African commercial banks performance measured by financial ratios analysis(kumbiari and Webb,2010). Performance of selected Indian commercial banks has done by growth in asset, profit, revenue, investment and deposit (Jaladhar, Anchula and Achari,2011) .While analyzing performance of AXIS bank in terms of capital adequacy ratios and correlation analysis is used(Shrivastava et al, 2011). The analysis includes CAMELS rating and multivariate regression analysis for comparing financial performance commercial banks (Jha and Hui, 2012). Khrawish (2011) explained that ROE is the ratio of Net Income after Taxes divided by Total Equity Capital. It represents the rate of return earned on the funds invested in the bank by its stockholders. ROE reflects how effectively a bank management is using shareholders’ funds. Thus, it can be deduced from the above statement that the better the ROE the more effective the management in utilizing the shareholders capital. Total assets or total net assets are used to describe a funds size. If the assets rise, the number of appropriate new stock prospects shrinks and transaction costs increases. If the assets size is high there is high scope for more investment and low the assets size which indicates otherwise. The financial performance of commercial bank measured in terms of capital adequacy and methodology used as ordinary least square method (Onaolopo and olufemi 2012) Jamal and Shariff (2012) attempted on dividend policy and to pay cash dividend implemented by US commercial banks as a possible alternative choice for dividend seeking investors. Most of the banks paid dividend at increasing rate and few banks have stopped. The study also indicated that the main predictor variables in predicting cash dividend are total assets, return on equity and equity to liability. ROE is a financial ratio that refers to how much profit a company earned compared to the total amount of shareholder equity invested or found on the balance sheet. ROE is what the shareholders look in return for their investment. A business that has a high return on equity is more likely to be one that is capable of generating cash internally. Thus, the higher the ROE the better the company is in terms of profit generation. IV. Metrics of bank in financial performance Capital adequacy ratio: A measure of a bank's capital. It is expressed as a percentage of a bank's risk weighted credit exposures. Capital Adequacy ratio = Tier1capital +Tier2 capital Risk – weighted Exposures Also known as "Capital to Risk Weighted Assets Ratio (CRAR)." Capital Adequacy Ratio – CAR: This ratio is used to protect depositors and promote the stability and efficiency of financial systems around the world. Two types of capital are measured: tier one capital, which can absorb losses without a bank being required to cease trading, and tier two capital, which can absorb losses in the event of a winding-up and so provides a lesser degree of protection to depositors. (Sat and Venkatesh) (2010) highlighting the importance of CAR for Islamic bank as a measures of capital (as defined by Basel) the banks have to maintain in relation to their total risk weighted assets (RWA), including offbalance sheet exposure. This is considered as the most important ratio for banks and the buffer against heavy losses that could question the very existence of a bank. As banks are heavily leveraged institutions, they must maintain sufficient capital to cover their RWA. This ratio is more meaningful during an economic crisis as this ratio acts as a predictor of bank failure .To reduce this probability, a bank may strengthen its capital over time. Further as per (Santmero and Watson) (1977), the lower the capital the higher the probability of failure. This ratio gauges the safety and soundness of a bank (Estrella, Park and Persitiani, 2000) and as such a comfortable CAR, especially during a crisis, adds confidence to the stability and soundness of a bank.

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Economic value added: A measure of a company's financial performance based on the residual wealth calculated by deducting cost of capital from its operating profit (adjusted for taxes on a cash basis). (Also referred to as "economic profit".) The formula for calculating EVA is as follows: Net Operating Profit after Taxes (NOPAT) - (Capital * Cost of Capital) This measure was devised by Stern Stewart & Co. Economic value added attempts to capture the true economic profit of a company. Bank is said to run in profit if its gets profit that is greater than cost of capital otherwise it operates at loss. EVA is related to a performance metrics offered by the other consultants. (Stern Stewart) calculates MVA(Market Value-Added) by adding the capital taken in by a company during its lifetime through securities offerings, loans, and retained earnings, [making] some EVA-like adjustments (such as capitalising and amortising R&D expenditures), and subtracting the total from the current value of the stock and debt (Meyers, 1996, ) Return on equity: Return on Equity (ROE) = Net Profit / Owners Equity ROE measures the rate of return on shareholders’ equity. It measures a firm’s efficiency in generating profit from every unit of shareholders’ equity. ROE is the return that the shareholders get for their investment. (Khrawish 2011) says that the ROE is the ratio of Net Income after Taxes divided by Total Equity Capital. It represents the rate of return earned on the funds invested in the bank by its stockholders. ROE reflects how effectively a bank management is using shareholders ‘funds. Thus, it can be deduced from the above statement that the better the ROE the more effective the management in utilizing the shareholders capital. Return on asset: (Khrawish, 2011) ROA is also another major ratio that indicates the profitability of a bank. It is a ratio of Income to its total asset. It measures the ability of the bank management to generate income by utilizing company assets at their disposal. In other words, it shows how efficiently the resources of the company are used to generate the income. It further indicates the efficiency of the management of a company in generating net income from all the resources of the institution (Khrawish, 2011),(Wen 2010), state that a higher ROA shows that the company is more efficient in using its resources. Return on Assets (ROA) = Net Profit / Total Assets ROA gives an idea as how efficiently management uses its assets to generate earnings. Higher return on assets reflects good utilization of available assets and lower return indicates otherwise. DuPont Analysis: A method of performance measurement that was started by the DuPont Corporation in the 1920s. With this method, assets are measured at their gross book value rather than at net book value in order to produce a higher return on equity (ROE). It is also known as "DuPont identity". DuPont analysis tells us that ROE is affected by three things: (1) Operating efficiency, which is measured by profit margin (2) Asset use efficiency, which is measured by total asset turnover(3) Financial leverage, which is measured by the equity multiplier ROE = Profit Margin (Profit/Sales) * Total Asset Turnover (Sales/Assets) * EquityMultiplier (Assets/Equity) It is believed that measuring assets at gross book value removes the incentive to avoid investing in new assets. New asset avoidance can occur as financial accounting depreciation methods artificially produce lower ROEs in the initial years that an asset is placed into service. If ROE is unsatisfactory, the DuPont analysis helps locate the part of the business that is underperforming. (Saunders 2000) is a model of financial analysis for financial institutions based on the DuPont system of financial analysis return on equity model. The return on equity model disaggregates performance into three components: net profit margin, total asset turnover, and the equity multiplier. The profit margin allows the financial analyst to evaluate the income statement and the components of the income statement. Total asset turnover allows the financial analyst to evaluate the left-hand side of the balance sheet: assets. The equity multiplier allows the financial analyst to evaluate the right-hand side of the balance sheet: liabilities and owners equity. The financial analysis of Arab bank which was based on the DuPont system and was presented in (Saunders 2000).The bank return on equity is decomposed into net profit margin, total asset turnover and the equity multiplier. This model is applied to Arab Bank of Jordan which is one of the largest banks in Jordan. The DuPont system of financial analysis shows the performance of The Arab Bank over the years from 2000-2009 and the impact of the world financial crisis that hit the region in the recent years. Though there was a negative impact of the recent financial crisis on most banks of the world but this impact hit slightly the performance of Arab Bank of Jordan. Risk-Adjusted Return on Capital – RAROC RAROC was popularized by Bankers Trust in the 1980s as an adjustment to simple return on capital (ROC). Risk-adjusted return on capital (RAROC) gives decision makers the ability to compare the returns on several different projects with varying risk levels. (Smithson, Brannan, Mengle & Zmiewski., 2003,) Return in riskadjusted performance is measured either by absolute returns or by relative returns (i.e. excess returns), This has given rise to the development of a considerable number of alternative risk-adjusted performance measures.

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(Weisman, 2002). As investment funds is an important investment category investors needed an effective tool to evaluate the respective performance of the various funds compared to the risk taken by the fund managers to choose the right option for capital allocation. (Zimmermann & Wegmann, 2003,) Financial institutions requires to hold a certain amount of equity as a cushion against unexpected losses for each risky position taken. As a result financial institutions have a great interest in efficiently allocating capital not only according to the resulting return but also to the risk shouldered and therefore, banks more and more turn to concepts based on risk-adjusted performance measures like Risk-adjusted Return on Capital (RAROC) when evaluating their business activities. Net Interest Margin = (Investment Returns- Interest Expenses) Average Earning Assets A performance metric that examines how successful a firm's investment decisions are compared to its debt situations. A negative value denotes that the firm did not make an optimal decision, because interest expenses were greater than the amount of returns generated by investments.(Gul etal., 2011) The NIM variable is defined as the net interest income divided by total earnings assets. It is a measure of the difference between the interest income generated by banks and the amount of interest paid out to their lenders (for example, deposits), relative to the amount of their (interest earning) assets. It is usually expressed as a percentage of what the financial institution earns on loans in a specific time period and other assets minus the interest paid on borrowed funds divided by the average amount of the assets on which it earned income in that time period (the average earning assets). Net interest margin measures the gap between the interest income the bank receives on loans and securities and interest cost of its borrowed funds. (Khrawish, 2011) The higher the net interest margin, the higher the bank's profit and the more stable the bank is. Thus, it is one of the key measures of bank profitability. CAMEL (Capital Adequacy, Asset Quality, Management Efficiency, Earnings Ability and Liquidity) (Dang, 2011)Says the internal factors which influenced the profitability of the banks include capital size, size of deposit liabilities, size and composition of credit portfolio, interest rate policy, labour productivity, and state of information technology, risk level, management quality, bank size, ownership. Capital Adequacy: (Athanasoglou et al. 2005) said that capital is one of the own fund that is available to support the bank business and act as a buffer at the time of adverse situation. (Diamond, 2000) The greater the bank capital the chances are brighter to reduce the distress Capital adequacy is the level of capital required by the banks to enable them withstand the risks such as credit, market and operational risks they are exposed to in order to absorb the potential loses and protect the bank's debtors. (Dang (2011), the adequacy of capital is judged on the basis of capital adequacy ratio (CAR). Capital adequacy ratio shows the internal strength of the bank to withstand losses during crisis. Capital adequacy ratio is directly proportional to the resilience of the bank to crisis situations. It has also a direct effect on the profitability of banks by determining its expansion to risky but profitable ventures or areas (Sangmi and Nazir, 2010). Asset Quality: (Athanasoglou et al., 2005) The bank's asset is another bank specific variable that affects the profitability of a bank. The bank asset includes among others current asset, credit portfolio, fixed asset, and other investments. Loan is the major asset of commercial banks from which they generate income. (Dang, 2011) The quality of loan portfolio determines the profitability of banks. The loan portfolio quality has a direct bearing on bank profitability. The highest risk facing a bank is the losses derived from delinquent loans. (Sangmi and Nazir, 2010)Thus, nonperforming loan ratios are the best proxies for asset quality. It is the major concern of all commercial banks to keep the amount of nonperforming loans to low level. This is so because high nonperforming loan affects the profitability of the bank. Thus, low nonperforming loans to total loans shows that the good health of the portfolio a bank. The lower the ratio the better the bank performing. Management Efficiency: (Athanasoglou et al. 2005) Management Efficiency is one of the key internal factors that determine the bank profitability. It is represented by different financial ratios like total asset growth, loan growth rate and earnings growth rate. Operational efficiency in managing the operating expenses is another dimension for management quality. The capability of the management to deploy its resources efficiently, income maximization, reducing operating costs can be measured by financial ratios. One of this ratios used to measure management quality is higher the operating profits to total income (revenue) the more the efficient management is in terms of operational efficiency and income generation. The ratio of operating expenses to total asset is expected to be negatively associated with profitability. Management quality in this regard, determines the level of operating expenses and in turn affects profitability. Liquidity Management: Dang (2011) Adequate level of liquidity is positively related with bank profitability. It is another factor that determines the level of bank performance. Mainly liquidity has to fulfil obligation of the depositors. The most common financial ratios that reflect the liquidity position of a bank according to the above author are customer deposit to total asset and total loan to customer deposits. Ilhomovich (2009) used cash to deposit ratio to

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measure the liquidity level of banks in Malaysia. (Said and Tumin, 2011) The study conducted in China and Malaysia found that liquidity level of banks has no relationship with the performances of banks V. CONCLUSION Banks are taking effective steps to improve their performance measurement and capabilities in the current competitive environment but measuring the performance of banks is hard and that metrics differ strongly in quality. This paper discussed various performance metrics but these measures are subjected to a predictive validity test. Though the various reforms have produced favourable effects on the banks which has taken place due to the various transformation taking place in all the banks .The profitability in the banks is measured by using these various model and metrics such as Camel, Risk-Adjusted Return on Capital, Net Interest Margin (NIM), Capital adequacy ratio and Economic value added, to perform in banks.. References [1] [2] [3] [4]

[5] [6] [7] [8] [9] [10]

[11] [12] [13]

Anitha. K, Saranya. J, Dr. S. Vasantha, An Exploratory study of Usage of Technology in Banking Sector, International Journal on Innovative Research in Science, Engineering and Technology. Dr. Ahmed Arif Almazari, Financial Performance Analysis of the Jordanian ArabBank by Using the DuPont System of Financial Analysis, International Journal of Economics and Finance Vol. 4, No. 4; April 2012. Gilbert Sebe-Yeboah, Charles, European Journal of Accounting Auditing and Finance Research Vol.2, No.1, pp.1-23, March 2014, A critical analysis of financial performance of agricultural development bank (adb, Ghana). Md. Abdullah Al Mamun, Performance Evaluation of Prime Bank Limited in Terms ofCapital Adequacy, Global Journal of Management and Business Research Finance, Volume 13, Issue 9, Version 1.0, Year 2013, International Research JournalPublisher: Global Journals Inc. (USA)Online ISSN: 2249-4588 & Print ISSN: 0975-5853. Prof. Dr. Karl Frauendorfer University of St. Gallen (HSG), St. Gallen, Switzerland St. Gallen, May 2010, Risk-Adjusted Performance Measurement â&#x20AC;&#x201C; State of the Art Referee. Rajan, R., 2005, Has financial development made the world riskier? Paper presented at the Federal Reserve Bank of Kansas City Economic Symposium at Jackson Hole. Ramachandran Azhagaiah Sandanam Gejalakshmi, Financial Performance of Private Sector and Public Sector Banks in India: An Empirical Analysis, International Center for Business Research, Volume 1, Dec. 2012. Saranya. J,Anitha. K , Dr. S.Vasantha, An Empirical Study on Role of ICT in Banking Sector, International Journal of Multidisciplinary and Current Research. Sajid Khan & Zohra Jabeen, Comparative study of Assessment of Capital Adequacy Ratio (CAR) for Islamic Banks in Pakistan under Basel II and IFSB formulae for Capital Adequacy. Urvashi Shrivastava, Bobby Brahme Pandey, Daljeet singh Wadhwa, Evaluating the Performance of Axis Bank in terms of Capital Adequacy using Financial Indicators) (IJMBS Vo l. 1, Issue 3, September 2011), ISSN : 2330-9519 (Online), ISSN : 2231-2463 (Print), Vincent Okoth Ongore , Determinants of Financial Performance of Commercial Banks in Kenya, International Journal of Economics and Financial Issues, ol. 3, No. 1, 2013, pp.237-252. Worthington, Andrew and West, Tracey (2001), Economic Value-Added: A Review of the Theoretical and Empirical Literature, Asian Review of Accounting, 9(1):pp. 67-86. Zawadi Ally, Comparative Analysis of Financial Performance of Commercial Banks in Tanzania, Research Journal of Finance and Accounting, ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol.4, No.19.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net State-of-the-Art: E-Marketing Types, Practices, Emerging Trends and Technologies 1

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Anuja Bokhare and 2Pravin Metkewar Symbiosis Institute of Computer Studies and Research (SICSR), A Constituent of Symbiosis International University(SIU), Atur Centre, Ghokhale Cross Road, Model Colony, Pune-411016,Maharashtra State, INDIA.

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Abstract: Basically E-Commerce is an integral combination of EDI, E-Marketing and Internet Commerce. EMarketing is one of the components of E-Commerce. Primarily we have focused exclusively on E-Marketing aspects. The purpose of this conceptual paper is to discuss E-Marketing concept including its types, practices, current trends and technologies, its challenges also have singularly important that has been taken care of. Emarketing is the fastest way to communicate with customer through Internet to satisfy their needs and requirements. The various E-marketing types are E-mail marketing, mobile marketing, digital marketing etc., are discussed further thoroughly. The practices which are running during the process of E-marketing are explained that helps for improvement of E-Marketing strategies. This study also highlights the various business opportunities in terms of emerging trends and technologies for understanding their influence. The reviews of the research have discussed about its types, different practices, trends and technologies but there is no specific state of the art of E-Marketing given yet in the existing literature that has been consolidated here. Keywords: E-Marketing, E-mail Marketing, Mobile Marketing, Viral Marketing, Digital Marketing, ContentMarketing _________________________________________________________________________________________ I. Introduction E-Commerce system includes commercial transactions on the Internet. It can be classified by application type as Electronic markets, Electronic Data Interchange (EDI) and Internet Commerce has been depicted below:

Electronic Markets

EDI

Internet Commerce

Figure 1: Classification of E-Commerce E-Marketing, means the marketing of products or services over the Internet. It's a redefinition of how businesses interact with their customers. The terms like E-Marketing, Online marketing and Internet marketing are generally interchanged, and can considered as synonymous. E-marketing usually stated as a new approach and modern realistic involvement with marketing of goods, services, information and even ideas via Internet and other electronic media (El-Gohary 2010). E-marketing strategies encompass existing utilities and combine them with communication and data network to form a relationship with the organization and its customers through Internet environment. E-marketing (Smutkupt, Krairit, & Esichaikul 2010) includes mobile phones, Intranet and Extranet environment etc. E-marketing helps e-business to improve and overwhelmed the problem of traditional businesses where Internet plays a dynamic role. The distribution channel consists of wholesaler, retailer, consumer and the set of processes which link these elements with the help of information communication and technology to identify and define marketing opportunities called marker research. The outcome of this research

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used to generate, refine or evaluate marketing action such as supervising performance of market and help to improve understanding of marketing as a process. The information generated through marketing research further utilize to address different issues, develop new method for collecting information, manage and implements the data collection processes, examine the results and transmit their findings and inferences. It is the systematic gathering, recording, and analysis of qualitative and quantitative data about issues relating to marketing products and services. The objective of marketing research is to determine and evaluate how changing elements of the marketing mix impacts customer behavior. The term is commonly interchanged with market research; however, expert practitioners may wish to draw a distinction, in that market research is concerned specifically with markets, while marketing research is concerned specifically about marketing processes (Berthon., Pitt, Plangger, & Shapiro 2012). Classification of marketing research is either by a) Target market:  Consumer marketing research, and Business-to-business (B2B) marketing research also, b) Methodological approach:  Qualitative marketing research, and Quantitative marketing research II. Types of E-Marketing Now a days, E-marketing strategy is often used to grow business in a dynamic way using Internet and other media. Internet plays an important role in managing marketing tools and activities within concerned business. Electronic marketing via Internet, Extranet, mobile phones may create lot of opportunities for a business as well reduces a lot of threats. Banner advertising, is the most popular form of advertising recently used which is placed on the website with the appropriate content. There are different types of E-Marketing such as E-mail Marketing, viral marketing, digital marketing, blog marketing, affiliate marketing, search engine marketing, content marketing etc. few of them are explained below with respect to recent development. A. E-mail Marketing The web can be used as a powerful Internet marketing tool to promote Online businesses and reach target audience across the globe in different ways. E-mail marketing used for E-marketing, it is one of the primary way to strengthen the association with customer. It is an effective way to retain your customer, it saves time and paper. But, most of the articles focused on studying consumer responses w.r.t. e- mail marketing but no study was conducted from a behavioral point of view and lack of individual analysis using single- subject design method for e-mail. The understanding of the effects of e- mail on consumer behavior is highly trademarked because it is conducted mostly by firms and therefore it is not published. Chittenden and Rettie (2003) recognized the factors affecting the response rate in e- mail text. As per findings, there is a significant connection between the response rate and subject line, e- mail length, incentive, and the number of images. Use of color in e-mails shows a difference as per Zviran, Te’eni, and Gross (2006) and if used correctly, can prompt the recipient to respond as the sender planned. Marinova, Murphy, and Massey (2002) have investigated e- mail marketing as a means of targeted promotion. A complete collection of execution elements have been observed by Ellis-Chadwick and Doherty (2012) in a sample of permission-based e-mail marketing promotions. For testing purpose 1000 promotional emails were written to U.K. e-retailers over an 18-month. (Rossiter 1981). Sigurdsson et al., (2013) have found that the uses of e- mail and other Online tools have been applied on behavior analysis should be examined, for example, exploring the usefulness and indicating best practices for education era in near future. B. Mobile Marketing Marketing on mobile device such as smartphone is Mobile marketing. It is explained as an marketing activity accompanied with the use of network to which customers are frequently connected using a personal mobile device. More research is needed on how mobile technology changes should guide retailers. Due to the advancement in technology, retailers should prepare themselves with applications not just mobile-ready, but also mobile-savvy to grab business opportunities (Shankar et al 2010). Over the past few years mobile marketing has opened up new opportunities for firms to communicate and engage with their target audience in a more effective way. Organizations should focus on the power of the personal nature of mobile devices that distinguish mobile marketing from other forms of marketing with respect to fully utilize the mobile marketing features (Smutkupt et al 2010). Mobile Marketing (Tripathi, S.N. 2008) is relatively at a nascent stage in India, customers are looking for customized marketing messages as per their requirement. Therefore customerization is an important aspect for this type of marketing. Customerization basically means that companies interact their customers on one to one basis and give them a customized service, product or a message as per their requirements. This can be done using artificial intelligence such as “Intelligent Mobile Software Agents” this enables the firms to completely customize the marketing messages as per the customers’ needs. There is a potential downside to the

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development of new digital channels. In a Forrester report companies using SMS expressed fear for invasion of consumer privacy (80%) and negative consumer reaction (60%) as disadvantages of the media. So a crucial question for marketers is that should they go ahead with implementing this strategy or should focus back on their traditional ways of marketing? A mobile being a portable device has its own set of pros and cons. However mobile as a marketing device is relatively new and the list below illustrates the distinctive features (Smutkupt et al 2010).:  Ubiquity: Ubiquity is a primary advantage of the mobile medium. It refers to the ability of users to receive information and perform transactions wherever they are and whenever they want.  Personalisation: The mobile phone can be seriously “customized”. While business professionals would use the device for emails and other business deals. Young teenage students can use Office applications, SMS, GPRS, Edge, 3G and MMS features. This enables the marketing team to design and execute marketing design effectively and efficiently.  Two way Communication: The mobile phone is a two way communication device. This helps in understanding the customer needs. This feature enhances the CRM (customer relationship management). Eventually it helps serve customers better. C. Viral Marketing Viral marketing is based on social media. It can be considered as a promotional tool for marketing. The major problem faced by the viral marketing industry is the lack of formal quantitative and qualitative comparisons between viral marketing tool and traditional tools, also the inadequacy of organized methods for optimizing viral marketing campaigns. Viral marketing distinguishes itself from other marketing strategies as it is built on trust among individuals. The growing popularity of many Online social network sites, such as Facebook, Myspace, and Twitter, presents new ways for succeeding large-scale viral marketing (Chen et al; 2010). Ho and Dempsey (2010) have observed some unknown factors of viral marketing which is related to Internet users' motivations to pass along Online content and their relationship. Conceptualizing means communication behavior through consumption of Online content. It is identified that noble users, tend to forward more Online content than others.  Social Media has a growing effect in many perspectives: from one standpoint, it reverses the way how and why users communicate with each other. From other standpoint, it permits the extension of marketing communication opportunities, both in a business-to-business (B2B) and a business-to-consumer (B2C) aspect. E.g.Facebook, Microblocks, Social News websites, Collaborative projects, Content Communities etc.  Web 2.0 discussed by Berthon., Pitt, Plangger, & Shapiro (2012) have raised to social media as well as allowed creative consumers to put their own choices e.g a shift in activity location from desktop to the web.  Social media is determined by three major things about a country (Berthon., Pitt, Plangger, & Shapiro 2012).  Technology, i.e., the infrastructure which enables social media.  Culture, i.e., shared values  Government, i.e., government rules and regulations. D. Digital Marketing Chaffey (2013) has states that digital marketing makes use of technologies to help marketing activities in order to improve customer knowledge by matching their needs. Customers can read reviews and write comments about personal experiences through blogs, which can be used as a tool for digital marketing, which helps to increase sales revenue. In order to find the effectiveness of digital marketing in Pakistan, Khan and Siddiqui (2013) have collected data using questionnaire method for analysis purpose and author uses descriptive statistics and factors analysis methodology. It is observed that digital marketing is one of the new ways of promotion, but is misleading and not useful for word of mouth (WOM). E. Content Marketing Content marketing means creation and sharing of media and publishing content in order to acquire customers. It can consist formats as videos, photos, Power Point presentations, info graphics, white papers, case studies, webinars, and pod-casts. It focuses primarily on communicating with customers/readers/viewers rather than selling to them directly. Forouzandeh et al (2014) used content marketing where instead of introducing goods, content of goods are presented for marketing. Pulizzi and Yoegel (2012) have emphasized content marketing means a marketing process of creating and properly allocating the content in order to attract, make communication with, and understand other people so that they can be motivated to do helpful activities. Donath, J. and Boyd, (2004) have stated that There some reasons for failure of Online Marketing such as users remain

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unaware of the advertised goods and their advantages, nonexistence of information about users and their tastes, go for product without inspecting them and their behavior in the system. III. Practices E-Marketing strategy primarily focused on various practices but few of them have been discussed below: A. Triangulation method El-Gohary (2010) has provided detail study of different methodologies adopted by researchers to study the concept of E-Marketing through extensive literature review between the periods 2003 to 2010. The existing methodologies have evaluated to identify the new method for future study. Triangulation method is suggested for future research in E-marketing, in which combined research strategy like a survey and case study strategy collects quantitative and qualitative data using questionnaire and interview method. A quantitative strategy will help to identify the relationship between research variable (what) and a qualitative strategy will identify the reason for relationship (why).This approach helps researchers to increase the validity and credibility, generalization ability of the research result. B. Support of ERP E-Commerce means buying and selling products using Internet and achieved through Online transaction. Ecommerce include E-messaging, electronic data exchange, electronic funds transfer, electronic e-mail, electronic news sharing, Online services and other electronic transmission for commercial purpose. Ionescu and Serban (2012) have identified a model which interconnects concept of ERP and e-business for better improvement of an organization from technology point of view. Integrating business processes using E-business supported by ERP systems. This helps to redefine business processes and requirements. C. Risk Identification It is important for an e-Business to understand the legal threats that business may have and develop strategies to avoid and eliminate the legal risk. There are many guidelines that E-marketing industry have to follow. â&#x20AC;&#x153;Advertising Standards Authorityâ&#x20AC;?, a regulatory body, manages the laws of Marketing. It is important for an e-Business to understand the legal threats and develop strategies to avoid and eliminate legal risks. In this regard Iqbal et al (2013) have presented a novel approach to promote risk management culture in organization. Risk mitigation strategy, educating people and use of information technology is being considered for risk management. D. Customerization The relationship between the mobile marketing and consumer can be very well understood during the study of mobile marketing .But mobile marketing do not have a significant impact on the purchase or brand decision of the customer. It is observed that the mass marketing approach is being used by the companies it hardly depend on mobile marketing messages to mobile users. Hence the need of the hour appears to be Customerization (Tripathi, S.N 2008) for the potential effective and efficient implementation of this plan. Operationally driven mass customization and customized marketing in customerization so that the company would able to respond to an individual customer by customizing its products, services, and messages on a one-to-one basis. Customer prefers session based messages, which would self-delete after a given period of time. IV. Emerging Trends and Technologies A. Role of ICT Information communication technology (ICT) has changed the way of conducting business with the use of Internet and communication media. It also leads to increase use of E-marketing, the easiest way to reach customers and identify their requirement and set the goals for businesses. It is the much cheaper, useful method suitable for producers and suppliers all over the world. Email Marketing is a form of direct marketing which utilizes electronic means to deliver commercial messages to an audience (Salehi Mirzaei et al 2012). Salehi Mirzaei et al (2012) have observed that Information Communication Technology (ICT) translate business over the Internet which creates new growing electronic channels for marketing. Different types of modern marketing i.e., Internet marketing, email marketing, and Online advertising plays very important role. Customer can have look and feel to product through traditional marketing, whereas e-marketing increases the scope and boundaries for new goods and services. Due to the use of Internet, E-marketing is much more advantageous than traditional marketing which is cheaper, faster and convenient way for marketing. B. Business Opportunities E-marketing is the most reasonable, economical and faster method to reach to customer or to provide service to the customer at door step. In traditional marketing, the domain of effect is low, whereas E-marketing exceeds

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the boundaries and brings in products and services to the demographic of Internet users. Rath and Samal (2013) have provided an overview about significance of E-commerce used for product promotion by organizations as well. They discuss about new tools and trends used for promotion purpose. Businesses use Internet for better product promotions and also to enhance business plans and economical health with the help of E-marketing strategies. B1. Market orientation E-marketing helps in relationship with market orientation (Tsiotsou and Vlachopoulou 2011). Market orientation is found to contribute to performance through a dual mechanism in that it contributes both directly and indirectly, through e-marketing, to the relationship. Market orientation means a business culture which accelerates firms in achieving sustainable competitive advantage. Market orientation is important to improve business performance. B2. Component-wise approach It is observed from the findings that several tourism firms in various countries are not effectively using the Internet for web-based marketing and e-commerce (Cheng & Krumwiede 2012). Earlier component-wise approach used for examining market orientation, so future study should examine the individual effects of the market orientation components on performance and other marketing activities.

V. Conclusion Current status or state of the art of E-Marketing with respect to its types, practices and trends and technologies have been elaborated and discussed thoroughly in the above sections. Its current status need to be strengthened by focusing on following aspects: Currently Inter-industry and inter-ecosystem opportunities and trends are available specific to particular market but not from global market perspective. E-Market Value addition is available at National and International level up to some extent but that needs to be extend at co-operative and urban level with ICT infrastructure. Open and closed networked business models need to enhance at root level by using the concept of Information kiosks systems where layman can handle the system with minimal effort addressing the end-user’s needs, wants and desires. VI. Future Scope As per the findings, there is no such specific study currently focusing on inter-relationship between E-marketing and its market orientation. Also their influence has impacted on business performance need to be taken care off. References [1] [2] [3]

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Berthon, P. R., Pitt, L. F., Plangger, K., & Shapiro, D. (2012). Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy. Business Horizons, 55(3), 261-271. Chaffey. D, “Definitions of Emarketing vs Internet vs Digital marketing”, Smart Insight Blog, February 16, 2013. Chen, W., Wang, C., & Wang, Y. (2010, July). Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1029-1038). ACM. Cheng, C. C., & Krumwiede, D. (2012). The role of service innovation in the market orientation—new service performance linkage. Technovation, 32(7), 487-497. ChiTTENdEN, l., & RETTiE, R.(2003). An evaluation of e- mail marketing and factors affecting response. Journal of Targeting Measurement and Analysis for Marketing, 11, 203–217. Retrieved from http://dx.doi.org/doi:10.1057/palgrave.jt.5740078 Donath, J.& Boyd, D.(2004). Public displays of connection.BT Technology Journal,22(4), 71-72. Ellis-Chadwick, F., & Doherty, N. F. (2012). Web advertising: The role of e-mail marketing. Journal of Business Research, 65(6), 843-848. El-Gohary, H. (2010). E-Marketing-A literature Review from a Small Businesses perspective. International Journal of Business and Social Science, 1(1), 214-244. Forouzandeh, S., Soltanpanah, H., & Sheikhahmadi, A. (2014). Content marketing through data mining on Facebook social network. Webology, 11(1). Ho, J. Y., & Dempsey, M. (2010). Viral marketing: Motivations to forward online content. Journal of Business Research, 63(9), 1000-1006. Ionescu, A. A., & Serban, R. (2012). Presentation of various types of electronic business available on the Internet, Advantages, Disadvantages, Key Requirements and Security, Implementation Model of an Electronic Business. International Journal of Economic Practices and Theories, 2(3), 179-196. Iqbal, M., Khan, A. A., & Naseer, O. (2013). A legal perspective of E-business and E-marketing for small and medium enterprises. arXiv preprint arXiv:1303.2675. Khan, F., & Siddiqui, K. (2013). The importance of digital marketing; an exploratory study to find the perception and effectiveness of digital marketing amongst the marketing professionals in Pakistan. Journal of Information Systems & Operations Management, 7(2), 221-228. MARiNoVA, A., MuRPhy, J., & MASSEy, B. l. (2002). Permission e- mail marketing as a means of targeted promotion. The Cornell Hotel and Restaurant Administration Quarterly, 43, 61–69. Retreived from http://dx.doi.org/10.1016/S00108804(02)80009-x. Mirzaei, H., Jaryani, E., Aghaei, M., Salehi, M., & Saeidinia, M. (2012). Differences of “Traditional Marketing” in opposition to “Electronic Marketing”. International Proceedings of Economics Development & Research, 29

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Pulizzi, J.,& Yoegel, R. (2012). Six useful content marketing definitions. Retrieved May 10, 2014, from http://contentmarketinginstitute.com/2012/06/content-marketing-definition. Rath, R. C., & Samal, M. S. (2013). An Overview of E-Commerce Practices of Marketing on Supply Chain Management in India: Emerging Business Opportunities and Challenges. International Journal of Supply Chain Management, 2(2). Rossiter, J. R. (1981) “Predicting Starch Scores” Journal of Advertising Research 21(5) pp 63-68. Salehi, M., Mirzaei, H., Aghaei, M., & Abyari, M. (2012). Dissimilarity of E-marketing VS traditional marketing. International Journal of Academic Research in Business and Social Sciences, 2(1), 814-825. Shankar, V., Venkatesh, A., Hofacker, C., & Naik, P. (2010). Mobile marketing in the retailing environment: current insights and future research avenues. Journal of interactive marketing, 24(2), 111-120. Sigurdsson, V., Menon, V., Sigurdarson, J. P., Kristjansson, J. S., & Foxall, G. R. (2013). A test of The Behavioral Perspective Model in the context of an e-mail marketing experiment. The Psychological Record, 63, 295-308. Smutkupt, P., Krairit, D., & Esichaikul, V. (2010). Mobile marketing: Implications for marketing strategies. International Journal of Mobile Marketing, 5(2), 126-139. Tripathi, S.N. (2008). Investigating the Impact of Mobile Marketing in the Current Indian Scenario and Proposing CUSTOMERIZATION as a solution, 11thAnnual Convention of the Strategic Management Forum. Tsiotsou, R. H., & Vlachopoulou, M. (2011). Understanding the effects of market orientation and e-marketing on service performance. Marketing Intelligence & Planning, 29(2), 141-155. ZViRAN, M., TE’ENi, d., & GRoSS, y.(2006). Does color in e- mail make a difference? Communications of the ACM, 49, 94– 99. doi:10.1145/1121949.112195. Short Bio-Data of Authors: Author’s Name: Dr. Pravin S. Metkewar Author’s profile: He has received his Ph.D. degree from SRT University, Nanded in computer science under the faculty of science in Aug 2005. He has 16 years of experience in the field of teaching, RnD (research) centre and industry. His specialization in Information Systems, Neural networks, Fuzzy Logic, OOAD and UML. He has presented and published 23 research papers and 2 books in his account. He is a research guide of Symbiosis International University, Pune. He is a CSI life member.

Author’s Name: Mrs. Anuja M. Bokhare Author’s profile: She is working as Assistant Professor in the department of Computer Science at Symbiosis Institute of Computer Studies and Research, Pune, Maharashtra India. She received M.Phil. (Computer Science) at Y.C.M.O.U , Nasik, India. She has 12 years of experience in the field of academics. Her research interest includes applications of fuzzy logic, neural network, Software Engineering. She had published 3 research papers in international journal and one book in her account.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Automatic Video Annotation using ECGM S. Divya Meena1, S. Vidhya Meena2 ME CSE1, BE CSE2 Kingston Engineering College, Vellore– 632 059, Tamil Nadu, INDIA1, 2 _________________________________________________________________________________________________________________________________________

Abstract: Identifying faces of characters in movies, though a challenging problem has drawn a significant research interest and led to many interesting applications. Automatic Video Annotation can be used to match the Face and Name of a character in movies. While there are many a methods which produces hopeful results in clean environment, the performance of these methods fail in complex movie scenes due to the inevitable noises generated during face tracking, detecting and clustering. The paper uses two schemes namely; (i) Face Name Matching with Cluster (ii) Face Name Matching without Cluster. Both the schemes use Error Correcting Graph Matching (ECGM) Algorithm in common. The complex character change in movies is handled by Simultaneous Graph Partition and Graph Matching. Two simulated noises are used to achieve an In-Depth Sensitivity Analysis. Finally Global Face-Name Graph Matching based framework for robust movie character identification is used. In brief, the core of the problem is to exploit the relations between videos and the associated texts in order to label them with the corresponding names in the cast. Keywords: ECGM; Sensitivity Analysis; Video Annotation. __________________________________________________________________________________________ I. Introduction The explosion of movie and TV has resulted in large amount of digital video data. The need of the hour is for an efficient and effective technique with which the videos can be understood and organised. Automatic video annotation can be applied for this purpose. This project focuses on annotating characters in the movie and TVs, which is called “Movie character identification”. Character identification problem develops a relation between videos and the texts associated to it, with which we can label the faces of characters with its appropriate name. Video Annotation is similar to identifying faces in news videos. But, in news videos, names of the faces are available from the local texts that appear simultaneously. And in TVs and Movies, names of the characters are mentioned in the script and not in the screenplay. But the script doesn’t contain any timestamp to align to the video. Movies content and its structure can be decided by the characters involved, which make the focus point of the audience.Automatic character identification is essential for semantic movie index and retrieval scene segmentation, summarization and other applications. II. System architecture The system architecture explains the various processes in video annotation. This process altogether forms the video annotation technique. Following are the process involved in the video annotation technique;

Figure 1: Architecture Diagram for Face detection and Recognition

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Error Correcting Graph Matching (ECGM): The two Schemes that are considered in robust face name graph matching uses ECGM algorithm, a powerful tool for graph matching with distorted inputs i.e. to measure the similarity of two graphs. In ECGM, the difference between the two groups is measured by editing distance which is a sequence of graph edits operations. The optimal match is achieved with the least edit distance. Method 1: Method 1 Face tracking framework is clustered using constrained K-means, where the number of clusters is set as the number of distinct speakers. Co-occurrence of names in script and face in videos represents the corresponding face graph and name graph. We use ordinal graphs for robust representation and an additional ECGM-based graph matching method is introduced. The face and name graph construction will be represented in rank ordinal level, which gets the strength of the relationships in a rank order from the weakest to strongest. Rank order data carry no numerical meaning and thus are less sensitive to the noises. Method 2: The proposed framework for Method 2 has two differences from Method 1. 1. Face tracks clustering step requires no cluster number. 2. A graph partition component is added before numbered graph representation, since the face graph and name graph may have different number of vertexes. Method 2 groups face tracks from different characters, instead of from same character. For this reason, we use Similarity propagation for clustering the face tracks. For each cluster, we take a sample and the face tracks are recursively clustered through the appearance based similarity broadcast and propagation. With this, we expect a high purity cluster with large number of clusters. Graph partition is introduced before graph matching, since one character name may correspond to several graph matching. If the partitioned graph achieves an optimal graph matching with the name graph, then the face cluster can be grouped further. In brief, we perform face clustering in two steps as (i) Clustering by appearance i.e., Face Clustering (ii) Modifying by script. Simultaneously, we optimize face clustering and graph matching to improve the robustness against errors and noises. III. System analysis A Global face-name graph matching based framework for robust movie character identification is used. Two methods are considered. There are connections as well as differences between them. Regarding the connections, firstly, the two methods both belong to the global matching based category, where external script resources are utilized. Secondly, to improve the robustness, the ordinal graph is employed for face and name graph representation and a novel graph matching algorithm called Error Correcting Graph Matching (ECGM) is introduced. Regarding the differences, method (1) sets the number of clusters when performing face clustering. The face graph is restricted to have an identical number of vertices in the name graph. While, in the method (2), no cluster number is required and face tracks are clustered based on their intrinsic data structure. Moreover method (2) has an additional module of graph partition compared with a method (1). From this perspective, the method (2) can be seen as an extension to the method (1). IV. Sensitive analysis Unavoidable noises are generated during the process of face detection, face tracking and face clustering. This may be due to the pose, expression, illumination variation as well as low resolution problem. All this implicitly mean that the derived face graph and the name graph do not exactly match. So, the face graph can be viewed as a noisy version of the name graph. Therefore, sensitivity analysis step has become a must to evaluate the performance of the methods. In this section, we first use two types of simulated noises namely, coverage noise and intensity noise. The sensitivity score for measuring ordinal graph downgrading is defined to evaluate the robustness of the ordinal similarity graph to noises. V. Coverage noise and intensity noise In the graph construction process for character identification, it is found that several noises such as vertex substitution, edge substitution and edge destruction or creation are involved. According to that, we use two types of noises, coverage noise and intensity noise for simulation. The graph connectivity structure is very important in graph matching. We use the graph to edit operations of edges creation and destruction to simulate the changes to the topology of the graph. We call this as coverage noise. The creation or destruction probability for each existing or potential edge denotes the coverage noise level. Intensity noise refers to the changes in the edgeâ&#x20AC;&#x2122;s weight. It has involvement with the quantitative variation of the edges, but with no care for the graph structure. A random value distributed uniformly in the range denotes the intensity noise level.

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VI. System design A. Detection In detection module we are going to detect the face of the movie characters using Error Correcting Graph Matching (ECGM), which is a powerful tool for graph matching with distorted Inputs. The Microsoft Face Detection system provides a solution that can automatically detect faces in still images and real time video feeds. The system can detect an arbitrary number of faces at any scale and location. Input to the system is either a photographic image or a video. The output of face detection is then aligned. If it detects no faces, it will return an empty array. The system attempts to eliminate false positives by skipping non-faces. It applies a color filter and an edge filter to improve the precision of the detection. It uses a lighting correction to further eliminate false positives. The detected faces along with confidence are outputted in the form of rectangle. The following graphic illustrates the Face Detection system:

Figure 2: Face detection B. Recognition Face recognition is the task of recognizing a person using digital face images. FRS is used to measure the similarity between two faces or images and produce the output. Automated FRSs typically involve finding key facial landmarks (such as the centre of the eyes) for alignment, normalizing the face’s appearance, choosing a suitable feature representation, learning discriminative feature combinations, and developing accurate and scalable matching. To recognize the face of the movie characters which is we previously stored on the face database, we just need to give the real name of it. This will be done with the help of the Eigen Object Recognizer we use to recognize the face. Four key factors that significantly compromise recognition accuracy are posing, illumination, expression, and aging it shows the impact of facial aging on face-recognition performance. Thus, deployments of fully automated FRSs are mostly limited to scenarios in which we can largely constrain these factors. VII. Conclusion Face recognition in video is an active topic in computer society because of the many potential applications it has. Two different methods and a recent work on face retrieval are introduced. Finally, some challenges and research directions are discussed. Although we mainly focus on video-based approaches, recent years have witnessed some interesting still-image based approaches, which could be helpful for face recognition in video. References [1] [2] [3] [4] [5] [6]

J. Sang, C. Liang, C. Xu, and J. Cheng, “Robust movie character identification and the sensitivity analysis,” in ICME, 2011, pp. 1–6. J. Sang and C. Xu, “Character-based movie summarization,” in ACM MM, 2010, pp 71-89. R. G. Cinbis, J. Verbeek, and C. Schmid, “Unsupervised Metric Learning for Face Identification in TV Video,” in International Conference on Computer Vision, 2011. M. Xu, X. Yuan, J. Shen, and S. Yan, “Cast2face: character identification in movie with actor-character correspondence,” in ACM Multimedia, 2010, pp. 831–834. L. Lin, X. Liu, and S. C. Zhu, “Layered graph matching with composite cluster sampling,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 8, pp. 1426–1442, 2010. H. Cevikalp and B. Triggs, “Face recognition based on image sets” in CVPR, 2011, vol. 42, no. 18, pp. 1526–1542.

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N. Cherniavsky, I. Laptev, J. Sivic, and A. Zisserman “Semisupervised learning of facial attributes in video”, in The first international workshop on parts and attributes (in conjunction with ECCV 2010), 2010. M. Guillaumin, J. Verbeek, and C. Schmid, “Multiple instance metric learning from automatically labeled bags of faces”, in ECCV, 2010. P. Pham, M. Moens, and T. Tuytelaars. Cross-media alignment of names and faces. IEEE Transactions on Multimedia,12(1):pp.13–27, 2010. R. Hong, M. Wang, M. Xu, S. Yan, and T.-S. Chua, “Dynamic captioning: video accessibility enhancement for hearing impairment”, in ACM Multimedia, 2010, pp. 421–430. Y. Zhang, C. Xu, J. Cheng, and H. Lu, “Naming faces in films using hypergraph matching,” in ICME, 2009, pp. 278–281. T. Cour, B. Sapp, A. Nagle, and B. Taskar, “Talking pictures: Temporal grouping and dialog-supervised person recognition,” in CVPR, 2010, pp.1014–1021. R. G. Cinbis, J. Verbeek, and C. Schmid, “Unsupervised Metric Learning for Face Identification in TV Video,” in International Conference on Computer Vision, 2011, pp. 278–281 . Arandjelovi´c, O., Cipolla, R.: “A pose-wise linear illumination manifold model for facerecognition using video” in Computer Vision and Image Understanding, pp. 113–125, (2009) J. Sang, C. Liang, C. Xu, and J. Cheng, “Robust movie character identification and the sensitivity analysis,” in ICME, 2011, pp. 1–6 J. Yang and A. Hauptmann, “Multiple instance learning for labelling faces in broadcasting news video,” in ACM Int. Conf. Multimedia, 2005, pp. 31–40. A. W. Fitzgibbon and A. Zisserman, “On affine invariant clustering andautomatic cast listing in movies,” in ECCV (3), 2002, pp. 304–320. O. Arandjelovic and R. Cipolla, “Automatic cast listing in feature lengthfilms with anisotropic manifold space,” in CVPR (2), 2006, pp. 1513–1520. D. Ramanan, S. Baker, and S. Kakade, “Leveraging archival video for building face datasets,” in ICCV, 2007, pp. 1–8. M. Everingham and A. Zisserman, “Identifying individuals in video by combining generative and discriminative head models,” in ICCV, 2005, pp. 1103–1110. R. G. Cinbis, J. Verbeek, and C. Schmid, “Unsupervised Metric Learning for Face Identification in TV Video”, in International Conference on Computer Vision, 2011. M. Xu, X. Yuan, J. Shen, and S. Yan, “Cast2face: character identificationin movie with actor-character correspondence,” in ACM Multimedia, 2010, pp. 831–834. M. Everingham, J. Sivic, and A. Zissserman, “Hello! my name is... buffyautomatic naming of characters in tv video,” in Proceedings of BMVC,2006, pp. 889–908. J. Sivic, M. Everingham, and A. Zisserman, “Who are you? – learning person specific classifiers from video,” in Proceedings of CVPR, 2009, pp. 711–734. T. Cour, C. Jordan, E. Miltsakaki, and B. Taskar, “Movie/script: Alignment and parsing of video and text transcription,” in ECCV (4), 2008, pp. 158–171.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Harnessing the power of Viral Marketing through Social Media– A study on IT industry Dr.Anita Venaik Assistant Professor-III, Amity Business School, Amity University, Sector 125, Noida, Uttar Pradesh 201303, INDIA ________________________________________________________________________________________ Abstract: Marketers are mainly concerned with tactics, which is followed by concern investment. Currently tactics have moved lower in priority and the return on investment is the main concern. There is no concern about if the social media works or not. It’s not the issue on chats now because Viral marketing campaigns having online presence has give birth to the jargon “Go viral”. This includes non-interactive media like videos, podcasts, articles or blog posts, as well as interactive content like tools, web-based games. However the most viral type of campaign is an idea of virus that is divorced from its original media and spreads by discussion. Individual contents can be viral content, while the concept of the content genre is a meme people talk about them and make their own rather than refer or link to one specific instance. The first step for going viral is to design strategy for marketers interested in creating a viral promotion. There are certain challenges like business goals which leads to lead of business the effective way to find potential target audience and then define a way/medium to reach those potential buyers. In the research here Researcher tried to analyze how marketers use social media to market, promote and grow their businesses. It will be of help to marketers who are just starting to build on Social media or seeking support on it. It also compares other tools used to gain business. The research lays emphasis on how B2B and B2C is using affiliate programs to promote their brand. Design and develop strategy for displaying logo along with relevant information on thousands of sites for free which creates a viral world. Key words: Viral marketing, marketers, strategy, media, online promotions __________________________________________________________________________________________ I. Introduction Viral marketing and viral advertising are buzzwords referring to marketing techniques that use pre-existing social networks increases brand awareness through self duplicating viral processes, that is analogous like virus / computer viruses. It can be word-of-mouth delivered and getting escalated by the various effects on Internet. It can be done in the form of video clips, interactive Flash games, adv-games, e-books, brand able software’s, images, or even text messages. The main task of marketers is to know such people who generally do social networking and create viral messages that can generate interest in such target customers which would help in generating the WOM. This technique is also quiet similar to stealth marketing campaigns—the unscrupulous use of astro-turfing which is on net and combined along with such techniques that market in store to create environment of WOM. Social media marketing has been recent upgrade to integrated marketing communications. It is a principle that organization‘s follow to reach their target markets. It coordinates and mixes the various elements of promotional mix, advertising, personal selling, public relations, publicity, direct marketing, and sales promotion. Viral marketing campaigns also form a part of this integrated group. As a part of traditional marketing communication, various parts-content, frequency, timing, and medium of communications are in collaboration with plans of advertising collaborating with marketing research that then decides the plans for public relations.

Source: Journal of Social Media Marketing

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Social media‘s main aim is to maintain such data that people would be interested and would get attracted to generate WOM. Generally WOM is maintained when the data is created by a person trusted in the network and also then spreads faster compared to others. Anyone can login to Social Media from anywhere irrespective of location. Due to better interaction with target customers, the organizations are able to cater to needs of that target. The overall cost being low for the same. In the past two years, a number of marketers have experimented with social media by hiring an intern to simply set up a Face book fan page or establish a Twitter account. In 2013, however, the experimentation phase is over and social media is ready for prime time. A March further more, social media is increasingly being used by both B2B and B2C marketers. Although B2B has lagged B2C, it is catching up and innovating new applications of social media. Both B2B and B2C businesses are using social media to engage with their customers for:

Source: Can Knowledge Management be Open Source?, IFIP International Processing, Open Source Development • Influence – increase and drive awareness • Customer Acquisition – drive sales of products and services • Ideation – solicit input into product development • Customer Service – provide a competitive edge to customer service • Customer Loyalty – build and strengthen brand loyalty

Federation for Information

Source: Journal of Marketing -Web2.0 In order to maximize social media and marketing ROI, recommendations are for using a five-step approach to campaign planning and design Step 1—Define Business and Marketing Goals Step 2—Strategic Solutions Step 3—Design and Build Step 4—Activate and Distribute Step 5—Measure and Optimize II. Research Objective 1. To analyzes the various components of viral marketing and various factors leading to its growth and profit. 2. To find out how marketers are benefit with the media using viral marketing. 3. To find out how IT companies harnesses Social Media Marketing to target potential customers 4. To find out how Companies can utilize viral elements in integrated marketing plan for their benefits. III. Research methodology Social media was utilized for this survey and research was focusing only on the industry people. And the links of Linkedln, facebook and other social media channels were given utmost importance. As per the survey when marketers were asked for their experience using social media marketing, 65% of marketers have either just started harnessing social media or have been marketing using Social media for a shorter time frame. 79.5% of survey participants have used Social Media Mainly for B2B whereas only 68.7% have actually used it for B2C marketing purpose. Maximum people use social media for at least 1 to 5 hours on a weekly basis. As per analysis only 43% among marketers utilize this Social media for more then 4 or 5 hours on a weekly basis on social media activities.

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Among Marketers,76% utilize Social Media for marketing for 4 hours weekly for marketing their business. Most important benefit of Social Media marketing is to generate awarance about the brand or the business. Among the marketers surveyed, 85% affirm that the Social Media efforts has given their business a good exposure in terms of target customers it also helped in increasing the visits to company websites. Major changes that took place in Social Media world with viral marketing is that all the major categories benefited and grew at the rate of 56%. The number of marketers who felt Social media helped reduce their expense grew from 35% to 48%.Marketers have been concerned if they can benefit by putting in more time can they benefit or would it increase their profit. IV. Results Most of surveyed marketers (almost 2/3rd) have utilized social media marketing websites as well as the necessary tools for event marketing etc. Quiet a few have moved to social sites like the ones face-book whereas quiet a few consider bogs, twitter and linkedln as a far better option. They even utilize blogs and micro-blogging sites established for internal and external use.

This show Trends and Technologies do affect companies Social Media Activities. It has a positive relation. Almost around 31% marketers utilized these tools for their exhibits to be marketed whereas around 24% need it and utilize it for the B2B Marketing as well as attracting B2C Customers. Quiet a lot of these marketers (57%) utilized these tools for normal brand marketing. Very little marketers were such that they had established proper success rules/mantras for judging these social media campaigns (around 1/3rd) . Social media tools actually over performed crossing expectations of over 9/10 people.

Whereas only 32% of marketers consider social media to be an immense potential too, most marketers(around 58%) consider it to have not much of the spark of generating profit

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More time needed for making successful social media plan is the one effecting business. Almost around 32% refuse to use it because they have shortage of time The below table shows how Effective activities in Company’s social media communications plan do help in increasing ROI. The 34% of respondents to this survey who indicated that they do not use Social Media for Marketing cite a less time for social media marketing efforts (22.1%) and lack of social media marketing knowledge (20.1%) as the top two reasons for not incorporating Social Media into their marketing efforts. 80.1% of those using Social Media for Marketing claim increased brand awareness as a direct result. In Other important get-backs are improving relationship on clients, prospects (54.9%), Coverage by Media (49.6%), increased event attendance (21.2%) and booth traffic (19.9%) and, increased or incremental increases in sales (16.5%). Companies generally utilize Social tools like face book, to connect to the consumers regularly and get their views and feedbacks on company’s performance, wherein two-thirds of IT company follow. Higher then 50% IT companies are regular on networking sites and blogs.

Usage of latest Technologies and Trends does impact Social Media Plan’ success. Nearly half of companies who’ve used social media for their events, report that following the event, their company tracks or reports metrics related specifically to the use of social media as a marketing tool. V. Discussion/Conclusion Organizations Having proper Planning for Social Media Some of the marketers surveyed said, around 41% of Organizations have a properly and strategically designed Social media Marketing plan. Among the rest of the Marketers from the 78% who use Social Media, organizations had no plans defined for utilizing Social Media effectively Parts of Organization Involved in Social Media Plan Marketers surveyed said marketing activities were the major chunk of the Social media planning and hence the team taking care of it as a whole was also marketing team of the organizations(almost around 71% had this view) . Almost around 83% of surveyed marketers said PR was also a good part of the social plan, whereas quiet a 55% expressed having concern on sales acts and hence including it in Social media plan. Having no Social Media Planning Most marketers who expressed that their organization didn’t have any such social media plan for marketing, also expressed that they did realize the need of one(almost around 80%) . Marketers said that most companies have a closed and a kind of cohesive marketing program and hence they fail to integrate Social Elements into it. VI. Trouble with Employee Migration to Social Technology Majority of marketers said that though their company has a proper social media plan in place, employees are not updated about it with any details, which leads to giving incorrect information to customers. Main teams taking part in Social Media transition is marketing team and PR team. Maximum of the organizations do not have a detailed plan in written mode or do not have any particular metrics tracked for success evaluation, which adversely affects the business reputation and its survival. Organizations should necessarily plan in a way that incorporates all effective teams in designing the social media plan. They should always keep employees updated on recent social media activities so that they are proactive rather reactive when need arises. It also helps in generating confidence in employees as well as communicating at proper levels with consumer. References Anonymous. March 29, 2008. Has Viral Marketing Burned Itself Out? Arno Scharl, Astrid Dickinger , Jamie Murphy, Diffusion and success factors of mobile marketing; Institute for Tourism and Leisure Studies, Vienna University of Economics and Business Administration, Augasse 2-6, A-1090. Bannan, Karen J. June 5, 2007. It's Catching. Adweek. Brewer, Brady. February 22, 2009. Tips for Optimizing Viral Marketing Campaigns. Brooker, Katrina. February 2008. No Sales, No Profits, No Problem. Now Ecompany Deal, Marianna, and Pete Abel. February 26, 2009. Grass Roots: The Exponential Power of One. Fattah, H.M. October 2010 .Viral Marketing- Nothing That‘s New . Technology of marketing. Godin, Seth. 2008, ―Unleashing - Idea of virus‖ , Do You Zoom, Inc. Hanson, Ward, 2010, ―Principle in Internet - Marketing‖, Southern College Publishing.

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Nick Higham, Nick. May 18, 2009. Why Viral Marketing can lead into the Virtual Pestilence. Jurvetson and Tim Draper. June 19 2008. Viral & Social Media Marketing. Kelly, Erin. November 27, 2009. The Virus One would Want to Spread. Fortune group. Mount, Ian. October 2008. Ebola. Smallpox. Christina Aguilera. Ecompany now. Murphy, Claire. August 3, 2008.Are Consumers Resisting Viral Ads? Marketing. Neuborne, Ellen. March 19, 2007. Viral Marketing Alert! Business Week. O'Reilly, T. 2009. What Is Web 2.0 – Design Patterns and Business Models for the Next Generation of Software, Web publication: http://www.oreillynet.com/pub/a/oreilly/tim/news/2009/09/30/what-is-web-20.html (accessed 02/24/2010). Pfaff, C. and Hasan, H. 2009. Can Knowledge Management be Open Source?, IFIP International Federation for Information Processing, Open Source Development, Adoption and Innovation, 234, pp 59-70. Rogers, E.M. 2008. Diffusion of Innovations, The Free Press. New York, 1st Edition ed. Vossen, G. and Hagemann, S. 2008. Unleashing Web 2.0: From Concepts to Creativity, Morgan Kaufmann. Rasmusson, Erika. June 2009.Viral Marketing is way Healthier. Marketing Management. Rosen, Emanuel. 2008 ,The Buzz: Creating Word-of-Mouth. Doubleday. Sandberg, Jared. April 12, 2007, The Friendly Virus. Newsweek. Sansoni, Silvia. July 5, 2009 , Word-of-Modem. Forbes. Samiee, S. (2008) Global marketing effectiveness via alliances and electronic commerce in business-to-business markets. Industrial Marketing Management, 37, 3 – 8. Thompson, S.A. & Sinha, R.K. (2008) Brand communities and new product adoption: The influence and limits of oppositional loyalty. Journal of Marketing, 72(6), 65 – 80. Weber, Thomas E. September 13, 2008. E-World: New Web Ploy -> Might Make You a great Friend. Wall-Street Journal. Wixom, B. H. and Todd, P. A. 2007. A theoretical integration of user satisfaction and technology acceptance, Information Systems Research, 16(1), pp 85-102. Yi, M., Jackson, J., Park, J., and Probst, J. 2009. Understanding Information Technology Acceptance by Individual Professionals: Toward an Integrative View, Information and Management, 43(3), pp 350-363.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net A Knowledge Management approach for Developing Research Community among Universities 1

Dhananjay S. Deshpande, 2P. R. Kulkarni, 3Pravin S Metkewar 1 Assistant Professor Symbiosis Institute of Computer Studies & Research (SICSR) A constituent of Symbiosis International University, Pune- 411016, Maharashtra, INDIA 2 Head of the Department, Kholeshwar Mahavidyala, Ambajogai, Dist- Beed– 431517, Maharashtra, INDIA 3 Associate Professor Symbiosis Institute of Computer Studies & Research (SICSR) A constituent of Symbiosis International University, Pune - 411016, Maharashtra, INDIA _________________________________________________________________________________________ Abstract: Universities provide Research Foundation degrees and Research oriented degrees like M.Phil., Ph.D. and Post Doctoral degrees through a network of Research Centers, Research Institutes & University Departments. This is centrally managed through collaboration with other institutes; but raises the challenge of making best usage of vast amount of knowledge and expertise in research.. Researchers who are working at different locations are not able to collaborate without creating or providing the research network. A mechanism by which this expertise can be shared is the need of the day. This can be achieved through a proper Knowledge Management System. This system should be investigated and then it should be employed at the university level in the form of tool. The Research scholars would be able to use this system as an important information and communication tool. This Knowledge Management System can serve as set of features for sharing research related databases, ideas, research experiences, knowledge, expertise, etc. This study is based on Knowledge Management approach for Research Knowledge Community development and specifically focuses on need of development of the system at university level. It aims to collaborate more number of researchers and high quality research in multidisciplinary and interdisciplinary streams. Keywords: Knowledge Management System (KMS), Research Knowledge Community (RKC), Research Knowledge Exchange Network (RKEN), Research Knowledge Management System (RKMS). _________________________________________________________________________________________ I. Introduction The rapid development in information technology and science changes the paradigm of the university, now a day’s universities are working on online systems. Universities have adopted online solutions for different processes like admission process, convocation process, exam applications, online exams, etc. The research projects and research work running in the university, has most important assets of the university [3]. The researchers contributing in their research work are most important knowledge resources. Researchers having subject expertise and set of knowledge in the respective subjects, due to various research projects and research work experience. The new researchers are adding in the list by obtaining different degrees like M.Phil., Ph.D, Research Fellows, Research Project Assistants. These all researchers, research supervisors and related all individuals forms a Research Knowledge Community (RKC) in the university. They need help and expertise guidance in different tasks of research process [14]. They initially rely on resources available in university like library, internet & expert consultation. But the expertise knowledge with individuals will not able to communicate due to geographical distances, so the knowledge exists in the individuals in the university should be managed in the university. Problem arise in the university is knowledge of individuals is not documented and well organized, and even the university itself does not realize that the individuals have knowledge that can enhance competitive advantage among researchers. Though this system, the Research Knowledge Exchange Network (RKEN) should be formed among the RKC. Through RKEN, researchers can easily get idea about the expertise and knowledge set of individuals, they can pass information, communicate each other, share their views and experiences through RKEN. This research knowledge can be managed by scientific systems, known by the term Knowledge Management System (KMS) [3]. In general, the implementation of KMS aims to increase the competitive advantage of university and can be one factor to improve the performance of the university.

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II. Research Knowledge Community A social, religious, occupational, or other groups sharing common characteristics or interests perceived or perceiving itself as distinct in some respect from the larger society within which it exists (usually preceded by the): the business community[2]; the community of scholars. A community is a social unit of any size that shares common values. Although embodied or face-to-face communities are usually small, larger or more extended communities such as a national community, international community and virtual community are also studied. As the concept of community, the university and its related institutions, colleges and students, teachers and other staff, these are named as academic community [3]. In academic community, the special and important community in higher education, at university level is Research Knowledge Community (RKC). The RKC is a community of research scholars, research facilitators and research staff as shown in the Fig. 1.1. RKC at university level includes different research institutions, research centres, university departments which are the controlling bodies for the researchers. The RKC members can be distributed in two different types i.e. direct members and indirect members. The RKC direct member means, who are directly related to the research work as individual â&#x20AC;&#x201C; Research Students, Research Fellows, Research Assistants, Research Guides / Supervisors. The RKC indirect member means, the members, who are not directly working on research but they are working as employee in the university departments, institutions and libraries. Also the other research experts and research scholars from other universities can be indirect members.

Fig. 1.1: Structure of Research Knowledge Community The RKC is having a common interest, common goal of research. The research of university is important knowledge asset of the nation. The government is always motivating the scholars for the research activities through offering different research student's scholarships and research project grants. The RKC would be a special kind of community which always needs interaction, communication, and sharing of knowledge from various streams and disciplines for innovative research work. The community members are trying to regularly contact and interact with each other for sharing knowledge and information of research, through different conferences, seminars, workshops and development programs, etc. The RKC is most important community and its individual member is a knowledge asset of the university. Since, till date there is no any such kind of community formed, or properly organized in the universities. Formation of RKC can make healthy environment for research in the university, by providing technology, tools, and various platforms for RKC, to inculcate the importance of research knowledge in students. This would be helpful for the high quality research with more positive and proper output to the nation building. III. Knowledge management A. What is knowledge management? There are many definitions of Knowledge Management [3], here we have combined the Knowledge Management and Organization Management literature to define Knowledge Management as the process of selectively applying knowledge [4] from previous experiences of decision-making to current and future decision making activities with the express purpose of improving the organizationâ&#x20AC;&#x2122;s effectiveness [5]. This definition allows us to define the goals of Knowledge Management [3] as: 1. Identify Critical Knowledge 2. Acquire Critical Knowledge in a Knowledge Base 3. Share the stored Knowledge 4. Apply the Knowledge to appropriate situations 5. Determine the effectiveness of using the applied knowledge 6. Adjust Knowledge usage to improve effectiveness.

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B. Why do we need knowledge management for RKC? The idea that an organization’s most valuable resource is the knowledge of its people and basically Knowledge management is based on this idea. This idea is not new – organizations have been managing “human resources” for years. What is new? is the focus on knowledge[13]. This focus is being driven by the accelerated rate of change in today’s organizations and in society as a whole. Knowledge management recognizes that today nearly all jobs involve “knowledge work” [13] and so all staff means “knowledge workers” to some degree or another meaning is that their job depends more on their knowledge than their manual skills [9]. In the same manner here, in this study university as an organization, and research knowledge community is most important part of the university. Research output and Research scholars are main knowledge resources in the university. The individual's set of research knowledge in the university, is major and important asset of the university. This knowledge set of researchers should be managed, means that in real sense the Research Knowledge Management of the university. The Research Knowledge Management means that creating, sharing and using the research knowledge, these activities are the most important activities, every person in every institution under the constituent of university should know about this. Do we know everything we need to know or are there gaps in our knowledge? Of course there are gaps in research knowledge. The current modernization programs requires us to let go of what we knew and to learn and apply new knowledge in research. In this concern the following questions should be asked to RKC's individual members [8]:  Do we share what we know?  Is the knowledge of individual researchers available to the university?  Is the research knowledge of organizations available to all?  Not at present. How many times have we lost valuable knowledge and expertise when a staff member moves on?  How many times have we “reinvented the wheel” when we could have learned from someone else’s experience?  Do we use what we know to best effect? Not always.  Clearly our knowledge has not always been applied to best effect, do we think? We lacked the time or resources to do anything about it?  How many times have we implemented a new initiative, only to find we reverted back to the “old way” a few months later? The answers for this question would be the basic reasons for forming RKEN, using RKMS as a medium of communication for RKC. In terms of how that can be done, the processes of knowledge management are many and varied [8]. As knowledge management is a relatively new concept [4], organizations are still finding their way and so there is no single agreed way forward or best practice. This is a time of much trial and error. Similarly, to simply copy the practices of another organization would probably not work because each organization faces a different set of knowledge management problems and challenges. Knowledge management is essentially about people – how they create, share and use knowledge, and so no knowledge management tool [9] will work if it is not applied in a manner that is sensitive to the ways people think and behave. Knowledge Management and few definitions: “The creation and subsequent management of an environment, which encourages knowledge to be created, shared, learned, enhanced, organized and utilized for the benefit of the organization and its customers.” Abell and Oxbrow, tfpl Ltd, 2001. “The capabilities by which communities within an organization capture the knowledge that is critical to them, constantly improve it, and make it available in the most effective manner to those people who need it, so that they can exploit it creatively to add value as a normal part of their work.”BSI’s A Guide to Good Practice in KM. IV. Research Knowledge and Its Resources Research Knowledge means the information collected, created, generated through analysis and stored in minds of researchers in form of experiences, stories, conclusions and suggestions. Research Knowledge is conclusive information [10] that forms the basis for thoughts, actions, and beliefs.” It includes the theories and experiments of scientists, who collaborate to establish our knowledge of the external world. Research knowledge is a scientific knowledge which is generated by proper scientific Research methods. Research Knowledge is having its own value in the research field and interdisciplinary research too. Research Knowledge is majorly concerned with individual researchers, is the information created and stored in their minds like their experiences, actions, thoughts, observations and beliefs during the time of research work activities. When we represent the knowledge means that, we can store and manage the explicit knowledge. The data can be managed in the different forms like text, images, charts, etc. but really managing the tacit knowledge is very different, we can try to represent this type of knowledge using the few formats, like we can store videos, audios, images and stories about the exact experiences during their research experiences. These data would be managed in data server, which can make available to the researchers to share the knowledge or tacit knowledge. This would help the researchers to prepare future plan in their research

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V. Research Knowledge Exchange Network (RKEN) During this study, after discussion with researchers, views comes out, RKC would be a effective community. RKC formation, organization, implementation is important task for the universities. The main purpose of RKC formation, is to provide common platform and tools to the research scholars for the exchange of Set of Research Knowledge (SRK). SRK related to different research tools and techniques, Statistical Analysis tools, various research disciplines, research methodologies, research streams, etc. The short comings of SRK in RKC would be fulfilled within itself as community. Researchers will get idea about the SRK of individual research scholars and related problems would be solved easily. RKC would arrange different activities for the sharing of knowledge and ideas related to their research, which will help them for further development. Hence for communication, sharing, exchange of ideas, learning new tools, university research environment, would require the proper network. The network, in the sense of Person to Person P2P, Person to Application P2A, Application to Application A2A interaction for the closeness of RKC. This network, basically exchanging Research Knowledge among RKC, is nothing but Research Knowledge Exchange Network (RKEN).

Fig. 1.2: Research Knowledge Exchange Network The RKEN is amalgam of each individual research scholar, Colleges, Institutes, Research Centres, set of infrastructure, hardware, networks, Research Knowledge Management System (RKMS), and various required tools and techniques. As shown in Fig. 5.1, at higher hierarchy level, UGC (University Grant Commission), University and Other Universities, treated as a controlling body for the Research Activities. These bodies will control the all protocols, activities & procedure of the RKEN. The next level is University Departments, Research Institutes / Colleges / Centres; these are working as a facilitators for individual research scholars. Individual Members of RKC are the end users of this RKEN, who are get benefited by this network. The main tool which should be developed, for the implementation of the RKEN, is the Research Knowledge Management System (RKMS). The RKMS is the main concept, which would be a interface or important media tool for the RKC – all members. RKC can directly communicate with each other using RKMS through the Internet. The RKEN would be a network system in the university, which will encourage RKC members to think about, and articulate the benefit and value to the RKC. RKEN would be a really good system for organizing and formation of RKC. In true sense RKC would be active with the proper working RKEN. The RKMS would provide a proper support for the RKC, for communication, sharing of research knowledge, etc. The next task for the development of RKC is to define the architecture of RKMS. Through the analysis of different needs and requirements of researchers. Challenges faced by researchers, the actual RKMS should be designed, which would be a complete solution for the researchers. All queries, questions and knowledge gaps should be filled with the use of RKMS in the RKC. Once the RKMS is developed and implemented, then for the awareness and importance of RKMS in RKC , we should provide the training for the researchers. Training program should include all aspects of the RKC like what is mission, goals and objectives of the RKC. How these would be achieved by the RKEN & RKMS. What are the Dos & Don’ts with system? How RKC and individual researchers would get benefits from this system. What different tools are available in the system for the communication, searching, sharing, and presentation of research knowledge. All important points, training program should be developed for all the members of RKC. After implementation and training of RKMS, RKEN would be in working conditions for the benefit of RKC. The RKC would be working in real sense, by the use of RKEN and RKMS in university and research scholars. The research scholars would come closer for their future research activities without any geographical distance barrier and they can exchange the ideas for mutual benefits. The Research Scholars could get all information and knowledge on a single point and which is authorized

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system, Research Scholars can get knowledge related to research activities, conferences, seminars, Ph.D. Vivas, research presentations and so on. They can easily search contact for any SRK and can contact with each other to get solutions. VI. Conclusion, Recommendations and Future work A. Conclusion This study is specifically focused on the Knowledge Management approach in research. An idea of developing RKC within university is for the research scholars so that the communication and knowledge gap among them can be avoided. The formation and development of RKEN and the implementation of RKMS would be a major task in the development of RKC. The RKEN formation, which is in progress, would be beneficial for RKC to great extent. RKMS would be a complete system for the research scholars for their exchange of SRK- for designing, disseminating, sharing and managing various research related protocols, rules, regulations, ideas, etc. B. Recommendations The following recommendations are suggested: 1) Any university can run the pilot project for the development of RKC, because till date no university has designed such RKC. 2) The proper Knowledge Exchange Network must be formed by research and redesign of the RKEN. The RKEN formation would be a continuous and repetitive process of diagnosis, analysis, implementation and verification. 3) The Knowledge Management Systems must be studied and challenges of research scholars should be investigated before the design of RKMS. 4) The RKC would be incomplete without formation of RKEN and without implementation of RKMS; hence all the activities should be started immediately for further development of RKC. 5) A proper model should be designed for both RKMS and RKEN. 6) The success of this system would be a major achievement for the respective university. Thsi can be an initiaion of integration of university, UGC or national level RKC. C. Future Work This is a conceptual study for developing a RKC, hence it is at infant level. Several process have to be carried out in the implementation of RKEN. The basic stages for developing the RKC includes, 1. Propose RKC to university authorities, 2. analysis and design of the RKEN and RKMS 3. developing Architecture Model for RKMS, 4. Software engineering for RKMS 5. evaluation of RKMS and its benefits to RMC. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Benchmark Magazine, Fall Issue. Wenger, E. (1998). Communities of Practice. Learning as a social system, The Systems Thinker , 9(5), 2-3. Wenger, E. (2007). Learning in communities of practice: a journey of the self. Witt, N., McDermott, A., Peters, M. & Stone, M. (2007). A knowledge management approach to developing communities of practice amongst university and college staff. In ICT: Providing choices for learners and learning. Proceedings ascilite Géraud Servin July 2005, “ABC of Knowledge Management”, by NHS National Library, Team of KM Researchers 2010, “Open Journal of Knowledge Management”, Published by Community of Knowledge, Cecil C Chappelow 8, 2004, “The Future of Knowledge Management”. An international Delphi Study, Journal of Knowledge Management, F M Ross Armbrecht Jr; Richard B Chapas Jul/Aug 2001, Knowledge management in research and development; ABI/INFORM Global Paul D. Leedy, Jeanne Ellis Ormrod 2010, 9 th Edition, “Practical Research: PLANNING Remy Magnier-Watanable, coroline Berrton & Daisenoo 2011(9),”A study of Knowledge Management enablers across countries”, KMR&P. Richard Baskerville & Alina Dulipovici 2006(4),”The Theoretical foundation of Knowledge Management”, KMR&P. Jon Landete Rodriquez, Anuro & Stanishy. 2004(10), “Knowledge Management Analysis of the Research and Development & Transference Process at HERO’s” Journal of Universal Computer Science. Team of R&D Leaders 2001, “Knowledge Management in Research and Development”, Industrial Research Institute. Victer Higo Dario Alejandro, 2012, “Knowledge Model for Research Projects Masters Program”, World Academy of Science, Engineering & Tech. Andrey Kryshtafovich Sept 2013, “The Theory of Knowledge and its use in Knowledge Management practice”, 3rd European Knowledge Management practice”, KM Summer School”.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Brain Actuated Wheelchair Using Brain Wave Sensor Aswathy M M.E. Embedded System Technologies, Department of Electrical and Electronics Engineering, Anna University, Nehru Institute of Engineering and Technology, Coimbatore - 641105, Tamil Nadu, INDIA. Abstract: This project discussed about a brain controlled mobile robot based on Brain computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. The intention of the project work is to develop a mobile robot that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can move a robot according to the human thoughts and it can be turned by blink muscle contraction. By using this brainwave concept executed in wheelchair the handicap will easily controls wheel chair. Keywords: Brain computer interface, brain wave sensor, EEG, Bluetooth, mat lab I. Introduction The human brain is made up of billions of interconnected neurons, the patterns of interaction between these neurons are represented as thoughts and emotional states. Every interaction between neurons creates an electrical discharge, alone these charges are impossible to measure from outside the skull. However, the activity created by hundreds of thousands concurrent discharges aggregates into waves which can be measured. Different brain states are the result of different patterns of neural interaction. These patterns lead to waves characterized by different amplitudes and frequencies, for example waves between 12 and 30 hertz, Beta Waves, are associated with concentration while waves between 8 and 12 hertz, Alpha Waves, are associated with relaxation and a state of mental calm. Here a wheelchair is controlled automatically according to the brain signal. The brain signals are collected using a brain wave sensor. Using these signals wheelchair can be moved. This brain wave sensor consists of 3 main parts. They are dry electrodes, signal conditioning circuit and inbuilt RF transmitter. Dry electrodes are used to absorb the brain waves. This signal is analog in nature. For further processing these analog signals should be converted to digital form. Signal conditioning stage will do this conversion. The next part is inbuilt RF transmitter. It converts this digital signal into packet of data. This data packet is transmitted through Bluetooth transmitter. Here the received data packets are processed using mat lab tool. M script or math script is an interface program for brain wave. The mat lab output is a graph showing attention & blinking in y-axis and time in x-axis. Attention means the robot is moving forward. Blinking is used for rotation of robot. This output is given to robotic module for automatic movement of robot. II. Literature survey In the paper involving young children in the development of a smart pediatrics wheelchair, Independent mobility is crucial for a growing child and its loss can severely impact cognitive, emotional and social development. Unfortunately, powered wheelchair provision for young children has been difficult due to safety concerns. But powered mobility need not be unsafe. In the paper brain controlled wheelchair a robotic architecture, Independent mobility is core to being able to perform activities of daily living by oneself. However, powered wheelchairs are not an option for a large number of people who are unable to use conventional interfaces, due to severe motor disabilities. For some of these people, nonâ&#x20AC;&#x201C;invasive brain computer interfaces (BCIs) offer a promising solution to this interaction problem and in this article we present a shared control architecture that couples the intelligence and desires of the user with the precision of a powered wheelchair. Here a master control of the wheelchair using an asynchronous motorâ&#x20AC;&#x201C;imagery based BCI is used.

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In the paper brain controlled telepresence robot by motor disabled people, a rather complex task as the robot is continuously moving and the user must control it for a long period of time (over 6 minutes) to go along the whole path. These two users drove the telepresence robot from their clinic more than 100 km away. Remarkably, although the patients had never visited the location where the telepresence robot was operating, they achieve similar performances to a group of four healthy users who were familiar with the environment. In particular, the experimental results reported in this paper demonstrate the benefits of shared control for braincontrolled telepresence robots. It allows all subjects to complete a complex task in similar time and with similar number of commands to those required by manual control. III. Proposed method description The main purpose of this project is to design a wheelchair for severely disabled person move them voluntarily. Their movement of wheelchair can be controlled with their own mind waves. A Neuro sky product called brain wave sensor is used for this purpose. The brainwave sensor consists of dry electrodes which collect raw brain signals, they are analog in nature. The signal conditioning unit converts this analog signal into digital form and transmits through an inbuilt Bluetooth transmitter. A Bluetooth receiver is connected to the computer where these raw brain signals are extracted and processing using mat lab platform .M script /math script is used to interface brain wave output with mat lab and produce output waveform with respect to time. The mat lab command window shows the signal strength of attention and blink signals. The output waveform shows the attention & blink signals in x-axis and time in y-axis. Attention signal strength is high then wheelchair starts forward motion and blink signal strength is high then wheel chair rotates clockwise. These mat lab output is given to robotic module. Robotic module consists of dc motor which moves forward, left or right according to brain signals. Figure 1: Block diagram of transmitter section

Figure 2: Block diagram of data processing unit

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Figure 3: Block diagram of receiving section

Mat lab code %function PSEMB301 %run this function to connect and plot raw EEG data %makes sure to change portnum1 to the appropriate COM port Clear all Close all Data BLINK = zeros (1,256); Data ATTENTION = zeros (1,256); Data MEDITATION = zeros (1,256); % X= zeros (1,256); portnum1 = 4; %COM Port # comPortName1 = sprint ('\\\\.\\COM%d', portnum1); %TG_BAUD_57600 = 57600; TG_BAUD_115200 = 115200; TG_STREAM_PACKETS = 0; %TG_DATA_DELTA = 5; %TG_DATA_POOR_SIGNAL = 1; %TG_DATA_ATTENTION = 2; %TG_DATA_BLINK_STRENGTH = 37; %TG_DATA_BATTERY = 0; TG_DATA_BATTERY = 0; TG_DATA_POOR_SIGNAL = 1; TG_DATA_ATTENTION = 2; TG_DATA_MEDITATION = 3; TG_DATA_RAW = 4; TG_DATA_DELTA = 5; TG_DATA_THETA = 6; TG_DATA_ALPHA1 = 7; TG_DATA_ALPHA2 = 8; TG_DATA_BETA1 = 9; TG_DATA_BETA2 = 10; TG_DATA_GAMMA1 = 11; TG_DATA_GAMMA2 = 12; TG_DATA_BLINK_STRENGTH = 37; TG_DATA_READYZONE = 38; %load think gear dll Load library ('Thinkgear.dll'); Fprintf ('Thinkgear.dll loaded\n'); %get dll version %dllVersion = calllib ('Think gear', 'TG_GetDriverVersion'); % fprintf ('ThinkGear DLL version: %d\n', dllVersion); %%% Get a connection ID handle to ThinkGear connectionId1 = calllib ('Think gear', 'TG_GetNewConnectionId'); % fprintf (‘Connect If (calllib ('Thinkgear','TG_EnableBlinkDetection', connectionId1, 1) =0)

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Disp ('blinkdetectenabled'); End; %record data Disp ('BLINK = '); Disp (data_BLINK (j)); If (data_BLINK (j) > 40 ) % if(data_BLINK(j) < 95) Blink = Blink+1; data_ATTENTION (k) = calllib ('Thinkgear','TG_GetValue', connectionId1, TG_DATA_ATTENTION); Disp ('ATTENTION = '); Disp (data_ATTENTION (k)); % plot (data_ATTENTION) % axis ([0 100 0 100]) % draw now; Blink=0; Axis ([0 100 0 200]) Draw now; % end End End %%end %disconnect Calllib ('Think gear', 'TG_FreeConnection', connectionId1); IV. Simulation Analysis using matlab A neurosky product called brain wave sensor is used for collecting brain signals. A Bluetooth receiver is connected to the computer where these raw brain signals are extracted and processing using mat lab platform. M script /math script is used to interface brain wave output with mat lab and produce output waveform with respect to time .The program is written in program window and run the program & output is taken from mat lab platform which consists of 2 signals .The mat lab command window shows the signal strength of attention and blink signals. The output waveform shows the attention & blink signals in x-axis and time in y-axis. Attention signal strength (red color) is high then wheelchair starts forward motion and blink signal strength(black cross mark) is high then wheel chair rotates clockwise. Figure 4: Mat lab output window

V. Conclusion and future scope From this project robotic vehicle can be controlled using brain wave sensor. I realized this project will be useful in medical field. This can be used for assisting disabled persons to move. The advantage of thought controlled wheel chairs are that they respond to commands much faster and that patients who have lost the ability to speak may utilize them. The main purpose of this project is to design a wheelchair for severely disabled person to move them voluntarily. There are different ways for operating a wheelchair for eg: voice controlled, using gestures, using eye movement or using joystick etc. But these types of wheelchair canâ&#x20AC;&#x2122;t be used for stroke patients or paralyzed patients. Here patients can control their wheelchair using their own brain signals. For that purpose a brain wave sensor is used. Mainly brain wave sensor is used for monitoring patient health condition to find is there any abnormalities. This system can also be used to identify the correct person with biometrics.

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VI. References [1] [2] [3] [4] [5] [6] [7]

[8] [9] [10] [11] [12] [13] [14] [15]

A. Cichocki and K. Choi(2008) “Control of a wheelchair by motor imagery in real time,” in Proc. 9th Int. Conf. Intell. Data Eng. Autom. Learning, pp. 330–337. A. Delorme and S. Makeig(2004), “EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics,” J. Neurosci. Methods, vol. 134, pp. 9–21. A. H. Adom,C. R. Hema, M. P. Paulraj, S. Yaacob, and R. Nagarajan(2010),“Robot chair control using an asynchronous brain machine interface,” in Proc. 6th Int. Colloquium Signal Process. Its Appl., pp. 21–24. A. Graser and H. Cecotti(Mar. 2011) “Convolutional neural networks for P300 detection with application to brain–computer interfaces,” IEEE Trans.Pattern Anal. Mach. Intell., vol. 33, no. 3, pp. 433–445. C. D. L. Cruz, T. F. Bastos-Filho, and R. Carelli(2011), “Adaptive motion control law of a robotic wheelchair,” Control Engineering Practice,vol. 19, pp. 113–125. C. Escolano, J. M. Antelis, and J. Minguez(Jun.2012), “A telepresence mobile robot controlled with a noninvasive brain– computer interface,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 3, pp.793–804. 30 C. Guger, E. Grunbacher , E. Opisso,J. Medina, M. Bruckner, R. Ortner, R. Pruckl and U. Costa(Apr. 2011), “Accuracy of a P300 speller for people with motor impairments:Acomparison,” in Proc. IEEE Symp.Comput. Intell.,Cognitive Algorithms, Mind, Brain, pp. 1–6. D. J. McFarland, G. Schalk,J. R. Wolpaw, N. Birbaumer and T. Hinterberger(Jun. 2004),“BCI2000: A general-purpose brain– computer interface (BCI) system,”IEEETrabs. Biomed. Eng., vol. 51, no. 6, pp. 1034–1043. F. Colas, J. d. R. Millan, R. Chavarriaga, R. Siegwart and X. Perrin(2010),“Brain-coupled interaction for semi-autonomous navigation of an assistiverobot,” Robot. Autonom. Syst., vol. 58, no. 12, pp. 1246–1255. F. Velasco-Alvarez, L. da Silva-Sauer ,R. Ron-Angevin, S. Sancha-Ros(Jun./Jul. 2011), “A two-class self-paced BCI to control a robot in four directions,” inProc. IEEE Int. Conf. Rehabil. Robot. , pp. 1–6. G. Pires, M. Castelo-Branco and U. Nunes(2011), and, “Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis,” J. Neurosci. Methods, vol. 195, pp. 270–281. 12. H. S. Hu, J. Q. Ganand T. Geng(2010), “A self-paced online BCI for mobile robot control,” Int. J. Adv. Mechatronic Syst., vol. 2, no. 1/2, pp. 28–35. 31 H. Soh and Y. Demiris(2011), “Involving young children in the development of a safe, smart paediatric wheelchair,” ACM/IEEE HRI-2011 Pioneers Workshop, Lausanne. J. Long, Y. Li, T. Yu, and Z. Gu(Jan. 2012), “Target selection with hybrid feature for BCI-based 2-D cursor control,” IEEE Trans. Biomed. Eng., vol. 59,no. 1, pp. 132–140. 15.P.L. Lee, H.C. Chang, T.Y. Hsieh, H.T. Deng, and C.W. Sun(Sep. 2012), “A brain-wave-actuated small robot car using ensemble empirical mode decomposition-based approach,” IEEE Trans. Syst., Man, Cybern. A:Syst. Humans, vol. 40, no. 5, pp. 1053–1064.

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ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Review on E-Learning Effectiveness Models A.Bindhu1, Dr. Hansa Lysander Manohar2 Research scholar (Department of Management Studies), 2 Associate Professor (Department of Management Studies), 1,2 Anna University, Chennai, Tamil Nadu, INDIA 1

Abstract:

E-learning systems are gaining momentum in the field of education and training. Corporations have recognized the importance of this new way of training to reduce costs and to ameliorate trainees’ competitiveness. Educational institutions are also introducing innovative e-learning programs to expand the scope of their curriculum. India is also witnessing an increase Government initiatives to promote e-learning. This scenario necessitates justification of the huge investments in e-learning systems. Hence, there is a pressing need to evaluate the effectiveness of an e-learning system. As research in the area of e-learning evaluation is burgeoning, an attempt is made to review diverse perspectives on elearning effectiveness. Models formulated for measuring e-learning effectiveness based on DeLone and McLean IS success model, Technology acceptance theory, Social cognitive theory and Media richness theory have been reviewed in this paper to gain an understanding of the measures of e-learning effectiveness and its antecedents. An insight into this area would facilitate e-learning companies and academicians to design and develop a successful e-learning environment. Keywords: E-Learning, effectiveness, DeLone and McLean IS success model, Technology acceptance theory, Social cognitive theory, Media richness theory.

I. Introduction Developed countries are financially competent to provide grants and special institutional facilities to support all levels of education. But in developing countries, not only there is a lack of such facilities, but there is a strict compulsion for youngsters to extend financial backing for their family. Hence the situation learn while you earn which is substantiated by the increasing enrollments in distance education. This paves way to an opportunity for education supported by internet. E-learning refers to any type of learning situation in which instructional context is delivered through the use of computer networked technology, primarily over an intranet, or through the Internet, where and when required” (Bondarouk & Ruël, 2010). E-Learning is expected to expand the scope of education. Electronic Learning congregates knowledge by transcending geographical barriers and provides learners with an engaging, intuitive, collaborative and personalized learning experience. Homing a large number of digital natives, Indian education system provides a huge room for technology based education. Corporations are increasingly adopting e-learning programmes to facilitate talent management. According to Global Industry Analysts, corporate training is a $200-billion industry out of which eLearning represents $56.2 billion and is expected to grow into a $107 billion market by 2015. Being the future of education and training, it is essential to enhance the effectiveness of an e-learning system. It is vital to understand the dimensions and drivers of elearning effectiveness. Identifying and specifying these items would define the features of an e-learning system and would imply the directions of designing and developing efficient e-learning systems. This understanding will also further utilization of novel IT tools in expanding the reach of technology based education. II. E-Learning effectiveness Learning effectiveness is defines as the quantity of knowledge, skills and behavior learned in a training session and their effective application by trainees on their job. Baldwin and Ford (1988) An E-Learning environment has in it a set of stakeholders differing in their expectations regarding the outcomes of an e-learning programmed. Hence the success measures may differ for different courses seeking different objectives - ROI for the organization or institution, Level of knowledge gained for the trainers,- User satisfaction for the employees/students. Accordingly the factors leading to these success measures also vary. A number of factors like system quality, content quality, content delivery, continuous feedback have to be incorporated into the system to attain the desired results So rather than trying to come up with an exhaustive list of measures and factors, it would be better to enlist the most generalisable dimensions. Several studies been conducted adopting different methodology and have revealed numerous dimensions and antecedents. Various Information systems (IS) theories are adopted by different researchers. As of yet a universally acceptable exhaustive model for evaluating the effectiveness of e-learning is yet to be developed. A review of relevant literature may be a precedential to develop a comprehensive framework for evaluating elearning effectiveness.

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III. Delone and McLean IS success model approach In order to provide a comprehensive understanding of IS success; DeLone and McLean have identified and explained six critical dimensions (systems quality, information quality, use, user satisfaction, net benefits) to evaluate the success of information systems. They developed a multidimensional model depicting the interdependencies between the success dimensions in 1992. Many scholars working in this area attempted to extend or respecify this model. Based on the feedback obtained from different researchers, the model was updated by the original authors a decade later (2003). The dimensions specified in the model measure the technical, semantic and effectiveness success of an IS. Table I: IS success dimensions Dimensions

Definition

System quality

The desired characteristics of the system such as usability, reliability, adaptability and response time

Information quality Service quality Usage User satisfaction Net Benefits

The content in terms of completeness, relevancy, understandability and security The overall support as provided by the IS department like assurance and responsiveness The nature of use, navigation patterns, number of site visits and transactions The users experience in using the system The positive and negative impact on the stakeholders such as cost savings, expanded markets, time savings, etc

Since an e-learning system is a special information system, the D&M IS success model has been widely adopted for evaluating the e-learning effectiveness. A. ELSS Model(2007) The model was conceptualized by Wang, Wang, et al was based on the widely cited Delone and McLean (2003) IS success model with six dimensions. A multidimensional model has been developed and validated for evaluating the success of an e-learning system. The e-learning systems success construct was empirically measured from the perspective of the e-learner. The items underlying the dimensions were obtained through a detailed review of literature. Table 1: Dimensions and items in the ELSS model Dimensions System quality

Information quality Service Quality Use User satisfaction Net Benefits

Items System availability, Ease of use, User-friendliness, Interaction, Personalized information presentation, Attractive features to appeal the users, Fast information access Right information, Responsiveness, Relevancy, Adequacy, Understandability, Up-to-Date Online assistance, Interaction, Availability, Responsiveness, Support Frequent usage, Voluntary usage, Dependency Attitude, Perceived utility, System satisfaction Improved job performance (individual), Enhanced competitiveness, Quick response to change, Better products or services to customers, Cost savings, Shortened product cycles, increased return on investment, goal achievement (organization)

The above items were measured on a 7 point Likert scale in the ELSS instrument. The responses were collected from employees of eight organizations in Taiwan that had implemented e-learning systems. The empirical analysis showed that the instrument had a high reliability and consistent factor structure. The result also emphasize that the six dimensions of success are interrelated as suggested by DeLone and McLean and hence equal importance has to be given to improve the system along all the six dimensions. It is suggested that the validated instrument could be used for making an overall assessment as well as for comparing different e-learning systems. Inspite of its applicability across various e-learning systems, the authors suggest future research to test the causal relationship between the dimensions. B. Structural model depicting e-learning success dimensions and its determinants (2009): With insights gained from previous literature on e-learning systems success, a multidimensional measure of elearning success is presented in this paper. Adapting the DeLone and McLean IS success model, a structural model depicting the main determinants of e-learning success has been proposed. Table 2: Dimensions measuring e-learning success Dimensions System use by learners Learner satisfaction Learning effectiveness Continuance intention

Definition The extent to which the e-learning system is used by the instructor to instruct and motivate his students The extent to which the learners are contented with the system based on their experience in using the system An evaluative measure of student learning reflected by their performance The intention of the learners to use e-learning components in the future courses they take up

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The structural model incorporated Content quality, System quality, Service quality and Perceived task value as the determinants of the success measures. Table 3: Determinants of e-learning success Determinants Content quality System Quality Service quality Perceived Task Value

Definition Value of the e-learning content Ease of use, Reliability and Responsiveness of the technology Amount of Instructor and Technical support The learners perception of the significance of being successful in the course

The model posits that system quality, content quality and service quality influences system use and learner satisfaction which in turn has an impact on learning and continuance intention. The learner perception about success is said to affect satisfaction and intention to continue relying on online courses. C. E-Learning success model: (2009) Lee post reports the knowledge acquired while developing an online course for business undergraduates. The focus is to integrate and formulate a holistic model for evaluating e-learning. Based on the experience of designing and delivering the online course, the author has proposed and a model empirically validated an elearning success model. The model has been developed by adapting the Delone and McLean Model (2003) with system quality, Information quality, Service quality, Use, Net benefits and User satisfaction as the dimensions of success. The study follows a process approach and hence hypothesizes that the overall e-learning systems success depends on the success achieved at every stage of system development – design, delivery and outcome analysis. Therefore the success dimensions have been categorized as follows: Design stage – System quality, information quality and service quality Delivery stage – Use Outcome stage – User satisfaction and Net benefits System quality corresponds to the ease of use, user friendliness, stability, security, speed and responsiveness of the e-learning environment. The quality features of the course content such as organization, presentation, clarity, usefulness etc are measured in the information quality dimension. The communication between the instructor and student is evaluated in terms of promptness, responsiveness, fairness, knowledge and availability in the service quality dimension. The Use dimension assesses the degree to which the course elements like case studies, PowerPoint slides, excel tutorials, practice problems etc are being actually utilized by the learners. The outcomes of the e-learning systems are measured in terms of net benefits and user satisfaction. The Net benefits represent both the positive aspects (improved learning, time savings, and academic achievements) and negative aspects (isolation, technological dependence). The user satisfaction is determined by the learners overall contentment and success facilitated by the e-learning system. The course feedback survey result reported that the students believed that e-learning provided them with greater control over study materials and reduced the time to learn. The findings of the pilot study states that the student’s indifferent attitude towards e-learning would be a barrier to successful development of e-learning initiatives. Thus Online readiness of the students is found to be critical to the success of e-learning. The overall success of an e-learning system depends upon the success of the three development stages. The success of system outcome stage depends upon success in the delivery stage which in turn relies on the success of the design stage. Realizing that the incorporation of instructor and institutional perspectives in the proposed model would add on value, the model was extended. Institutional support for instructors in terms of sound infrastructure, workshops, technical and pedagogical aids were included in the extended system. The impact of incorporating adopting these were evaluated in terms of cost saving, increased enrollment for the institution etc. Consequently institutional outcome is added as the seventh success dimension along with the six dimensions as proposed by DeLone and McLean IS success model. This research has contributed an e-learning model that would guide the design, development, and delivery of successful e-learning initiatives. IV. Kolb Learning Style Approach: David Kolb proposed the learning styles model based on the Experiential learning Theory. According to Kolb's model, the ideal learning process happens through a four stage learning cycle – Concrete experience-Reflective Observation-Abstract conceptualization – active experimentation. Effective learning is seen when a person progresses through a cycle of four stages: of (1) having a concrete experience followed by (2) observation of and reflection on that experience which leads to (3) the formation of abstract concepts (analysis) and generalizations (conclusions) which are then (4) used to test hypothesis in future situations, resulting in new experiences. Kolb views learning as an integrated process with each stage supporting and impacting the following stage. Based on this learning cycle Kolb defines four learning distinct styles – Diverging (feeling and

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watching – Concrete experience + Reflective Observation), Assimilating (watching and thinking - Abstract conceptualization + Reflective Observation, Converging (doing and thinking - Abstract conceptualization + active experimentation) and Accommodating (doing and feeling - Concrete experience + active experimentation). Kolb explains that different people follow different learning style based on their basic cognitive structure, educational experiences and social environment. Kolb’s model implies that learning opportunities should be designed and developed in such a way as to facilitate the learners to adapt their preferred learning style through the learning cycle. Hu, Paul, et al (2005) conducted a longitudinal quasi field experiment to compare the learning effectiveness and outcomes produced by e-learning with that of conventional face-to-face learning. Adapting the Learning style model by Kolb this study analyses the impact of learning style of an individual on the outcome improvements of e-learning. The study evaluates the comprehensive outcome measurements – learning effectiveness, satisfaction, personalized learning support, learning community support, overall course learnability and course content assessments. Table 4: Outcome measures Dimensions

Definition The learners perception about their learning achievement The learners feeling (good and positive) about taking up a elearning course The amount of control the learner has over the method of learning and selection of contents and the feedback provided about their learning progress The amount of course related interaction the learner has with the instructor and the amount of sharing (of knowledge) with the other students in the course The learner’s perception about the course delivery in terms of ease of understanding, consistency and clarity The validity of the course content with respect to relevancy, usefulness, adequacy and updation

Learning effectiveness Learning satisfaction Personalized Learning support

Learning community support

Overall course learnability Course content assessment

The analysis of the above mentioned measures suggests that e-learning resulted in higher learning effectiveness compared to the conventional face-to-face learning. Through increased Personalized learning support, e-learning improves both objective learning achievement and perceived learning effectiveness and results in favorable course content assessments. While weak learning community support reduces course learnability and hence results in lesser satisfaction compared to conventional learning. The analysis also statistically proved that the difference in the learning outcomes was attributed to by the learning style of individuals. Hence it is suggested that learning environments and materials to be customized based on the individual’s particular learning style. And future research is recommended to investigate the effects of learning style and incorporate it in an elearning system to enhance its benefits. V. Technology Acceptance Theory: The Technology Acceptance Model (TAM) is a leading IS theory used to measure the acceptance, use and success of a technology. The model explains the factors that influence the user’s decision pertaining to the usage of a new technology. In the original TAM model proposed by Davis (1985), the actual use of a system would be determined by the user’s attitude towards the system, which in turn would be influenced by two beliefs – perceived ease of use and perceived usefulness. TAM was continuously studied and extended by researchers. TAM 3 was proposed in the context of e-commerce that includes dimensions more apt in the context of elearning. Table 5: Dimensions of e-learning based on TAM Dimensions Perceived ease of use Perceived usefulness Computer self efficacy

Definition The degree to which an individual believes that using a particular system would be free of physical and mental effort The degree to which an individual believes that using the system would enhance his job performance The individuals belief about his ability to perform a task using the computer

Model of e-learning determinants (2013): Zaddem, 2013 conducted a research to identify the effect of five factors namely computer self efficacy, ease of use, perceived usefulness, interaction, and social presence on e-learning effectiveness. A theoretical model has been proposed with the factors based on Technology acceptance theory, social cognitive theory and Media richness theory. The framework includes computer self efficacy, ease of use, perceived usefulness, interaction, and social presence as influential factors of e-learning achievement which in turn affects e-learning transfer. Self efficacy denotes an individual’s belief about his/her capacity to work on the e-learning system to accomplish the learning tasks. Self efficacy is hypothesized to positively influence the learner’s perception of ease of use, perceived usefulness, interaction and learning achievement. Perceived ease of use is defined as “the

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degree to which an individual believes that using a particular system would be free of physical and mental effort.” (Davis, 1989). Clarity and understandability of the learning material along with the flexibility of interaction facilitated by the learning platform allows the learner more freedom to use the online learning platform. Ease of use is assumed to have a positive impact on perceived usefulness and learning achievement. Perceived usefulness is defined as “the degree to which an individual believes that using a particular system would enhance his or her job performance” (Davis, 1989). It is the learners feeling regarding the improvement their understanding of the course and increased interaction with the instructor leading to better learning. Interaction denotes the Instructions from the instructors to resolve problems and questions of the learners and the feedback given to the learners are the communication processes to be facilitated by an online learning environment. Learner’s achievement in an e-learning course depends upon the amount of face-to-face and email interactions between learners and instructors. Social Presence is the degree to which the learners are allowed to actively, sensitively, personally and socially connect with others (learners and instructors). An environment that supports social presence improves interaction. E-learning effectiveness is measured by the knowledge and skills acquired (e-learning achievement) by the learners during the training as well as on how effectively these are being practiced (e-learning transfer) on the job With a detailed review of literature the authors have identified the items corresponding to the factors and have formulated twelve hypotheses to study the relationship between the factors and the measures. Employees from nine Tunisian companies participated in a semi structured interview and responded to 50 items on a 5 point Likert scale. The causal relationships were analyzed using SEM and the proposed model was found to have a good fit. The study concludes that the factors influencing trainee’s e-learning achievement are perceived usefulness, perceived ease of use, face-to-face interaction, e-mail interaction and social presence while self efficacy has an indirect influence on learning achievement. Lesser efforts by the learners due to ease of use of an online platform tends to improve perceived usefulness and thereby results in better achievement. Thus self efficacy acts as an antecedent with perceived ease of use and usefulness act as mediating factors. Increased interaction between the trainers and trainees improved learning achievement. The mutual relationship between Social presence and interaction (face to face and E-mail) was verified to be true. Greater the feeling of interactivity lesser is the feeling of isolation. With improved social presence better is the communication process in the shared e-learning environment. Colliding with many other research findings, this study also revealed that Learning achievement influences learning transfer. The respondents of the study being employees the results cannot be generalized for other participants (students). And also the study limits the influential factors. Hence there is a scope to extend the model in light of different participants and other possible factors of e-learning effectiveness. VI. Learner satisfaction Dimensions and antecedents(2007): An empirical study was carried out by Sun P.C.et al to integrate the different factors discussed by various researchers into one holistic framework. The framework was developed and validated through a survey conducted among e-learning volunteers of two Taiwan universities. It included six dimensions with thirteen variables that were measured on a seven point Likert scale. Table 6: Learner satisfaction dimensions and variables Dimensions Learner Dimension

Instructor Dimension

Variables Learner attitude Learner anxiety Self Efficacy Responsiveness

Course Dimension

Instructor attitude towards e-learning Course flexibility

Technology Dimension

Course quality Technology quality Internet quality

Design Dimension

Perceived usefulness Perceived ease of use

Environment Dimension

Diversity in assessment Learners perceived interaction with others

Definition The learners feeling about partaking in e-learning The mental pressure and fear about using computers in e-learning The learners capability to assess their internet usage skills The learners perception about the prompt response from the instructor to solve problems encountered in the online course The learners perception that the instructor has a positive inclination towards elearning the learners perception of the efficiency and effects of adopting e-learning in their convenient timing the amount of virtual characteristic provided by IT tools to an e-learning course the learners perception of the quality and reliability of the IT tools and techniques used in e-learning the network quality and transmission speed the learners perception of the amount of learning affected by an e-learning system the minimal effort put by the learner in adopting an e-learning system the varied assessment tools and methods used to evaluate the learners learning efforts the level of information exchange between the learners, instructors and the course materials

It was presumed that the above mention variables had a significant impact on learner satisfaction. Accordingly hypothesis was formulated and tested using stepwise Multiple regression analysis. The analysis resulted in seven of the thirteen variables to have critical relationship with learner satisfaction. While learner’s computer anxiety had a negative impact on satisfaction, the remaining six variables namely

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instructor attitude towards e-learning, course flexibility, course quality, perceived usefulness, perceived ease of use and diversity in assessment had a positive influence on learner satisfaction. Although the tested framework incorporates most of the factors influencing e-learning success, the desired outcome is limited to use satisfaction alone. Hence the scope of the framework can still be widened by giving due consideration to other success measures like learning achievement, behavioral changes etc. VII. Md. Aminul Islam(2010) The researchers’ prime focus was to investigate the factors that influence the effectiveness of e-learning. Learner’s reaction and satisfaction, participation and interaction, familiarity with technology and Learning outcomes and achievement were identified as independent variables through pre survey research. Table 7: Variables influencing e-learning effectiveness Dimensions Learners reaction Learners satisfaction Participation Interaction Familiarity with Technology Measuring learning achievement

Definition A measure of the students feel about their e-learning experience A measure of students gain from the system the incorporation of all the concerned parties throughout from acquisition to evaluation the face to face contact between the students and the tutors Students competency to use the internet in order to access knowledge as e-learning includes online courses, e-mail, e-book etc the benefits obtained from e-learning course in terms of skills gained and the amount of cognitive learning

A survey was conducted among randomly selected students who have undertaken e-learning courses in their diploma and degree program. The selected variables were measured on a 5-point Likert scale. Multiple regression analysis was used to find out whether the independent variables have significant effect on e-learning effectiveness. Reaction and satisfaction were found to be the major factors affecting e-learning effectiveness. The analysis revealed that e-learning resulted in enhanced Learning achievement. The learners who were more familiar with computer and internet technology seemed to use the e-learning system more effectively. But the student’s participation through the online forum was not as efficient as in traditional learning, because face-to-face interaction is vital for the learning process. VIII. Conclusion and future work: This paper details only a few models on e-learning effectiveness. With the understanding gained from the literature, the dimensions suggested by DeLone and McLean model seems to have a better fit to e-learning effectiveness. Most of the studies have considered learning achievement and user satisfaction as the success measures of an e-learning system. System quality, Content quality, service quality and perceived usefulness are identified as the most relevant antecedents of e-learning effectiveness. Only one paper highlights learning style as an important determinant. Moving on to the items underlying these dimensions the list is endless and still needs refinement and consolidation. Therefore, the DeLone and McLean model could be used as a basis for developing an e-learning effectiveness model. The researcher intends to develop and validate a comprehensive model for e-learning effectiveness by expanding this model incorporating the identified variables. References [1] [2] [3] [4] [5] [6]

[7] [8] [9] [10] [11]

DeLone, W.H., and McLean, E.R. Information systems success: The quest for the dependent variable. Information Systems Research, 3, 1 (1992), 60–95. DeLone, W.H., and McLean, E.R. 2003. "The Delone and Mclean Model of Information Systems Success: A Ten-Year Update.," Journal of Management Information Systems (19:4), pp 9-30. Measuring e-learning systems success in an organizational context: Scale development and validation - Wang, Wang, et al. 2007 Sun, P. -C. et al., What drives a successful e-Learning? An empirical investigation ..., Co mputers & Education (2007), doi:10.1016/j.compedu.2006.11.007 Lee-Post, A. “e-Learning Success Model: an Information Systems Perspective.” Electronic Journal of e-Learning Volume 7 Issue 1 2009, (pp61 - 70), available online at www.ejel.org Samarasinghe, S.M. & Tretiakov, A. (2009). A multi-dimensional measure of e-learning systems success. In Same places, different spaces. Proceedings ascilite Auckland 2009. http://www.ascilite.org.au/conferences/auckland09/procs/samarasingheposter.pdf Rabeb Mbarek and Ferid Zaddem, “The examination of factors affecting e-learning effectiveness,” International Journal of Innovation and Applied Studies, vol. 2, no. 4, pp. 423–435, April 2013. Islam, A., et al. "Factors Affecting E-Learning Effectiveness in a Higher Learning Institution in Malaysia." Jurnal Pendidikan Malaysia 35.2 (2010): 51-60. Hu, Paul, et al. "Examining e-Learning Effectiveness, Outcomes and Learning Style: A Longitudinal Field Experiment." (2005). Chuttur, Mohammad. "Overview of the technology acceptance model: Origins, developments and future directions." (2009). Davis, Fred D. "Perceived usefulness, perceived ease of use, and user acceptance of information technology." MIS quarterly (1989): 319-340.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Protocol Based On Round Trip Delay and Paths for Sensor Node Failure Detection S. Sam Perinba Nayagan Department of Electrical and Electronics engineering Nehru Institute of Engineering and Technology, Coimbatore, Tamilnadu, INDIA. Abstract: The usage of Wireless Sensor Networks (WSNs) has been increased nowadays due to its vast advantages of being used in the industrial environment. Also the reduction in the cost of semiconducting devices has increased the usage of WSNs. The important thing to be noted while using the wireless sensor networks is to maintain its Quality of Service (QOS). Many sensors can be used in a WSN to increase its quality of service. But the increase in the number of sensors will have a danger of increase in the failure of sensor nodes, which indeed affects the quality of service of that particular sensor network. In order to maintain the quality of service of a WSN a protocol called Round Trip Delay protocol has been proposed here. The suggested protocol is experimented in WSN with six sensor node designed using ARM processor and Zigbee. Also this method is verified by simulating the WSN with large number of sensor nodes in NS2 Keywords: Quality of Service, WSN, ARM, Round Trip Delay. I. Introduction In the wireless sensor networks (WSN) more number of sensor nodes can be used to increase the quality of service (QOS). But the exercise to use large number of sensor nodes will increase the probability of sensor node failure. The output data from these faulty sensors is incorrect or else it will deviate vastly from the original value. This will eventually degrade the quality of service (QOS) of that WSN [5]. In any WSN the various reasons for the failure of sensor node are the failure of the battery, environmental effects or hardware or software malfunctions. So in all these circumstances a better Quality Of Service (QOS) can be achieved by discarding data from the faulty sensor nodes during the analysis [6], [7]. In order to do that an efficient and accurate detection of faulty sensor node is needed. II. Literature Review The faulty sensor nodes identification suggested in [8] is based on comparisons between neighboring nodes and dissemination of the decision made at each node. Algorithm proposed in this method canâ&#x20AC;&#x2122;t detect the malicious nodes. Cluster head failure recovery algorithm used in [9] to detect the faulty node has data loss problem, occurring due to transfer of cluster head. Path redundancy technique to detect faulty sensor node is suggested in [10] and [11]. Redundancy increases the energy consumption and reduces the number of correct responses in network lifetime. Excessive redundant paths in WSNs will slow down the fault detection process. In [12], link failure detection based on monitoring cycles (MCs) and monitoring paths (MPs) is presented. Three-edge connectivity in the network, separate wavelength for each monitoring cycle and monitoring locations are the limitations of this method. III. Proposed System The proposed method of fault detection is based on RTD time measurement of RTPs. RTD times of discrete RTPs are compared with threshold time to determine failed or malfunctioning sensor node. Initially this method is tested and verified on six wireless sensor nodes, implemented by using ARM processor and Zigbee. In order to verify the scalability of this concept, WSNs with large numbers of sensor nodes are implemented and simulated in open source software NS2. Generalized model to determine the fault detection analysis time for WSNs by using discrete RTPs is suggested. Various experiments are performed in hardware and software based on RTD time measurements. Analysis time in all cases of fault detection is determined with the help of generalized model. Result analysis in hardware and software indicate that RTD time measurement results in both cases are quite equal, validating the real time applicability of this method. A. Round Trip Delay and Paths Analysis: Round trip delay time of the RTP will change due to faulty sensor node. It will be either infinity or higher than the threshold value. Faulty sensor node is detected by comparing the RTD time of RTPs with threshold value. The sensor node common to specific RTPs with infinity RTD time is detected as failed. If this time is higher

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than the threshold value then this senor node is detected as malfunctioning. Detection time of faulty sensor node depends upon the numbers of RTPs and RTD time.

Fig.1 Circular topology WSN with six sensor nodes

B. RTD Time Estimation: RTD time mainly depends upon the numbers of sensor node present in the round trip path and the distance between them. Proposed fault detection technique accuracy can be increased by reducing the RTD time of RTP. It can be decreased only by reducing the sensor nodes in RTP because the distance between sensor nodes in WSNs is determined by particular applications and can’t be decided. Selecting minimum numbers of sensor nodes in the RTP will reduce the RTD time. The round trip path (RTP) in WSNs is formed by grouping minimum three sensor nodes [13]. Hence the minimum round trip delay time (τRTD) of RTP with three sensor node is given by, τ RTD = τ1 + τ2 + τ3 (1) where τ1, τ2 and τ3 are the delays for sensor node pairs (1,2), (2,3) and (3,1) respectively [14]. Circular topology with six sensor nodes is shown in Fig. 1. Three consecutive sensor nodes in each RTP are almost at equidistance because of circular topology. As a result sensor node pair delays τ1, τ2 and τ3 will be equal. Let ‘τ’ be the uniform time delay for all sensor node pairs in RTPs i.e. τ = τ1 = τ2 = τ3. Round trip delay time for RTP with uniform sensor node pair delay is obtained by referring equation (1) as, τRTD = 3τ (2) This is the minimum RTD time of an RTP in WSNs. It is determined by the sensor node pair delay (τ), which is decided by particular application of WSNs, as it depends upon the distance between the sensor nodes. Hence the efficiency of proposed method can be improved only by reducing the RTPs in WSNs. C. Evaluation of Round Trip Paths: Faulty sensor node is detected by comparing the specific RTPs to which it belongs. More numbers of sensor nodes in the round path will reduce the RTPs created. But due to this individual sensor node will be present in more RTPs. While detecting faults, comparisons of all such RTPs become necessary. This will delay the fault detection process. The numbers of RTPs formed with ‘m’ sensor nodes is given by, P = N(N − m) (3) where P is the numbers of RTPs. Analysis time of fault detection method is the time required to measure the RTD times of all RTPs in the WSNs. It is the addition of all RTD times. The equation for analysis time with P numbers of RTPs is given by, τANL (M) = τRTD−1 + τRTD−2 + · · ·+ τRTD−P (4) τANL = τ RTD-i (5) RTD time of RTP will increase for additional numbers of sensor nodes. Referring (2), optimum value of RTD time of RTP is obtained by considering only three sensor nodes. All the RTPs in WSNs are formed by selecting only three sensor nodes (m = 3). Then the round trip delay for all RTPs is approximately same. i.e. τRTD = τRTD−1 = τRTD−2 = · · · = τRTD−P (6) Equation (5) can be written with the equal RTD time as, τANL= P × τRTD (7) Referring (2), analysis time can be written in terms of sensor node pair delay is as, τANL= P × 3τ (8) Minimum numbers of sensor nodes used to form RTP will create substantial numbers of RTPs. The maximum possible round trip paths PM, created by three sensor nodes per RTP are obtained by substituting m = 3 in (3) and is given by, PM = N (N − 3) (9)

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Analysis time τANL(M), to detect the faulty sensor node using maximum RTPs is obtained by referring (8) and (9) as follows, τANL (M) = N(N − 3) × 3τ (10) The fault detection analysis time will increase exponentially with increase in numbers of sensor nodes N in WSNs. Also the maximum numbers of RTPs produced are not required for comparison to detect the fault. Such selection of RTPs is not an adequate solution to speed up fault detection. Hence optimization of RTPs in WSNs is essential to speed up the fault detection. D. Optimization of Round Trip Paths: Fault detection by analyzing RTD times of maximum numbers of RTPs will require substantial time and can affect the performance. Therefore essential numbers of RTPs has to be selected for comparison purpose. Optimization of RTPs can be done by either linearly selecting the RTPs or by discretely selecting the RTPs. D.1. Linear Selection of RTPs: In order to reduce the RTPs in the fault detection analysis, instead of considering maximum numbers of RTPs, only few paths corresponding to the number of sensor nodes in WSNs are sufficient. We can select the RTPs equal to the numbers of nodes in WSNs to reduce the analysis time. RTPs selected in this way are called as linear RTPs because of the linear relationship between N and P. Individual sensor node is present in three linear RTPs. Hence comparison of such three linear RTPs is sufficient to detect the faulty sensor node. The linear RTPs in WSNs with N sensor nodes can be written as PL = N (11) where PL is the number of linear RTPs. Measurement of RTD times of such paths is essential. Referring (8) and (11), the analysis time τANL(L) for linear RTPs is given by, τANL (L) = N × 3τ (12) Linear RTPs selected will be higher for large value of sensor nodes N. This will not optimize the fault detection time in case of large size WSNs. Therefore, further reduction in the numbers of RTPs is must to increase the efficiency of proposed method. D.2. Discrete Selection of RTPs: In the first level of optimization the analysis time is curtail up to certain limit. Still the numbers of RTPs are high. For WSNs with large numbers of sensor nodes the fault detection time is significantly high. So again there is need to minimize the RTPs in WSNs. In the second level of optimization, numbers of RTPs are reduced by selecting only discrete paths in WSNs. Discrete RTPs are selected from sequential linear RTPs only. They are selected by ignoring the two consecutive paths, after each selected linear path. In this way RTPs are selected in discrete steps of three as each RTP consists of three sensor nodes. The equation to select the discrete RTPs in WSNs is given by, P D= Q + C (13) Q and C in above equation are expressed as below, Q = [N/m] C=

(14) (15)

where Q is the quotient, m is the numbers of sensor nodes in RTP, R is remainder, N is numbers of sensor nodes in wireless sensor networks and C is correction factor to be added. Correction factor will be 0 if remainder is 0 otherwise it is 1. Analysis time τANL (D) required for detecting fault in discrete RTPs is obtained by referring (8) and (13) as follows, τANL (D) = (Q + C) × 3τ (16) The numbers of sensor nodes used in RTP are three only i.e. m = 3. Equation (16) can be written in terms of N and m as τANL (D) = ([N/3] + C) × 3τ (17) Analysis of particular selected discrete path will be sufficient to monitor the fault. Selection of discrete RTPs will save the analysis time to a large extend. D.3. Comparisons of RTPs: After analysis time of discrete RTPs with variable numbers of sensor nodes from 3 to 10 for WSNs with 100 sensor nodes, it’s been found that the numbers of discrete RTPs required are only 34% as compared to linear selection. Hence analysis time curtail in discrete RTPs is up to 66% w.r.t linear RTPs. The efficiency of fault detection method is improved by considering the discrete RTPs. E. Generalized RTD Time Model: After optimization of number of RTD paths, the quick detection of fault is done by using discrete RTPs. Fault present at source node in round trip path is identified by discrete plus one RTPs analysis. Along with discrete

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RTPs in WSNs, additional two RTPs are essential to locate the fault present at second and third level respectively. Hence total numbers of RTPs used to detect fault are given by, PT = PD+L (18) where PT is number of total optimized RTPs and L is the numbers of sensor nodes excluding source node used in formation of RTP i.e. L = (m − 1). The equation (18) can be written in terms of N and m by referring (13) and (14) as, PT = [N/m] + C + (m − 1) (19) Analysis time of the proposed method depends upon the RTD times of RTPs used to examine. Generalized model of analysis time by selecting discrete RTPs in WSNs having N sensor nodes and RTP created by grouping ‘m’ sensor nodes is given by, τANL (G) = {[N/m] + C + (m − 1)} × mτ (20) After creating the RTPs and calculating the analysis time related to it by referring equation (20), for various numbers of sensor nodes (m) selected per RTP in WSNs with 100 nodes it is found that analysis time is lowest in the case of least numbers of sensor nodes i.e. m = 3. Thus the selection of sensor nodes per RTP equal to three will be better to enhance the detection efficiency of the proposed method. Deciding the specific RTPs (discrete RTPs) in WSNs with unique source node is a novel approach, which will enable the parallel analysis of such RTPs to improve the efficiency of suggested method. F. Algorithm to Detect Faulty Sensor Node: The algorithm to detect the working as well as faulty sensor node is explained below. The discrete RTPs with three sensor nodes are used to determine the fault in WSNs. Algorithm is executed in two phases, first phase is used to decide the threshold value of RTD time and fault is detected in the second phase. In the first phase all sensor nodes in WSNs are considered as working properly. Discrete RTPs are selected by incrementing the source node value by three and their respective RTD times are measured by using the subroutine. The highest value of RTD time measured during the execution of first phase is selected as the threshold RTD time for all discrete RTPs in WSNs. In the second phase of fault detection, instantaneous RTD time of discrete RTPs is compared with the threshold time. Discrete RTPs whose RTD time is found to be greater than threshold time is then analyzed in detail. This particular discrete RTP is examined in three stages to locate the exact position of fault. Let SX be the source node of this particular discrete RTP with sequence of sensor nodes as SX–SX+1–SX+2. Faulty sensor node in the WSNs can be present at position SX or SX+1 or SX+2 in RTP. Hence RTPs formed by these sensor nodes have to be examined to locate the fault. RTPs formed by second and third node in this particular discrete RTP have the sensor node sequence as SX+1–SX+2–SX+3 and SX+2–SX+3–SX+4 respectively. The RTD times of these RTPs are measured sequentially. On the basis of this RTD time, these RTPs are compared to detect the faulty sensor node. Detected faulty sensor node, which can be either failed or malfunctioning, is verified by comparing the RTD times of respective RTPs with threshold time. Execution of second phase in three stages to locate the fault is as follows. In the first stage RTP_X and RTP_X+1 are compared. If RTD time of RTP_X+1 is equal to threshold, provided that RTP_X time is greater than threshold value, then sensor node SX is determined as faulty. Here if the RTD time of RTP_X is infinity then SX is concluded as failed (dead) otherwise it is malfunctioning. RTP_X+1 and RTP_X+2 are compared in second stage, provided that RTD time of RTP_X is greater than threshold time. If the RTD time of RTP_(X+1) is greater than threshold and RTP_(X+2) is equal to threshold value, determines that SX+1 is faulty. In this case if RTD time of RTP_(X+1) is infinity then SX+1 is concluded as failed (dead) otherwise it is malfunctioning. Similarly RTP_X, RTP_(X+1) and RTP_(X+2) are compared in third stage. RTD times of RTP_X, RTP_(X+1) and RTP_(X+2) higher than threshold time determines that SX+2 is faulty. Infinity value of RTD time of RTP_(X+2) indicate that SX+2 is failed (dead) otherwise it is malfunctioning. Finally the SX+2 sensor node value is compared with the last node SN in the WSNs, if it is found to be less, then algorithm is executed again for the next discrete RTP. This process is continued till the examination of last discrete RTPs in WSNs. IV. Experimental Analysis A. Hardware implementation: The hardware implementation is done using six sensors, ARM LPC2148 and XBEE S2 wireless module. The statuses of the sensors are received on the other end using Zigbee device and it displayed using a LCD Device. First the threshold RTD time is detected. And then to test the suggested protocol one of the sensor is made faulty and the malfunctioning sensor is detected. While performing the experiment for failed (dead) state detection, one sensor node is made faulty by switching off its power supply. Infinity (∞) value of RTD time in simulation is indicated by ‘−2’ value in all cases. For detecting malfunctioning state, a delay of 5s is added to the RTP of particular sensor node in WSNs.

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Implementation of WSNs with large numbers of sensor nodes in hardware will be complicated. Hence to verify the scalability of investigated method, WSNs with large numbers of sensor nodes are implemented in software NS2. Fig.2 ARM LPC 2148 Board

B. Software implementation: Circular topology WSNs having different sensor nodes (N) are implemented by using the open source software NS2. In the proposed method round trip paths are formed by combining the adjacent three sensor nodes. RTD protocol is developed and implemented to measure the RTD time of such RTPs. Appropriate threshold RTD time is estimated by considering initially all sensor nodes in WSNs as working properly. Specific sensor node in WSNs is declared as faulty in order to test and verify the suggested method. Faulty sensor node can be either failed or malfunctioning; hence two cases have to be evaluated separately. Failed (dead) sensor node detection is done by declaring the particular node as dead in tcl script. Similarly malfunctioning behavior is detected by adding certain delay in the RTPs of particular sensor node. V. Conclusion The protocol proposed here is successfully implemented and tested on both hardware and software. The faulty sensor node is detected by comparing the RTD time of discrete RTPs. This proposed protocol is verified in hardware using the ARM processor and in the software using the NS2. This protocol can be used in any topology Wireless Sensor Networks. From the experiments conducted it has been clear that the efficiency of this RTD protocol will be good when we consider RTPs with three sensor nodes. Since only circular topology has been considered here, in the future work this suggested protocol can be applied in other topologies like NJ-LATA, circular and triangular. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

K. Sha, J. Gehlot, and R. Greve, “Multipath routing techniques in wireless sensor networks: A survey,” Wireless Personal Commun., vol. 70, no. 2, pp. 807–829, 2013. M. Asim, H. Mokhtar, and M. Merabti, “A fault management architecture for wireless sensor network,” in Proc. IWCMC, Aug. 2008, pp. 1–7. M. Younis and K. Akkaya, “Strategies and techniques for node placement in wireless sensor networks: A survey,” Ad Hoc Netw., vol. 6, no. 4, pp. 621–655, 2008. P. Jiang, “A new method for node fault detection in wireless sensor networks,” Sensors, vol. 9, no. 2, pp. 1282–1294, 2009. I. Chen, A. P. Speer, and M. Eltoweissy, “Adaptive fault tolerant QoS control algorithms for maximizing system lifetime of query-based wireless sensor networks,” IEEE Trans. Dependable Secure Comput., vol. 8, no. 2, pp. 1–35, Mar./Apr. 2011. A. A. Boudhir, B. Mohamed, and B. A. Mohamed, “New technique of wireless sensor networks localization based on energy consumption,” Int. J. Comput. Appl., vol. 9, no. 12, pp. 25–28, Nov. 2010. W. Y. Poe and J. B. Schmitt, “Node deployment in large wireless sensor networks: Coverage, energy consumption, and worstcase delay,” in Proc. ACM, AINTEC, Nov. 2009, pp. 1–8. M. Lee and Y. Choi, “Fault detection of wireless sensor networks,” Comput. Commun., vol. 31, pp. 3469–3475, Jun. 2008. A. Akbari, A. Dana, A. Khademzadeh, and N. Beikmahdavi, “Fault detection and recovery in wireless sensor network using clustering,” IJWMN vol. 3, no. 1, pp. 130–138, Feb. 2011. C.-C. Song, C.-F. Feng, C.-H. Wang, and D.-C. Liaw, “Simulation and experimental analysis of a ZigBee sensor network with fault detection and reconfiguration mechanism,” in Proc. 8th ASCC, May 2011, pp. 659–664. A. Mojoodi, M. Mehrani, F. Forootan, and R. Farshidi, “Redundancy effect on fault tolerance in wireless sensor networks,” Global J. Comput. Sci. Technol., vol. 11, no. 6, pp. 35-40, Apr. 2011. S. S. Ahuja, R. Srinivasan, and M. Krunz, “Single-link failure detection in all-optical networks using monitoring cycles and paths,” IEEE/ACM Trans. Netw., vol. 17, no. 4, pp. 1080–1093, Aug. 2009. R. N. Duche and N. P. Sarwade, “Sensor node failure or malfunctioning detection in wireless sensor network,” ACEEE Int. J. Commun., vol. 3, no. 1, pp. 57–61, Mar. 2012. T. W. Pirinen, J. Yli-Hietanen, P. Pertil, and A. Visa, “Detection and compensation of sensor malfunction in time delay based direction of arrival estimation,” IEEE Circuits Syst., vol. 4, no. 1, pp. 872–875, May 2004.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net AN EMBEDDED REAL TIME FINGER VEIN RECOGNITION SYSTEM FOR ATM Sonu.p.sam ME Embedded System Technologies Anna University Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India ____________________________________________________________________________________ Abstract: In consideration of emerging requirements for information protection, biometrics, which uses human physiological or behavioral features for personal identification, has been extensively studied as a solution to security issues. However, most existing biometric systems have high complexity in time or space or both, and are thus not suitable for mobile devices. In this paper, we propose a real-time embedded finger-vein recognition system for authentication on ATM. The system is implemented on embedded platform and equipped with a finger-vein recognition algorithm. We have described and implemented an algorithm for finger-vein recognition system using image processing. In the pc we have database were the authentication the matching will done & the pc will matches the two images until provided the information to the GSM modem. The gsm modem is using to generate the one time password to the user. Keywords: ATM, Embedded, Image processing, GSM ______________________________________________________________________________________ I. Introduction The traditional way to provide private information is by the use of passwords or Personal Identification Numbers (PIN), magnetic swipe cards, keys and smart cards that are easy to implement but are subjected to risk of exposure or being forgotten and are hence unreliable. As a result, biometrics that involve analysis of human biological, physical and behavioral characteristics have been developed to provide more reliable security A long list of biometric patterns is available. Many systems using these have been developed and implemented, namely face, iris, finger print, palm print, hand shape, voice, signature, gait and so on. Irrespective of this variety of biometric patterns, none of them are completely reliable and secure. In case of fingerprint, the condition of the finger surface (e.g. dryness, sweat) and skin distortion degrades the recognition accuracy. Performance for face recognition depends hugely on facial expressions and illuminations, which can change by occlusions or facelifts. The biometrics like fingerprint, iris, signature, hand shape, voice, and face do not necessarily provide confidentiality since the features used in the methods are exposed outside the human body. These methods are hence susceptible to forgery from the point of view of security and convenience, the finger-vein is a promising biometric pattern for personal identification II. Literature Survey In this section we will be describing the various approaches that were used in person identification using biometric systems. A biometric system is uses the specific physiological or behavioral features possessed by the user for identification and these features are unique, universal and persistent. These systems include face recognition, fingerprint technology, iris recognition, hand geometry, keystroke, signature and speech recognition A. Face Recognition Facial images are the common biometric feature used for personal identification. Face recognition is mainly performed by two approaches, they are Eigen face based recognition and 3D face recognition. The eigen face based recognition works by analyzing face images and computing Eigen faces which are faces composed of eigenvectors. The comparison of Eigen faces is used to identify the presence of a face and its identity. The Eigen face technique is straightforward, efficient, and yields generally good results in controlled circumstance. There are also some limitations of Eigen faces. There is limited robustness to changes in lighting, angle, and distance. 2D recognition systems do not capture the actual size of the face, which is a fundamental problem. These limitations affect the techniqueâ&#x20AC;&#x2122;s application with security camera. 3D face recognition systems make 3D models of faces and compare the 3D faces for recognition. These systems are more accurate because they capture the actual shape of faces. The acquisition of 3D data is one of the main problems for 3D systems. Another face identification technology, Facial thermo grams, uses infrared heat scans to identify facial characteristics. This non-intrusive technique is light-independent and not vulnerable to disguises. Even plastic surgery, cannot hinder the technique. This technique delivers enhanced accuracy, speed and reliability with minimal storage requirements. To prevent a fake face or mold from faking out the system, many systems necessitate the person to smile, blink, or otherwise move in a way that is human before verifying 1. 2D recognition is affected by changes in lighting, the personâ&#x20AC;&#x2122;s hair, the age, and if the person wear glasses.

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2. Requires camera equipment for user identification; thus, it is not likely to become popular until most PCs include cameras as standard equipment. B. Fingerprint Technology A fingerprint is the pattern of ridges and grooves on the surface of a fingertip. The fingerprints are highly stable and unique. The uniqueness of fingerprint is determined by global features like valleys and ridges, and by local features like ridge endings and ridge bifurcations, which are called minutiae. The recent studies reveal that probability of two individuals, having the same fingerprint is less than one in a billion. There are various fingerprint matching algorithms like minutiae based matching correlation based matching, genetic algorithms based. Among these, minutiae based matching is the dominated one. In minutiae based matching the similarity of two fingerprints is determined by computing the total number of matching minutiae from the scanned fingerprints. Extraction of minutiae features before matching needs a series of processes, containing alignment computation, image segmentation, image enhancement, and ridge extraction and shinning, minutiae extraction and filtering Correlation based matching uses one to one correlation between fingerprints. This method gives poor results in fingerprint recognition because correlation cannot recognize elastic-distorted versions of the same fingerprint. In neural network based approach the finger prints are classified by using HAVNET. The number of output nodes of HAVNET was equal to number of enrolled fingerprints. The method was not able to distinguish fingerprints of similar shapes the genetic algorithm based methods try to identify the optimal global alignment between two fingerprints. The process is highly time consuming one. 1. For some people it is very intrusive, because is still related to criminal identification. 2. It can make mistakes with the dryness or dirty of the fingerâ&#x20AC;&#x2122;s skin, as well as with the age (is not appropriate with children, because the size of their fingerprint changes quickly). 3. Image captured at 500 dots per inch (dpi). Resolution: 8 bits per pixel. A 500 dpi fingerprint image at 8 bits per pixel demands a large memory space, 240 Kbytes approximately â&#x2020;&#x2019; Compression required (a factor of 10 approximately). C. Iris Recognition Iris recognition systems make use of the uniqueness of the iris patterns to identify a person. This system uses a high-quality camera to capture a black-and-white, high-resolution image of the iris (the colored ring surrounding the pupil). Iris recognition consists of five operations; they are image acquisition, iris localization or segmentation, iris normalization and unwrapping, feature encoding, and matching algorithm. In image acquisition step the system takes a high-quality image of the iris, Iris localization takes place to detect the edge of the iris as well as that of the pupil; thus extracting the iris region, Normalization is used to transform the iris region to have fixed dimensions, and hence removing the dimensional inconsistencies between eye images, The normalized iris region is unwrapped into a rectangular region. The feature encoding is used to extract the most discriminating feature in the iris pattern so that a comparison between templates can be done. Finally a decision can be made in the matching step 1. Intrusive. 2. A lot of memory for the data to be stored. 3. Very expensive D. Hand Geometry Hand geometry recognition systems use a number of measurements taken from the human hand, including its shape, size of palm, and lengths and widths of the fingers. The technique is very simple, relatively easy to use, and inexpensive. Hand geometry based identification consists of following steps, image capturing and preprocessing, measurements and feature selection and finally classification and verification. The imaging equipment used for hand geometry recognition is simple and it includes a platform where the hand should be placed and a camera. The hand image should be pre-processed to extract the features. Color image is converted into grey scale image with background subtracted. The measured features in hand geometry are finger length, width and palm dimensions. Once the features are measured a statistical analysis is performed for selecting significant features. The extracted features are compared using common distance measures like Euclidean or Hamming distance. Comparison is done by normalized correlation between sample and template feature vectors. If the correlation exceeds the pre-defined threshold, the identity indicated by the user is verified. 1. Very expensive 2. Considerable size. 3. It is not valid for arthritic person, since they cannot put the hand on the scanner properly. E Signature Recognition Signature recognition is based on the way a person signs his or her name. Signatures are a behavioral biometric that change over a period of time and are influenced by physical and emotional conditions of the persons. Professional forgers may be able to reproduce signatures that fool the system. Biometric signatures are used in banking and finance industry in order to restrict duplicate signature frauds. Dynamic signature verification technology is used, where the parson make signatures on contact sensitive devices like PDA or tablet PC. This technology is also installed in mobile phones to prevent illegal access, although the device is lost or stolen.

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F. Speech Recognition Speech Recognition (is also known as Automatic Speech Recognition (ASR) or computer speech recognition) is the process of converting a speech signal to a sequence of words, using a computer program. Speech recognition technology was increasingly used within telephone networks to automate as well as to enhance the operator services. Generally there are three approaches to biometric speech recognition; they are Acoustic Phonetic Approach, Pattern Recognition Approach and Artificial Intelligence Approach. Acoustic Phonetic Approach was based on finding speech sounds and providing appropriate labels to these sounds. The patternmatching approach contains two steps namely, pattern training and pattern comparison. This approach uses a well formulated mathematical framework and establishes consistent speech pattern representations, for reliable pattern comparison, from a set of labeled training samples via a formal training algorithm. The Artificial Intelligence approach is a hybrid of the acoustic phonetic approach and pattern recognition approach. From the point of view of security and convenience, the finger-vein is a promising biometric pattern for personal identification .The finger-vein has following advantages over other biometrics 1. Every individual has a unique pattern of veins and it is even different in case of identical twins. As the individual grows, the veins do become larger, but the position and number of veins do not change from infancy. 2. As the vein structure is underneath the skin, it is invisible to the naked eye and is very complex that it cannot easily spoof the system. 3. It is more acceptable by user because noninvasive and contactless capture of finger-vein provides convenience and hygiene. 4. It is a natural and convincing proof that the person whose finger- vein is captured is alive, since finger-vein pattern can only be taken from a live body. III. System Analysis Analysis of finger-vein based biometric security system has been carried Operating System using MATLAB.From each people the forefinger, middle finger, and ring finger of both hands are considered. The image which is obtained from the real- time camera. The image after preprocessing, while matching two types of errors results in the finger-vein based biometric verification security system. The errors are false rejection rate and the false acceptance rate. False rejection is a claim that a genuine image is considered as impostor. False acceptance is a claim that an impostor image is considered as genuine. When the false rejection rate and the false acceptance rate are equal, then the performance of the system is evaluated as equal error rate. This system is suitable for mobile device applications with low computational complexity. Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and Model-Based Design for dynamic and embedded systems. MATLABâ&#x20AC;&#x2122;s Graphical User Interface Development Environment (GUIDE) provides a rich set of tools for incorporating graphical user interfaces (GUIs) in Mfunctions. Using GUIDE, the processes of laying out a GUI (i.e., its buttons, pop-up menus, etc.)and programming the operation of the GUI are divided conveniently into two easily managed and relatively independent tasks Figure 1 Finger vein scanner & pc section

Finger vein Scanner

PC Section

RS 232

Zigbee

IV Methodology A Finger-Vein recognition system consists of two stages - enrollment stage and verification stage. Both stages consist of following steps. A Image Acquisition

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Image acquisition is of two type off-line and online. In image acquisition, we are using off-line images. Online images are the images which are taken real time and off-line images means the images which are taken from already created database. 1) Resizing By this, the dimensions of the image are changed to the required new width and height Figure 2

Receiver Section Battery Power (+5v)

GSM

Zigbee

ARM 7 (LPC 2148) Touch screen

4*20 LCD Display

Driver control part

Resize an image Syntax B = imresize(A,m,method) B = imresize(A,[mrows ncols],method) B = imresize(...,method,n) B = imresize(...,method,h) Description imresize resizes an image of any type using the specified interpolation method. method is a string that can have one of these values: 1. 'nearest' (default) uses nearest neighbor interpolation. 2. 'bilinear' uses bilinear interpolation. 3. bicubic' uses bicubic interpolation. If you omit the method argument, imresize uses the default method of 'nearest'. B = imresize(A,m,method) returns an image that is m times the size of A. If m is between 0 and 1.0, B is smaller than A. If m is greater than 1.0, B is larger than A. B = imresize(A,[mrows ncols],method) returns an image of size [mrows ncols]. If the specified size does not produce the same aspect ratio as the input image has, the output image is distorted. When the specified output size is smaller than the size of the input image, and method is 'bilinear' or 'bicubic', imresize applies a lowpass filter before interpolation to reduce aliasing. The default filter size is 11-by-11. You can specify a different order for the default filter using [...] = imresize(...,method,n) n is an integer scalar specifying the size of the filter, which is n-by-n. If n is 0 (zero), imresize omits the filtering step. You can also specify your own filter h using [...] = imresize(...,method,h) h is any two-dimensional FIR filter (such as those returned by ftrans2, fwind1, fwind2, or fsamp2). B. Image Enhancement Image enhancement is used to improve the quality of an image. It is used to improve image contrast and brightness characteristics as well as to reduce noise contents. It highlights certain features of interest in an image. Histogram equalization is a technique for adjusting image intensities to enhance contrast. Let f be a given image represented as a mr by mc matrix of integer pixel intensities ranging from 0 to L â&#x2C6;&#x2019; 1. L is the number of possible intensity values, often 256. Let p denote the normalized histogram off with a bin for each possible intensity. So pn = number of pixels with intensity n / total number of pixels "image histogram" is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance. Image histograms are present on many modern digital cameras.

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Photographers can use them as an aid to show the distribution of tones captured, and whether image detail has been lost to blown-out highlights or blacked-out shadows. The horizontal axis of the graph represents the tonal variations, while the vertical axis represents the number of pixels in that particular tone. The left side of the horizontal axis represents the black and dark areas, the middle represents medium grey and the right hand side represents light and pure white areas. The vertical axis represents the size of the area that is captured in each one of these zones. Thus, the histogram for a very dark image will have the majority of its data points on the left side and center of the graph. Conversely, the histogram for a very bright image with few dark areas and/or shadows will have most of its data points on the right side and center of the graph. C. Wavelet Transform In order to analyze signals of very different sizes, it is necessary to use time- frequency atoms with different time supports. The wavelet transform decomposes signals over dilated and translated functions called wavelets, which transform a continuous function into a highly redundant function D Feature Extraction In pattern recognition and in image processing, feature extraction is a special form of reduction. When the input data to an algorithm is too large to be processed and it is suspected to be very redundant (e.g. the same measurement in both feet and meters, or the repetitiveness of images presented as pixels), then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input. One very important area of application is image processing, in which algorithms are used to detect and isolate various desired portions or shapes (features) of a digitized image or video stream. It is particularly important in the area of optical character recognition. V Conclusion In this paper, we presented a finger-vein based biometric security system that can be used for security based electronic devices. The method can extract the finger-vein feature for recognition from the NIR images. This method uses single sample and is convenient to the application. This work can be extended with increasing the database for further verification. The project has demonstrated and proved the capability of personal identification based on vein patterns. Although the scale is small, other applications can be extended by solutions which are used in this project. For large applications, some modules should be done in hardware in order to improve the speed of the whole system. If problems of accuracy and speed are solved there is a huge market waiting for the system such as ATMs, cars, houses, cell phones, entrance doors, etc VI [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

References

A. K. Jain, S. Pankanti, S. Prabhakar, H. Lin, and A. Ross, “Biometrics:a grand challenge”, Proceedings of the 17th International Conference on Pattern Recognition (ICPR) ATM security System using fingerprint biometric identifier: An Investigative Study‖ ,By- Saatci, V avsanogh,M. Purser. Year of publishing paper 2009-2010 IEEE. D. Wang , J. Li, and G. Memik, “User identification based on fingervein patterns for consumer electronics devices”, IEEE Transactions on Consumer Electronics, vol. 56, no. 2, pp. 799-804, 2010. D. D. Hwang and I. Verbauwhede, “Design of portable biometric authenticators - energy, performance, and security tradeoffs,” IEEE Transactions on Consumer Electronics, vol. 50, no. 4, pp. 1222-1231, Nov.2004. N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger vein patterns based on repeated line tracking and its application to personal identification”, Machine Vision Application, vol. 15, no.4, pp.194–203, H. Lee, S. Lee, T. Kim, and Hyokyung Bahn, “Secure user identification for consumer electronics devices,” IEEE Transactions on Consumer Electronics, vol.54, no.4, pp.1798-1802, Nov. 2008 Novianto, Suzuki, and Maeda, “Optimum estimation of local fractal dimension based on the blanket method,” Transactions of the Information Processing Society of Japan, vol. 43, no.3, pp. 825-828 P. Corcoran and A. Cucos, “Techniques for securing multimedia content in consumer electronic appliances using biometric signatures,” IEEE Transactions on Consumer Electronics, vol 51, no. 2, pp. 545-551 2010 P.K. Amurthy and M.S. Redddy, “Implementation of ATM Security by Using Fingerprint recognition and GSM”, International Journal of Electronics Communication and Computer Engineering vol.3, no. 1, pp. 83-86, 2012. “Smart Card & Security Basics”-CardLogix, paper no.:710030 www.cardlogix.com [2] “Smart card based Identity Card And Survey”-White Paper JKCSH (Jan Kremer Consulting Services). W. Song, T. Kim, H. C. Kim, J. H. Choi, H. Kong and S. Lee, “Afinger-vein verification system using mean curvature”, Pattern Recognition Letters, vol. 32, no.11, pp. 1541-1547, 2011. Y. Kim, J. Yoo, and K. Choi, “A motion and similarity-based fake detection method for biometric face recognition systems,” IEEE Transactions on Consumer Electronics, vol.57, no.2, pp.756-762, May 2011 Y. G. Dai and B. N. Huang, “A method for capturing the finger-vein image using non uniform intensity infrared light”, Image and Signal Processing, vol.4, pp.27-30, 2008. Z. Liu, Y. Yin, H. Wang, S. Song, and Q. Li ,“Finger vein recognition with manifold learning”, Journal of Network And Computer Applications, vol.33, no.3, pp. 275-282, 2010

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