Ijbea vol2 print by iasir journals

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

Issue 8, Volume 1 & 2 March-May, 2014

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: India, Australia, Germany, 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 eighth 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 eighth issue, we received 95 research papers and out of which only 33 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 eighth 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 (March-May, 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.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, 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, India. 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.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur 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. 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 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.) 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. 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,TamilNadu,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, India. 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. 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.

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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 Eng.,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. 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, India.

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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. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. 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.

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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, 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 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. 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, India. 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

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, 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. . 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) 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 Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura 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.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, 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, Punjab, India. 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 (March-May, 2014, Issue 8, Volume 1 & 2) Issue 8 Volume 1 Paper Code

Paper Title

Page No.

IJEBEA 14-207

The effects of Philanthropy Social Responsibility on Brand Personality and Creation Corporate Brand Equity in SMEs (A case study in IRAN) Mohammad Reza Hamidizadeh, Morteza Rezaee

01-05

IJEBEA 14-209

The Link between Institutions and Industrial Development: An Empirical Study of Indian States BIMAL C. ROY, DR. SATYAKI SARKAR, DR. NIKHIL RANJAN MANDAL

06-12

IJEBEA 14-211

Rediscovering, Redefining and Rebuilding Manufacturing in the Education Sector S.K.Suman, R.M.Belokar, Harish Kumar Banga, Pratik Belokar

13-17

IJEBEA 14-212

Analysis and Forecasting of Drought by Developing a Fuzzy-Based Hybrid Index in Iran Parisa Lakhaye Rizi, Reza Moghaddasi, Alireza Eghbali

18-23

IJEBEA 14-215

Influence of tool pin profile on mechanical properties of FSP processed aluminium 6063 Chandan Deep Singh, Ripandeep Singh, Napinder Singh, Jaimal Singh Khamba

24-28

IJEBEA 14-217

Online sales promotions of Grocery and other FMCG products in Chennai Entity Alexander CVJ Victoria and Dr. M. Ganesan

29-36

IJEBEA 14-222

Research Perspective Review on Retinal Blood Vessel Detection Dr Ravi Subban, G. Padma Priya, P.Pasupathi, S.Muthukumar

37-42

IJEBEA 14-225

Parametric Optimization of SAW Welding Parameters using Taguchi L9 Array Mr. Pradeep Deshmukh, Prof. S. N. Teli

43-47

IJEBEA 14-228

Database Designing for the Online Examination Application Using SAP-ABAP Preeti Singh Bhadoria, Kapil Nimker, Sanjay Ojha

48-52

IJEBEA 14-229

Insights into Awareness Level and Investment Behaviour of Salaried Individuals towards Financial Products Puneet Bhushan

53-57

IJEBEA 14-238

Implementing security to OFDM symbols of 802.11n networks SANTOSH, VINOD B DURDI

58-61

IJEBEA 14-240

To study the role of manufacturing competency in the performance of Sonalika tractor manufacturing unit Chandan deep Singh, Palwinder Singh*, Jaimal Singh Khamba

62-66

IJEBEA 14-244

BIM creation using point clouds Desislava Georgieva Tanusheva

67-72

IJEBEA 14-251

Employer Branding: The New Mantra for Talent Acquisition Mrs. Shipra Sharma, Ms. Sakshi Chabbra

73-80

IJEBEA 14-252

Analysis of Risk Management of Vendor in Banking Dr Hariharan.N.P, Reeshma.K.J

81-84

IJEBEA 14-253

Competitive Intelligence Loop Contexts in Insurance Industry Dr. Mohammad Reza Hamidizadeh, Dr. Ahmad Roosta, Dr. Jalil Lajevardi, Moghadaseh Mohamadian

85-89

IJEBEA 14-256

Talent Management in TCS Dr. Namita Rath, Ms. Sujata Rath

90-93

IJEBEA 14-260

Cost Optimization and Resource Utilization via Virtualization Using Virtual Box Sonia Bansal, Gurmeet Singh

94-96

Issue 8 Volume 2 Paper Code

Paper Title

Page No.

IJEBEA 14-261

A Study on Stress Related Problems among the Employees Dr.A.Sathish Babu, SK.Irshad

97-100

IJEBEA 14-262

Performance of Reinforced Concrete Beam under Line Impact Loading I.K .Khan

101-104

IJEBEA

Integration of Big Data in Banking Sector to Speed up the Analytical Process

105-110

14-267

Prof. Dr. P.K. Srimani, Prof. Rajasekharaiah K.M.

IJEBEA 14-270

Online Examination Application using SAP ABAP Kapil, Shwetank Sharma, Sanjay Ojha

111-118

IJEBEA 14-271

An Empirical Study of Extracting information for Business Intelligence V.Jayaraj, V.Mahalakshmi

119-121

IJEBEA 14-272

Hierarchical Decentralized Averaging for Wireless Packet Network A R ASWATHA, RAHUL R, M PUTTARAJU

122-126

IJEBEA 14-275

Improvement of Quality through Six Sigma A Case Study R.M.Belokar, Harish Kumar Banga, Jagbir Singh, Pratik Belokar

127-131

IJEBEA 14-276

An Efficient Face Recognition Technique Using PCA and Artificial Neural Network KARTHIK G, SATEESH KUMAR H C

132-137

IJEBEA 14-277

An Overview of Changing Trend of Traditional Retailing to i-Retail in India Shantanu Saha, Arvind Rathore

138-144

IJEBEA 14-278

PROCESS FLY ASH EFFECT ON HARDEN PROPERTIES OF SELF COMPACTING CONCRETE Mr U. N. SHAH, Dr C. D. Modhera

145-148

IJEBEA 14-280

A Critical Review on Different Heat Exchangers used for Heat Transfer between Two Fluids Avinash D. Jadhav, Tushar A. Koli, Vijay H. Patil

149-152

IJEBEA 14-283

A highly efficient method for denoising of an image using gradient histogram preservation Sainath, Nagarathna

153-157

IJEBEA 14-285

Testing Methodology to test Online Examination Application developed in SAP ABAP Shwetank Sharma, Ayushi Chhabra, Sanjay Ojha

158-163

IJEBEA 14-286

Comparative Review of Scheduling and Migration Approaches in Cloud Computing Environment MS. Alankrita Aggarwal, Rajju

164-166

IJEBEA 14-287

A Policy Driven Architecture for Effective Service Allocation in Cloud Environment Mansi Goyal, Richa Chhabra

167-171

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 COST OPTIMIZATION AND RESOURCE UTILIZATION VIA VIRTUALIZATION USING VIRTUAL BOX 1

Sonia Bansal, 2Gurmeet Singh Dept. of CSE, Kurukshetra University, Kurukshetra, Haryana. India _______________________________________________________________________________________ Abstract: If manager have embraced virtualization because it reduce the cost and complexity. Virtualization also allows the separation of resources and dependencies. It enables the movement of other resources to different location in order to increase the performance of the overall system. Performance testing is very important in the software development life cycle of a Product. This work gives very exposure to the users for proper utilization of resources using virtualization and help to maintaining the performance of the System. In this paper we will study the virtualization method to increase the performance of the overall system. Keywords: Virtualization; virtual box; performance; Resource Utilization; cost analysis ______________________________________________________________________________________ I. Introduction Virtualization first appeared in 1960s to enable timesharing of hardware between multiple users. Virtualization has covered large area but we focus on the server virtualization. Virtualization reduces the total cost, reduce power, increase efficiency etc. due to these reasons many company uses virtualization to build a no of different physical servers on a single virtual server. This can be achieved by using different virtual software such as virtual box, VMware, Xen etc. These software run on a shared physical environment. Virtualization allows two or more environments to run on the same physical machine such that the different environments are completely isolated from each other. Virtual machines support more flexible and finer grain resource allocation and configuration than physical machines .even the hardware cost will reduces. The server virtualization will convert the physical system into a pool of logical computing resources. Resources are allocated to the different operating system according to their needs this will increase the proper utilization of hardware and software resources. Many researchers have been done in the optimization of cost and resources but that is not sufficient .Hence a study of resource utilization and performance optimization using the virtual machine will help in understanding the application. This study will help to take an accurate decision and to optimize the performance of the system. II. Virtualization In computer science, the word virtualization is used for abstraction of computer resources, such as memory, network, processor, application, etc [2], [5], [6]. When we talk about virtualization, we mean the platform virtualization, which in fact represents the computer virtualization or the virtualization of the operating system [2]. The core of the virtualization which handles virtual computers is called hypervisor. The virtualization can be categorized according to the level of virtualization and operating mode of the hypervisor. Based on the operating mode, we distinguish non-hosted hypervisor and hosted hypervisor. While the first one Operates directly on hardware (e.g. Xen, ESX Server), the second one performs within another operation system (e.g. Microsoft Virtual PC and VMware Workstation) [3], [12], [16]. The categorization based on the degree of Virtualization is a bit more complicated. The main categories of virtualization are: a. Full Virtualization B.Partial virtualization C.Para Virtualization D.Hardware Virtualization E.Operating System level Virtualization III. Overview of virtual machine IBM develops the concept of virtual machine as a way of time sharing very expensive mainframe computer. An organization can afford only one mainframe and this mainframe is used for the development of application. Developing an application on the same system you intend to deploy on while other applications are “in production” on that system was generally considered bad practice. The virtual machine concept allows the same computer to be shared as if it were several. IBM defined the virtual machine as a fully protected and isolated copy of the underlying physical machine’s hardware [2]. IBM designed their virtual machine systems with the goal that applications, even operating systems, run in the virtual machine would behave exactly as they would on the original hardware.

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A. Benefits of Virtual Machine 1. Reducing Number of Servers: the benefit of virtualization is fewer servers, which result in lower the capital expenditure. 2. Running multiple operating systems simultaneously: Virtual Box allows you to run more than one operating system at a time. This way, you can run software written for one operating system on another (for example, Windows software on Linux or a Mac) without having to reboot to use it. 3. Easier software installation: software vendors can use the virtual machine for the software installation. For example, installing a complete mail server solution on a real machine can be a tedious task. But with the help of virtual machine this software can be easily packed in virtual machine and installed. Figure 1: Computational virtualization representation

4. Diminishing real estate costs: Virtualization often enables data center consolidation, leading to the closure of facilities or a reduced footprint. There are also productivity savings and efficiencies that can result from virtualization: 5. Greater flexibility: When requirements are modified, workgroups change or products move into development, virtualization allows supportive IT assets to keep pace more readily than a straight physical environment is able to. 6. USB device support: Virtual Box implements a virtual USB controller and allows you to connect arbitrary USB devices to your virtual machines without having to install device-specific drivers on the host. USB support is not limited to certain device categories. IV. Experimental Work The total cost is calculated when we need to configure the different servers on different system. If we configure these servers the cost of configuration is as:

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Table 1: Cost of different servers Servers

Hardware cost

Hp Proliant ML110 G7(B6S91A)

Rs.36,500

Hp Proliant ML10 server(737650375)

Rs.39,750

Hp Proliant ML110 G7 server(647336-B21)

Rs.41,300

Dell Poweredge t110 11 Compact tower server

Rs. 43.000

By using virtualization the the cost of configuration can be reduced.with the help of virtualization the different server can be configure on a single operating system. The virtual box is software that is used to configure the different serners.virtual box will create a virtual environment in which different servers configure and rum simultaneously and reduce the capital cost and increase the stability and improve the performance of the system. The cost reduced by using virtualization is: Table 2: Cost after virtualization Servers

Hardware cost

Hp Proliant ML110 G7(B6S91A)

Rs.36,500

Hp Proliant ML10 server(737650375)

Hp Proliant ML110 G7 server(647336-B21)

Dell Poweredge t110 11 Compact tower server

REFERENCES [1] [2] [3] [4] [5] [6] [7]

Puspanda Hatta,Widyaman&Warsun Najib,Total Cost of Ownership Formulation Analysis For Virtualization Data center in University,2013. Shivangi Johri,Nikita Jaiswal,Shivangi Tyagi& Manvi Mishra,Desktop Virtualization-Empowering Information Security,vol 1,Iss 1,2013. Gurmeet Singh & Amit Bindal,Resource utilization via Virtualizationin Linux on different workloads using virtual Box,IJCST Vol 2,Issue 4,2011. Gurmeet singh & Amit Bindal,Resources Utilization and Performance Optimization via Virtualization using different virtualization Application,ISSN Vol 6,no, 18,2011. White Paper:Virtualization and Optimization,The Right Technology,CDWG.com. White Paper:Virtualization and Infrastructure Optimization .CDWG.com. Survey of system virtualization techniques,March 8,2004.

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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 A STUDY ON STRESS RELATED PROBLEMS AMONG THE EMPOYEES 1

Dr.A.Sathish Babu, 2SK.Irshad P.G Dept of Commerce & Management Studies, VRS & YRN P.G College, Andhra Pradesh, India Department of Management Studies, Guntur Engineering college, Guntur, Andhra Pradesh, India _________________________________________________________________________________________ Abstract: Man is a Social Animal Man being the member of society is generally defined in terms of his Social roles and obligations and the type of work activities he is involved in. This definition of Man in terms of his work or his work related activities is not something of recent origin. Both in the west and in the East, for hundreds of years the dommating theme of classification and stratification in Society has been work and Job One’s status or station in life has been intimately linked with his calling. A role may be defined “As a totality of formal tasks, in formal tasks and acts as recognized by the Individual”. The human being when he opts to associate himself with an organization enters into the relationship with certain needs of his own. To achieve the organizational goals, therefore, it becomes necessary to integrate the Individual with the organization. A respectable job is no protection against the stress life in the Business world today. Stress arises when an Employee is unable to meet with external Demands Expectations and or internal needs/aspirations. Big events do not Stress us as much as the constant petty annoyances of everyday life. Stress is “Perception” It is the demands that are imposed upon because there are too many alternatives, too many choices. Stress is caused by being conscientious hard working. It is “being willing to labor under the pressure of deadlines”. It is being strong enough to face up to resolving difficult business problems and naturally. It is also rampant in the mere of complex interpersonal Business relationships. __________________________________________________________________________________________ I. DEFINITION Stress can be defined as “a dynamic condition in which an individual is confronted with an opportunity, constraint, or demand related to what he or she desires for which the outcome is perceived to both uncertain and important” ------- Prof. Robbins Stress is not necessarily bad in and of itself. While stress is typically discussed in a negative context it has a positive value. It is an opportunity when it offers potential gain. More typically, stress is associated with CONSTRAINTS AND DEMANDS. Constraints prevent you from doing what you desire. Bemands refers to the loss of something desired. Two conditions are necessary for potential stress to become actual stress. There must be un-certainty over the outcome and the outcome must be important. Regard less of the conditions. It is only when there is doubt or uncertainty regarding whether the opportunity will be sized. The constraint removed or the loss avoided that there is stress. According to IVANCEVICH AND GIBSON stress can be defined in four different ways. 1. STIMULUS DEFINITION: Stress is the force acting on the Individual that results in a response of strain where strain is pressure or deformation. However this definition fails to recognize that two people subjected to the same stress may show far different levels of strain. 2. RESPONSE DEFINITION: Stress is the Physiological or Psychological response of an individual to an environmental stressor, where a stressor is an external event or situation that is potentially harmful. Here, stress is viewed as an internal response. This definition fails to enable any one to predict the nature of the stress response or even whether there will be stress response. 3. STIMULUS-RESPONSE DEFINITION: Stress is the consequence of the interaction between an environmental stimulus and the response of the Individual. Stress as per this version, is the result of a unique interaction between stimulus conditions in the environment and the individuals predisposition to respond in a certain manner. 4. A WORKING DEFINATION: Stress is an adaptive response mediated by individual differences and or psychological Processes that is a consequence of any external environmental action situation or Event that places excessive Psychological and or Physical Demands on a person. Stress is highest for those individuals who perceive that they are un-certain as to whether they will sin or lose and lowest for those individuals who think that winning or losing is a certainty. But importance is also critical. If winning or losing is an unimportant outcome, there is no stress.

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II. CLASSIFICATION OF STRESS Actually stress can be classified into two ways. (1) CONSTRUCTIVE STRESS (2) DESTRUCTIVE STRESS. Constructive stress (EUSTRESS as it is sometimes called) acts in a positive manner for the individual and the organization. Ex:- Winning a contest, falling in Love. Eustress can indicate a situation where the individual is in balance or within tolerable limits. This can be depicted through a figure as follows.

The figure shows that low to moderate stress can increase effort, stimulate creativity and encourage diligence in one’s work. It can be equated with tension that causes you to work hard before exams, pay attention in class and complete projects and assignments on time. The same positive results of stress can be found in the work place. DESTRUCTIVE STRESS (DISTRESS) is not healthy for the individual and / or organization. Distress would indicate effects that are out cf balance or outside the tolerance limits. Excessive stress may lead to overload and break down a person’s physical and mental systems. Performance can suffer as people experience illness brought on by very intense stress and or react to high stress through absenteeism, turnover, errors, accidents and dissatisfaction and reduced performance. Managers seek the positive performance edge provided by constructive stress. At the same time. They must also pay attention to destructive stress and its likely impact on people and work performance in a negative way. III. SOURCES AND SYMPTOMS OF STRESS The stress reaction is a coordinated chemical mobilization of the entire human body to meet the requirements of Life and Death, struggle or of a rapid escape from the situation. The intensity of the stress reaction depends on the brain’s perception of the Severity of the situation. The term pressure refers to those features of a situation that may be problematic for the individual and that amount to demands for adoption of some kind. Stress on the other hand refers to specific set of Bio-chemical condition within the person’s body conditions that reflect the body’s attempt to make the adjustment. In short, PRESSURE IS IN THE SITUATION. STRESS IS IN THE PERSON. Three sets of factors – (1) ENVIRONMENTAL (2) ORGANIZATIONAL (3) INDIVIDUAL IV. NEED FOR THE STUDY Transiquent to the introduction of information technology the world has become so small and the intention of the Government as well as State Bank of India particularly is to bring banking facilities to the threshold of the common man. In view of the fact the state bank of India is intending to use information technology as the basic platform to provide as many. Facilities as possible such as debit cards, credit cards. Online banking, card to card transactions and value added services. The success of any organization depends on how best it reduces the cost this implies cost reduction needs to profit maximization, by using minimum personnel the bank is contemplating to earn maximum profit without compromising the quality service to the customer. Hence the need has arisen to study this stress levels.

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V. SCOPE OF THE STUDY In view of the fact the government of India is giving utmost importance for the development of banking sector on the platform of information technology India is biggest country and it is difficult task to provide banking facilities to each and every citizen on other hand the government is contemplating to reduce currency notes and develop plastic money hence the scope of banking sector is increasing day by day and at the same time many number of employees are going to retire shortly as such there is a shortage of man power in banking sector consequently the stress levels of existing employees is increasing. This causes a lot of concerns on the health of employees hence there is scope for to study how the stress levels are damaging the performance of existing bank employees and necessary steps are to be initiated to reduce stress levels VI. OBJECTIVES OF THE STUDY The main objective of study is 1. To examine the causes of stress 2. Study the factors causing stress among bank employees 3. To review the role of information technology in banks 4. To study the impact of policies of state bank of India 5. To examine the views and attitudes of bank employees about the work culture 6. To suggest measures for the reduction of stress among the bank employees VII. METHDOLOGY OF THE STUDY Information for the present study will be collected from both primary and secondary sources PRIMARY DATA First hand information will be collected by conducting personal interview with employees, middle level and top level management and union leaders. An effort will be made to elicit opinions of the bank employees about the work culture for this purpose a questionnaire will be circulated and all the branches of state bank of India in Guntur district will be collected. SECONDARY DATA Secondary data will be collected from the records, health schemes and by the state bank of India to its employees and other relevant documentary material general information will be collected from the various libraries. SAMPLE SIZE The sample size is 1000 employees VIII. SAMPLE TECHNIQUE MASLOCH JACKSON Technique will be used, basically this personalization emotional exertion (EE), reduced personal accomplishment (RPA) and depersonalization (DP) DP reflects for measuring job induced tension which is viewed as â&#x20AC;&#x153;the existence of tension and pressures growing out of job requirements including possible outcomes in terms of feelings or physical symptoms like tiredness , stiffness, weakness, irritation problems the scale will be prepared on seven points. The house and rezzo scale comprising of seven items if it is true is equal to 2, false is equal to 1 EE reflects feelings of being depleted of energy and drained due to exertive physiological demands. RPA in characterized by attributions of inefficiency reduced motivations and low esteem TRUE = 2 FALSE = 1 IX. HYPOTHESIS The hypothesis of the study is 1. The bank work culture is not having any relation to stress level 2. Information technology is causing stress level 3. The policies of bank are causing or increasing stress levels in terms of A) PHYSIOLOGICAL B) PHSYCHOLOGICAL C) BEHAVIOURAL prepared on seven points. The house and rezzo scale comprising of seven items if it is true is equal to 2, false is equal to 1 EE reflects feelings of being depleted of energy and drained due to exertive physiological demands. RPA in characterized by attributions of inefficiency reduced motivations and low esteem TRUE = 2 FALSE = 1

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References Books Beehr, T.A. & Newman, J.E. (1978). "Job Stress, employ Health and Organisational Effectiveness- A fact analysis model and literature reviews." Caplan, R.D., & Jones, K.W. (1975). "Effects of work load, role ambiguity, and type A personality on anxiety, Depression, and heart rate." Chermiss, C. (1980). "Staff burnout: "Job stress in human service." Beverly Hills: Sage. Dewe, P.J. (1989). "Examining the nature of work stress: Individual evaluations of stressful experiences and coping." Ivancevich, J.M.& Matteson, M.T. (1980). "Stress and Work: A Managerial Perspective." Scottforesman & Co., Glen view Illinois. Ivancevich, J.M., Matteson, M.T. and Preston. (1982). "Occupational Stress: Type A behaviour and physical well being." Kahn et.al. (1964). "Organisational Stress: Studies in role conflict and ambiguity." Wiley, New York. Selye, H. (1974). "Stress without Distress." Harper and Row Publications, U.S.A. Selye, H. (1936). "A syndrome produced by diverse noxious agents." Selye, Hans. (1978). "The general adaptation syndrome and the disease of adaptation." Journal of clinical endocrinology. Shailendra Singh. (1990). "Organisational Stress and Executive Behaviour." Sreeram Centre for Industrial Relation and Human Resources, New Delhi. Shailendra Singh, (1990). "Executive under stress- Exploration in the Structure and Dynamics." Classical Publishing Co., New Delhi.

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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 Performance of Reinforced Concrete Beam under Line Impact Loading I.K .Khan Department of Civil Engineering Aligarh Muslim University, Aligarh-2002002 (U.P.), INDIA ___________________________________________________________________________________________ Abstract: There has been a growing interest in the past few decades among the engineering community to understand the response of reinforced concrete structures subjected to extreme loads due to blast and impact. These severe transient dynamic loads are rare in occurrence but for most of the structures, their effect can result in catastrophic and sudden structural failure. Even residential buildings and other dwelling units are very commonly attacked for spreading panic by terrorists using explosive devices. Missiles and inert projectiles of spherical or cylindrical shape, hitting the structures with high kinetic energy are potent threats to the survival of structures. Thus as concrete is our main construction material it is necessary to know its behaviour under such type impact load. Nowadays, with the rising concern for improved protective civilian structures, these design methods are proving uneconomical and require development of the improved procedures and design tools for impact and blast design of conventional structures. In the present research behaviour of conventionally designed RC beam was studied under line impact using impact hammer testing machine. Keywords: Reinforced concrete, impact load, Impact hammer, Grade of concrete, Shear reinforcement, deflection Crack pattern and Strain energy. _________________________________________________________________________________________ I. Introduction Impact is a daily life phenomenon. It happens whenever two bodies collide. The study of projectile impact has been an area of interest for scientists for about two hundred and fifty years. But for the last four decades this field has become very important for both developed and developing countries. As today the power of a nation is judged on the basis of technology. Every country is investing lot of money for the defense sector and India too is one of them. Every country is developing new missile technology and also the safety devices to have a powerful defense sector. The field of impact is not only confined to defence sector but it has a wide field like in production of high speed blanking and hole flanging, in geology where scientists use improved understanding of earth penetration processes to carry out remote seismic monitoring and surveying not only this but also the automobile engineers are too much concerned with this field for good designing of the vehicles to have least harm to the vehicle’s main structure and the people sitting over it. As the penetration of fragments into targets has long been of interest in military applications, different metallic and non-metallic armours having various thicknesses are being used to give protections to vehicles employed for definite roles in the military operations. The phenomenon of projectile penetration can be categorized by several schemes. For example: on the basis of effect impacts can be classified as either: Hard Impact: when the kinetic energy of the impacting mass is transformed into plastic deformation of the impactor. Soft Impact: when the impactor’s kinetic energy is transformed into deformation energy in both the impactor and the structure. On the basis of velocity it may be classified under the following heads. (i) Freely falling bodies (0 – 25 m/s) (ii) Sub ordinance range (25 – 500 m/s) (iii) Ordinance range (500 – 1300 m/s) (iv) Ultra ordinance domain (1300 – 3000 m/s) (v) Hyper velocity impact (above 3000 m/s) II. Literature Review M. Alam [1], worked on quasi – static and drop hammer loading of plain and skin reinforced concrete plates of thickness 25 and 35mm and of regular strength 15, 25, 35 MPa, with and without edge ring. To reinforce these plates of plain concrete, thin steel sheets (0.15 and 0.2mm thick) have been used on rear or on both faces using Araldite glue. Experiments on these plate specimens have been performed using a cylindrical penetrator with spherical nose positioned at the centre of the plate. M. Kumar et al. [2], developed a relationship for computation of dynamic modulus of elasticity using non – destructive testing. It is observed that dynamic as well as static modulii of elasticity of concrete are largely influenced by the age and grade of concrete. However, hale variation has been observed in case of Poisson’s ratio regardless of the age and grade of concrete.

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A.K.H. Kwan et al. [3], studied that the effects of shock vibration on concrete using a newly developed test method that applies hammer blows to prismatic specimens in the longitudinal direction and evaluates the short and long term effects by observing crack formation, measuring the immediate change in ultrasonic pulse velocity and measuring the reductions in 28days tensile and compressive strengths. A total of 198 prisms cast of typical concrete mixtures with 28days cube strength ranging from 40 to 60MPa had been tested at ages of 12hours to 28days. The tests revealed that the major effect of shock vibration is the formation of transverse cracks. Based on the test results, the shock vibration resistances of the concrete mixtures at different ages were determined and correlated to their material properties. It was found that the single most important material parameter governing of concrete is the dynamic tensile strain capacity finally. Several new sets of vibration control limits, which are less conservative than most existing, ones, have been established. N. Kishi et al. [4] – They worked on “Impact Behavior of Shear-Failure-Type RC Beams Without Shear Rebar”. To establish a rational impact-resistant design procedure of shear-failure-type reinforced concrete (RC) beams, falling-weight impact tests were conducted. Twenty-seven simply supported rectangular RC beams without shear rebar were used. In these experiments, the impact force excited in the steel weight, the reaction force, and the mid-span displacement were measured and recorded by wide-band analog data recorders. After testing, crack patterns developed on the side surfaces of the RC beams were sketched. III. Testing of Materials, Beam Specimens and Test Results A. Test Set up and Testing Materials like cement, fine and coarse aggregates, steel and concrete were tested in accordance with Bureau of Indian Standard (B.I.S.) codes and their properties were found to be within the prescribed limits. Impact test on concrete beams were done with the help of impact hammer testing machine, with impact hammer of equivalent weight 25.40 kg at the level of point including weight 0.675 kg of the impact tool fixed with the impact hammer as showing in the Figure 3 whereas it strikes the beam with a velocity of 4.14 m/s. During test maximum deflection at the centre of beam was recorded with the help of LVDT and crack patterns were also observed after each blow. Total nine beams of size 150mmx150mmx500mm were cast. Each group of three beams were cast with M 20, M25 and M30 grade concrete and beams in each group were designed as under , balanced and over reinforced sections in accordance to IS 456:2000. During test beams were provided simple supports with centre to centre distance of supports as 400mm and an overhang of 50mm on both sides to avoid overturning during testing. The line impact test on concrete beam was done with the help of impact hammer testing machine, the impact hammer of equivalent weight 25.4kg at the level of point, where it strikes the beam with a velocity of 4.14 m/s. The line impact hammer was initially kept horizontal as shown in Figure 1(b) and then allowed to fall freely under gravity to strike the beam horizontally as shown in Figure 1(c). The hammer was lifted manually and was anchored to a wooden trigger, from where it was dropped by trigger a side. The position of trigger was properly marked so that every time hammer is dropped, the height of fall remains constant. At the point of striking a rod of 16 mm was fixed on the impact hammer so that the load acts as a Line load on beam. In the impact hammer testing machine the beam to be tested was clamped with the help of steel rod and plates of diameter 20mm and thickness of plates 12mm. The steel rod had threads at its end which were fastened by bolts. Proper care was taken at the time of experiment to avoid loosening of bolts by wrapping the cotton thread on rod. Wooden packing was also put to avoid any type of slippage. Figure 1 Testing of beam under line impact load

(a)Arrangement for line impact (b) Hammer in horizontal position (c) Testing of beam

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B. Observation and Test Results For the beam cast with M30 grade concrete and designed as balanced section and designated as M30B, deflection and physical condition of the beam after each line impact or blow is shown in the Table 1. These were also recorded for other type of beams viz. M20B, M20O, M25U, M25B, M25O, M30U, and M30O where M20, M25 and M30 are grades of concrete and U, B and O stands for under, balance and over reinforced sections respectively. Test results like strain energy and theoretical deflection etc. obtained by analysis is shown in Table 2 for the beam cast with M30B. Crack patterns observed during line impact test for beam M30B is shown in Figure 2. Table 1 Deflection and physical condition of beam M30B Blow no. 1

Deflection (mm)

Formation of Cracks

Remark

8.135

Two vertical cracks

First crack on left hand side

11.162

First and second increased and third crack also formed

Second crack on right hand side

12.522

Cracks width increased

First crack distance 5cm and second crack distance 10cm from support

13.283

Cracks width increased

Third crack on left hand side

13.872

Cracks width increased

Third crack distance 7.5 cm from support

14.082

Fourth crack formed

Fourth crack on right hand side

14.550

Cracks width increased

Fourth crack distance 2cm from support

14.654

Maximum width of cracks

Beam failed

Table 2 Strain energy and theoretical deflection values for beam M30B Deflection (mm)

W (kN)

8.135

58.38

0.024

Theoretical Deflection (mm) 0.832

11.162

42.55

0.0128

0.606

12.522

37.93

0.0102

0.540

13.283

35.76

0.009

0.509

13.872

34.24

0.008

0.487

14.082

33.73

0.008

0.48

14.550

30.54

0.006

0.43

14.654

30.34

0.006

0.432

Blow No.

Strain Energy ‘U’ (kJ)

Figure 2 crack patterns for beam M 30B

IV. Conclusions On the basis of experimental testing and analysis of reinforced concrete beam under line impact load following conclusions have been drawn: 1. Beam M20U failed at third blow, M20B failed at fifth blow and M20O failed at eight blow. 2. Beam M25U failed at fifth blow, M25B failed at seventh blow and M25O failed at ninth blow. 3. Beam M30U failed at sixth blow, M30B failed at eight blow and M30O failed at twelfth blow. 4. The maximum experimental deflection in beam M20U is 18.23mm, in beam M20B is 17.92 mm and in beam M20O is 17.28mm. 5. The maximum experimental deflection in beam M25U is 17.94mm, in beam M25B is 16.26mm and in beam M25O is 15.58mm.

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The maximum experimental deflection in beam M30U is 15.70 mm, in beam M30B is 14.65mm and in beam M30O is 12.67mm. 7. The maximum theoretical deflection in beam M20U is 3.16mm, in beam M20B is 2.90mm and in beam M20O is 1.46mm. 8. The maximum theoretical deflection in beam M 25U is 1.58 mm, in beam M25B is 1.62mm and in beam M25O is 1.22mm. 9. The maximum theoretical deflection in beam M30U is 0.90mm, in beam M30B is 0.85mm and in beam M30O is 0.74mm. 10. The scabbing started in beam M20U at second blow, in beam M20B at third blow and in beam M 20O at fourth blow. 11. The scabbing started in beam M25U at third blow, in beam M25 B at fourth blow and in beam M25O at fifth blow. 12. The scabbing started in beam M30U at fourth blow, in beam M30B started at fifth blow and in beam M30O started at sixth blow. In view of the above results it can be concluded that with the increase in the grade of concrete and quantity of steel more number of blows are required to cause failure in the beam, whereas maximum experimental and theoretical deflection reduces with the increase in the grade of concrete and quantity of steel. Scabbing in the beams started at increased number of blows as grade of concrete and steel increases in the beam. Thus we can say that impact load carrying capacity of the beam improve by increasing in the grade of concrete and quantity of steel. References 1. 2. 3. 4.

M. Alam, “Quasi-Static & Drop Hammer Loading of Plain and Skin Reinforced Concrete Plates” PhD Thesis, Indian Institute of Technology, Delhi, India , Dec. 1999. M. Kumar, V. Kanwar. And S. Kumar, “Non – Destructive Evaluation of Dynamic Properties of Concrete”, IE (I) Journal, India, Vol. 86, August 2005. A.K.H. Kwan., W. Zhang. and I.Y.T Ng, “Effects of Shock Vibration on Concrete”, A. C. I. Material Journal, USA, Nov. – Dec., 2005. Kishi, N., H. Mikami, and T. Ando, “Impact Behaviour of Shear – Failure – Type RC Beams without Shear Rebar”. Int. J. of Impact Engineering, Vol. 27, pp. 955 – 968, 2002.

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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 Integration of Big Data in Banking Sector to Speed up the Analytical Process 1

Prof. Dr. P.K. Srimani, F.N.A.Sc. 2Prof. Rajasekharaiah K.M. Former Chairman, Dept. of Computer Science & Maths, Bangalore University Director, R & D, Bangalore University, Bangalore, India. 2 Professor & HOD, Department of Computer Science and Engineering, JnanaVikas Institute of Technology, Bangalore Mysore High Way, Bidadi, Bangalore, Visvesvaraya Technological University (VTU), Belgaum, Karnataka, India. 1

____________________________________________________________________________________________________

Abstract: In banking area, we find Big Data which is scattered in different places or sources in heterogeneous format using different Databases or Files. Hence, it is very difficult to analyze the data fastly for making Decision Support System (DSS). In this paper, we have developed a High Level Design (HLD) of Data Warehouse system and making the whole process or the system automated using ETL (Extraction, Transformation and Loading) tools like IBM InfoSphere Information Server, PowerCenter Informatica etc/, In the first phase, Hadoop Data Warehouse is designed by integrating Big Data from various sources like Oracle DB’s, DB2, Sybase, SAP, Data Marts, Flat Files, on WEB SPHERE etc. into a Warehouse in a single format and in one place. Hence, we use ETL tool – Informatica to integrate all banking data and also use “ERWIN” for warehouse design and “SQL LOADER” for fast data transfer. It can be operated on Windows and/or Unix O/s platform. In order to integrate all this data, initially we design a Multi-dimensional Modeling of Data (MDMD) by using Star Schema and Snow Flake Schema. Secondly, we pool all the data in one area called “Staging Area”, from this we make ETL process of all data into Data Warehouse. Keywords: Hadoop Data Warehouse, heterogeneous data, Database files, Flat files, HLD, automated , ETL, Informatica, Web Sphere, Staging Area _________________________________________________________________________________________ I. INTRODUCTION: In this paper, a detailed study of the banking system which uses OLTP (On Line Transaction Processing) for handling the day-to-day transactions and to generate the business analysis reports is made. The existing system provides limited options for analyst to generate reports for future business forecasting and also to develop business strategies. Further, these reports do not support system applications and thus cannot meet the requirements of the Bank to enhance their business objectives. Currently the Big Data in the business is competitive in all directions vertically, horizontally and parallelly. The success of the banking sector or organizations depends on the effectiveness of the use of technology, tools and services in meeting the customer’s requirements and their satisfaction. Certain developmental activities in this direction move through a set of planned strategies consisting of establishment of clear objectives and goals, from the generation of ideas to concept development, service design, prototyping, service launch and customer feedback. As mentioned here some expert of literature exists in this direction but have served major drawbacks. Hence, the present study is carried out [1, 3, 10, 11]. II. OBJECTIVES AND GOALS: A. Objectives: Our research will dwell in the following area:  Data Mining both from structured and unstructured data  Mapping from heterogeneous sources of data through Staging Area into DWH  Big Data integration and analytics to speed up the process for querying or report generation B. Goals: Our research goal is to create DWH using ETL tool – Informatica. This tool is used for analyze DW and provides us various reports of the Bank [2]. The results/solutions are compared with other business analytical tools and prove that the advantages in our solutions are the best to practice and to implement in all business enterprises. III. PROBLEM DOMAIN: Presently, the Big Data is scattered in various sources and also in different formats. We are facing the following problems –

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 It is very difficult to analyze those data fastly  Limited options for analysis  Limited options for analyst to generate reports  Reports taken are not sufficient or sometimes short falls for DSS like business forecasting and to develop business strategies  Reports even do not support some system applications and can’t meet the requirements of the Bank to enhance their business IV. DESIGN PROCESS & DEPLOYMENT: In Fig. 1, the technical diagram of a complex Data Warehouse Architecture (DWA) is presented, Implementations are done by using the following Hardware and Software’s: [5, 6, 8, 9] A Bank needs the development and design of an analytical DWH which is inextricably linked to various business needs. The various design process which involves are – [6, 9]

Figure 1: Technical Diagram of Complex Data Warehouse Architecture 1. OLTP – Transaction Processing The input to the DWH (Data Warehouse) if from various sources likes –  Oracle DWH tables , dup files, data files etc  Flat Files or Text Files, Excel Sheet etc. 2. CRS and SRS (Customer and System Requirement Specifications) 3. ETL (Extraction Transformation & Loading) specifications Involves Source Data to the Target Data 4. HLD – High Level Documents Description of the tools used and naming conventions 5. DWH – Data Warehouse Design It involves three phases of design –  Conceptual Design - (Dimensions and Fact Tables)  Logical Design - (Using Dimension Modeling Technique, Attributes and Constraints)  Physical Design - (Data type, Data size, Data Tables and SQL statements) 6. Loading into DWH (Loading all data from different sources into one storage area i.e. Staging Area into DWH and in one format to make query/retrieval of various reports easily) 7. Testing (Nest step is to test the loaded data by using Unit and System Testing) Unit Testing is done by developer by writing SQL procedure or query. System Testing is done by using Software Testing Tools. 8. Certification (We have to complete ETL specifications with mappings done by developers. If our design meets the ETL specifications then it is implemented.) 9. Production Phase (This is the final phase where in further enhancements are carried out depending upon the customer’s need or requirements, after it is successful, full implementation will be done.) (See Fig. 2) V. CASE STUDY of AFFIN BANK, MALAYSIA: In our research, we implement Data Warehouse Architecture (DWA – Fig.2) which deals with heterogeneous data sets. In the first phase, we have created and designed the Data Warehouse, Dimensions and Fact tables. In the second phase, we are going to mapping with source and target data marts. The bank has a need for an

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analytical data warehouse and a leading bank since from 30 years. Since it is fiancé cum facilitator banking company,

Figure 2: Systems Development Life Cycle Phases (SDLC) it has to be linked with many kinds of business enhancements and competitive edge over business using Information Technology to make –  Better decisions  Dedicated better customer services  Business Intelligence Analysis Further, Bank offers the following additional services to their customers – 1. Offshore Finance 2. Commercial Fiancé 3. Trade Finance 4. Vehicle Fiancé 5. Housing Finance The Bank have number of branches all over South Africa and searching for new business avenues, attracting more new investments and to increase number of customers by using various medias, promoting new finance schemes, implementing new business strategies and decisions. [7] A. SCOPE: The below Fig.4 describes the HLD – High Level Documents requirements of the Data Warehouse System. It is meant for use by the designers and developers and will be the basis for validating the final deliverables of the system. Source1

Source 2

S T A G I N G A R E A

E T L T O O L S

S T A G I N G A R E A

D W H

O L A P

Source n

PHASE 1 & 2

PHASE 3

PH 4 & 5

Figure 3: 5 Phases of Data Warehouse Architecture:

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Figure 4: Physical Data Integration VI. PROPOSED SOLUTIONS: By considering all the above problems and reports, we are finding solutions as – [2, 5, 8]  All these data is to be integrated in a single format and pooling in one place, (see Fig. 2 DWH implementation) so that the mining will be effective and efficient report/information for making proper business analysis and decision making  Using latest ETL technology tools as mentioned earlier for fast processing of data  Mapping is done by using source and target data  Whole process is made Automated by using the above ETL tool – Informatica Power Center Ver.9.0.  Creating High Level Design (HLD) of DW System and making the whole process Automated  Creating of Dimensions (MDDM )and Fact tables  Using ‘ERWIN’ for DW design  Using ‘SQL-LOADER’ for fast data transfer  We design Multi Dimensional Modeling of Data (MDMD) in order to integrate all the data by using Star and Snowflake Schema  After all the above operations is over we will pool all the data in an intermediate area called ‘STAGING AREA’ (Ref. Fig.3)  Finally, from Staging Area, we are going to pool all data into DW by using ETL (See Figs. (3) to (6)).

Figure5: ETL Process

Figure 6: Stakeholders who uses reports

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VII. ADVANTAGES The following are the advantages of the system over the existing system of the Bank: User friendly, easy to handle and flexible in all reports  Cost is reduced by using this tool and also saves lot of time  Uncovering those details which are lacking right before  Validations are made throughout the entire process to avoid occurrence of errors  Error handling and exceptions are made easy by redirecting to a particular box by naming its path.  Error handling with error descriptions are also populated in the system  VIII. APPLICATIONS The applications are widely used in banking sector and the following are various stakeholders –  Business Analysts and Executives  Senior Managers  Top level and Middle level management people to take DSS in their business  Management Information System tool –  To make forecasting of business  To analyze trend identification  To make market analysis  To make competitive business edge in the market  To create global market  Also supports OLAP applications and to generate various reports  IX. CONCLUSION In this paper, we concluded that the Data Integration of Banking Finance System is successfully designed, developed, tested and implemented with case study. Care is taken for data validation check at each level of data flow. Further, the Software is friendly, menu driven, easy accessible and maintainable. X. FUTURE ENHANCEMENT Future enhancements can be done to control data redundancy, data independence, data accuracy and integrity and also recovery from failure. REFERRENCES [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14].

Inmon W.H .”Building the Data Warehouse”, Second Edition ,J Wiley and Sons ,New York,1996 B de ville (2001),”Microsoft Data Mining :Integrated Business Intelligence for E-Commerce and knowledge Management”. Boston: Digital press. Frawley W Piatetsky –Shapiro G and Matheus C ,”Knowledge Discovery in Databases” An overview”.Al Magazine,Fall 1992,pgs 213-228 Integrate the Insight An oracle approach to integrate the big data and white paper. 5. 2012” IBM Global Training outlook “ March 2012.http://www.research.ibm.com/files/pdfs/goto_booklets_executive_review_march_12pdf”. ”Data warehousing Life cycle and ETL tool kit. Informatica Guide Ralph Kimball D Pyle (2003) “Business Modeling and Data mining” Morgan Kaufmann, an Francisco, CA Barry D Data Warehouse from architecture to implementation Addison Wesley 1997. Krulj D “Design and implementation of data warehouse systems .M.Sc. Thesis, Faculty of Organizational sciences, Belgrade 2003. Lohr ,Steve .”The Age of Big Data” “New York Times.11 Feb 2012.http://www.nytimescom/2012/02/12/sunday-review/bigdatas-impact-in-the-world.html? r=2 & pagewanted=all Manyika,James,Michel Chui, Brad Brown, Jacques Bughin ,Richard Dobbs, Charles Rexburg and Angela H.Byers.”Big data: The net frontier for innovations, competition and productivity c Kinsey Global Institute (2011) 1-137 May 2011. Boyd ,Dana and Crawford,Kate “Six Provocations for Big Data”Working Paper –Oxford Internet Institute 21Sept.2011http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431 Boyd, Dana and Crawford, Kate. Bohanec .M (2001) What is Decision Support? Proceedings Information Society IS-2001: Data Mining and Decision Support in action! (pp 86-89), Ljubljana, Slovenia Bajec,M & Krisper,M (2005) .A Methodology and Tool Support for Managing Business Rules in Organizations ,Information Systems,30,423-443 Holsheimer,M (1999) data mining by Business Users :Integrating Data Mining in Business Process. Proceedings International Conference on Knowledge Discovery and Data Mining KDD-99( p.p 266-291) ,San Diego USA:ACM.

ACKNOWLEDGEMENT One of the author’s Mr. Rajasekharaiah K.M. thanks Ms. Chhaya Dule, Asst.Prof. Jyothy Institute of Technology, Bangalore for her valuable suggestions. AUTHOR: Presently Mr. Rajasekharaiah K.M. is working as Professor & HOD Department of Computer Science & Engineering, Jnana Vikas Institute of Technology, Bangalore. He has done M.Tech. in Computer Science & Engg. M.Sc. Information Technology, M.Phil. in Computer Science, and PGDIT from reputed Universities, India. He is having 30+ years of total experience including 16 years of Industrial experiences. He is a Life fellow Member of Indian Society for Technical Education (ISTE), New Delhi. He is presently pursuing the doctoral degree in the Branch of Computer Science & Engineering, in the domain area of Data Mining & Warehousing.

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He has research publications in reputed national and international journals. His other area of interests are DBMS, Software Engg., Software Architecture, Computer Networks, Programming Languages, Data Structures and Mobile Computing. He is also a resource scholar for other Engineering Colleges/University

Screen Shots, Reports and Dashboard Snapshots

Report: 1

Report: 3

Report: 5

Report: 2

Report: 4

Report: 6

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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 Online Examination Application using SAP ABAP Kapil1, Shwetank Sharma2, Sanjay Ojha3 School of Management,Centre for Development of Advanced Computing (CDAC), Noida, Uttar Pradesh, India __________________________________________________________________________________________ Abstract: Online Examination Application is a software solution which allows particular company or institute to arrange, conduct and manage the examination via an online environment, this can be done through, internet, intranet or LAN. The manual procedure used for conducting the exam is a time consuming process and error prone due to human limitations. It is an SAP based web application for conducting examinations through internet or intranet (with in Organizations) for candidates. Keywords: LAN, SAP, ERP, OEA, ABAP __________________________________________________________________________________________ I. Introduction Online Examination Application (OEA) is a Multiple Choice Questions (MCQ) based examination system that provides an easy to use environment for both Test Conductors and Students appearing for Examination. The main objective of OEA is to provide all the features that an Examination System must have. It is an SAP based web application for conducting examinations through internet or intranet (with in Organizations) for candidates. Candidates are given limited time to answer the questions and after the time expiry the paper disables automatically and the answers are sent to the examiner. The examiner will evaluate the answer through automatic process and the result will be sent to the candidate or made available on the website. II. Theoretical Framework A. About SAP SAP is the world leader in enterprise applications in terms of software and software-related service revenue. Based on market capitalization, we are the world’s third largest independent software manufacturer. In 1972, five entrepreneurs had a vision for the business potential of technology. With one customer and a handful of employees, SAP set out on a path that would not only transform the world of information technology, but also forever alter the way companies do business. Now 41 years and 251,000 customers stronger, we’re just getting started. Building on a track record of innovation and a vision proven true throughout every economic and IT shift, now more than ever, SAP is fueled by the pioneering spirit that inspired its founders to continually transform the IT industry. [1] B. About ABAP ABAP is one of the many application-specific fourth-generation languages (4GLs) first developed in the 1980s. It was originally the report language for SAP R/2, a platform that enabled large corporations to build mainframe business applications for materials management and financial and management accounting. ABAP used to be an abbreviation of Allgemeiner Berichts Aufbereitungs Prozessor, German for "generic report preparation processor", but was later renamed to the English Advanced Business Application Programming. ABAP was one of the first languages to include the concept of Logical Databases (LDBs), which provides a high level of abstraction from the basic database level(s). The ABAP language was originally used by developers to develop the SAP R/3 platform. It was also intended to be used by SAP customers to enhance SAP applications – customers can develop custom reports and interfaces with ABAP programming. The language is fairly easy to learn for programmers, but it is not a tool for direct use by non-programmers. Knowledge of relational database design and preferably also of object-oriented concepts is necessary to create ABAP programs. III. System Design A. Context Diagram The context diagram is a top-level view of an information system that shows the boundaries and scope. It describes the main objective of the system and the entities involved. Figure 1: Context Diagram of OEA

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Use Case Diagram

The Use Case Diagram capture the dynamic aspect of the system also shows the functional requirement of the system of OEA. Figure 2: Use Case Diagram of OEA

Process Flow Diagram

The Process Flow Diagram is also known as System Flow Diagram. The main reason for using PFD is to show the relation between major parts of the system. [3] Figure 3: Process Flow Diagram of OEA

IV. Functioning Of OEA The Online Examination is a web based application developed using the ABAP language and utilizes SAP as its platform. The application is responsible for testing the performance of the students where they are required to answer in a pre-specified time limit. The students are required to initially register themselves before taking the test. Only the students who have registered themselves will be allowed take up the test wherein their basic details like the email id, name, address and the contact number is asked. There by every registered candidate is provided with a unique login id and password with the help of which he can take up the project. The end of the test is marked by the termination of the timer which navigates the candidate out of the test screen and hence saves the result of the candidates of which they are informed later. The whole Online Examination is divided into four modules.

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A. Registration Module The registration page of the testing application looks as demonstrated in the screenshot. It is here that the candidate is required to register himself by providing all the asked information. For a successful registration, the student is required to follow the following checks.  There should be no special character in the email id field.  There should be no alpha numeric character in first name field of student.  There should be no alpha numeric character in last name field of student.  There should be no special character in the address of the student.  There should be no alpha numeric character in city field.  The age of the student appearing for the exam should not be less than 18 years and more than 50 years & it should be in between 1950 and 1995.  There should be only numeric characters in Contact Number & Pin code field. Figure 4: Registration Page

After entering all the details, the candidate has to execute the program, it will verify all the checks, if all the checks are followed properly, message for successful registration will come. B. Upload Questions Module In below screen, by clicking select file field, it will ask for delimited text containing questions with answer which can be stored on your system or any removable drives. After selecting the file, a message will pop up confirming the upload of questions successfully. Figure 5: Upload Question Screen

After successful upload, questions will be uploaded in a table which will contain the data from a delimited text file. C. Examination Module Working of examination module can be explained in following steps: For giving the exam, first of all candidates have to Login in the test by User Name and Password which is created by administrator for him/her.

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Figure 6: Login Screen of OEA

After successful login, main screen for OEA will appear. The information about candidate registration id, date of exam and time left for test will appear on top of screen. Figure 7: Main Exam Screen

As soon as the student will enter this page, the timer will start. The time duration of exam will be 20 minutes including the time for reading the instructions. On clicking at the instructions button we have the instructions window which demonstrates the instructions to be followed while taking up the test. Figure 8: Instruction Screen

On clicking on the Start Exam button the candidate can start taking up the exam. Once the start button has been clicked it becomes disabled, meaning that it cannot be clicked again during the entire testing procedure.

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Figure 9: Exam Start Screen

The figure above shows the disabled start exam button which signifies that the test has started and now cannot be re-started again. Also the second pointer shows the timer clock which works in a decrementing trend. Once the timer gets terminated, then the candidate is navigated out of the test window. The figure above also shows the first question that appeared on the test window. After the next question button has been clicked, then it is seen that the question number has been highlighted signifying that the question either has been answered or has been left unanswered. In any question the candidate has the option of jumping back from the current question to any of the previously attempted or viewed question. The figure below thus shows the highlighted question as Q1 after the first question has been answered by the candidate. However, there is no such provision where the candidate can switch to any of the unanswered questions from the current question i.e. the non-highlighted question cannot be jumped to from any of the highlighted ones. Figure 10: Highlighted question screen

The figure below shows that attempt or viewed questions are getting highlighted one by one.

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Figure 11: Attempted Question screen

The figure below shows the screen which shows the number of questions that are being skipped by the candidate during the course of the exam, Candidate can go to those questions directly and answer them. Figure 12: Skipped Question Screen

The figure below shows the submit button which the candidate can click on to after taking up the test. Clicking on this button ends up the test and a message for the same is displayed. Figure 13: Submit Question Screen

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D. Result Generation Module By executing the result program, we can display the result of candidates who appeared for exam. The result for the candidate will be displayed on screen & pdf file containing the result of Candidate will also be generated side by side. Figure 14: Result Generation Screen

After giving all details, Print ALV screen will appear in which we give name of the output device.We are using LP01, which is the default output device of SAP. Result of candidates will be shown as below. Figure 15: OEA Candidate Result generation

Figure 16: OEA Generated pdf Result file

III. Conclusion The On line test System is developed using SAP ABAP fully meets the objectives of the system for which it has been developed. The system has reached a steady state where almost all possible bugs have been eliminated. The

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system is operated at a high level of efficiency and all the teachers and user associated with the system understands its advantage. The system solves the overhead associated with manual type of examination. IV.

References

[1] http://global36.sap.com/corporate-en/our-company/history/index.epx [2] http://www.saphub.com/abap-tutorial/ [3] http://www.edrawsoft.com/Process-Flowcharts.php

IV. Acknowledgments We would like to thank all the faculty members of School of Management CDAC-Noida for sharing their knowledge and experiences towards completion of this project. Without their constant feedbacks this project would have been a distant reality. Our sincere gratitude is to Ms. Mary Jacintha (HOD, school of Management) and all the faculty members for supporting us.

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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 Empirical Study of Extracting information for Business Intelligence V.Jayaraj 1V.Mahalakshmi2* Associate Professor 2 Research Scholar 1,2 School of Computer Science & Engineering, Bharathidasan University, Tiruchirappalli-24, Tamilnadu, India __________________________________________________________________________________________ Abstract: Sentimental/opinion analysis is an emerging area of research in text mining. Sentimental analysis or opinion 1

mining refers to identify and extract subjective information in source materials. As a response to the growing availability of informal opinionated texts like blog posts and product reviews, comments, forums which is collectively called as user generated contents. A field of sentimental analysis has sprung up in the past decades to address the question what do people feel about certain topic? Bringing together researchers in computer science, data mining, sentimental analysis expand the traditional fact-based text analysis to enable opinion- oriented information systems. This paper provides an overall survey about sentiment analysis or opinion mining related to Business intelligence.

Keywords: Opinion mining, Opinion analysis, Text Mining, Business Intelligence. ___________________________________________________________________________________________ I. INTRODUCTION Dealing with the ever growing information in the internet opinion mining plays an essential part in our information gathering before taking an decision. Opinion mining is the area of research refers to identify and extract subjective information in source materials. Opinion mining is also referred as sentimental analysis. Opinion Mining concentrates on classifying documents according to their source materials [1]. The main goal of an Sentimental analysis is to determine the polarity of comments (positive, negative or neutral) by extracting features and components of the object that have been commented on in each document .A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral[2]. As a response to the growing availability of informal opinionated texts like blog posts and product review websites, a field of sentimental analysis has sprung up in the past decades to address the question What do people feel about certain topic? Sentiment classification classifies whether an opinionated document as positive or negative [3]. A text document is classified using a machine learning techniques (Naive Bayes, Maximum Entropy, support vector machines)[4]. A piece of text can be used as an feature or object in opinion mining. The opinion expressed in every document is either direct opinion or comparative opinion. Direct opinion express a target, a person etc. ( e.g) I bought an Nokia x2 mobile. Comparative opinion express e.g. laptop x is cheaper than laptop y. Opinion mining task is carried out in the sentence and document levels. Subjectivity/ Sentence level opinion mining is performed by two tasks. Subjectivity classification identifies whether a sentence is subjective or objective. The research in the field started with sentiment and subjectivity classification, which treated the problem as text classification problem. Sentiment classification classifies whether an opinionated document as positive or negative. Subjectivity classification identifies whether a sentence is subjective or objective. Many applications required more detailed analysis because the user wants to know the opinion of others. Let us consider the following example, (1)I bought an galaxy mobile 4 days ago. (2) It was an beautiful phone.(3)The touch screen was really superb. (4)The Voice excellence was also good. (5)However, my father was fight with me as I didnâ&#x20AC;&#x2122;t inform him before I bought it.(6)He felt that the mobile was too costly, and wanted me to return it to the shop. The question is: what we want to know from this review? There are several opinions in this review (2),(3),(4) express positive opinion, while (5) and (6) express negative opinion. The opinion in sentence (2) is on galaxy mobile, (3) is on touch screen and (4) voice excellence are the features of galaxy mobile. Sentence (6) is on the cost of a galaxy mobile .This is an important place to understand the users are interested on other opinions, but not on all. With this example in mind, we can define opinion mining ,an opinion can be expressed as target, opinion holder, opinion and orientation, direct opinion, comparative opinions.Finding the relevant information about companies from the multiple sources on the web has become increasingly important for business analysts. To get an accurate result of a business entity, text mining tools have been used. With the appropriate tools, company analyst would have to read thousands of reports, news articles etc.This paper is organized as follows: In section 2, various research works has to be analyzed in order to enhance our work. In section 3, our discussion has been described in details. Finally, the paper is concluded by summarizing the work

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II. RELATED WORK Wenhg Zhang et al., [7] identified the weekness of the product by using weakness finder algorithm. The algorithm extract the implicit and Explicit features using morpheme based method and hownet based method to determine the polarity of each sentence. The weakness of the product has to be identified because to know the unsatisfication of the customers and compared with the competitors product reviews to improve their product weakness. Guang Qiu et al.,[8] proposed an advertising strategy DASA to promote advertisement and then to identify the negative review of the customers. These approaches uses pre-set rules, and also design an prototype system for the users. Shumin Zhou, et al.,[9] proposed an architecture to connect the government and the people .The customers may post their opinion by mobile or internet named as information collection channels. The architecture is named as people opinion collection processing. the dataflow process starts and then collect and processing pocp This POCP promotes to build the harmony society. To evaluate the extraction system, we use traditional metrics for information extraction Chinchor et al., [10] calculate the precision, recall, and F-measure values . Precision measures the number of correctly identified items as a percentage of the number of items identified. It measures how many of the items that the system identified were actually correct, regardless of whether it also failed to retrieve correct items. The higher the precision, the better the system is at ensuring that what is identified is correct. Recall measures the number of correctly identified items as a percentage of the total number of correct items measuring how many of the items that should have been identified actually were identified. The higher the recall rate, the better the system is at not missing correct items. The F-measure is often used in conjunction with Precision and Recall, as a weighted average of the two usually an application requires a balance between Precision and Recall. Horacio Saggion et al., [11] f inding the relevant information about companies from the multiple sources on the web has become increasingly important for business analysts. To get an accurate results of an business entity, text mining tools have been used. With the appropriate tools, company analyst would have to read thousands of reports, news articles etc. M.Rushdi Saleh et al.,[12] Opinion mining is receiving more attention due to the increase of blogs, forums, websites etc, support vector machine has been used for testing the dataset and using several weighted schemes. In this work, Support Vector Machines have been applied in order to classify a set of opinions as positives or negatives.svm has achieved good results in opinion mining.svm has also been successfully achieved in many classification tasks. SVM has applied with different features in order to test how the sentiment classification is affected. Different weighting schemes (TFIDF,BO) and n-grams techniques are used. By using the svm tool sentiment orientation classification was fulfilled. Symbolic approaches and machine learning techniques are extended in order to attack the classification of reviews . Dietmar Gräbner et al., [13] proposed a lexicon based approach to classify the customer reviews based on sentimental analysis .when the precision and recall values exceeds the given baseline of our approach with the algorithm for sentimental analysis proved to be successful. Generate a reliable classification approach of customer reviews by applying lexicon based sentimental analysis. Three steps to be carried out to create an lexicon 1.build an lexicon with semantic orientation 2. Create an sentimental analysis based lexicon to generate classification reviews 3.classification results are evaluated with quantitative ratings. Zhongwu Zhai et al.,[14] proposed an several methods have been proposed to extract product features from the reviews. very limited work has been done in the clustering. Lexical similarity can be used in clustering but it was not still accurate because with very high similarities are reliable. so to overcome these problems proposed an semi supervised learning. For semi supervised learning, use the EM algorithm formulated in which is based on NaïveBayes classification. EM algorithm performs much better when compare to the other algorithm. Due the poor performance of the unsupervised methods an EM algorithm based on Naïve Bayes classification is adapted to solve this problem. After a semi supervised method applied then connect feature expressions using sharing words, and then merge components using lexical similarity and select the leader components as labeled data. Alexandra Balahur et al.,[15] proposed an method to evaluate an used generate content. In order get k nowledge from user generated content, automatic methods must to be developed. To multi document summarization of opinions from blogs, forums etc. Vast different approaches have been used to identify the positive a n d n e g a t i v e opinions and then summarize the opinions. The aim of the work is to study the manner in which opinion can be summarized, so that they obtained summary can be used in real-life applications e.g marketing, decision- making. Business Intelligence (BI) is a process for increasing the competitive advantages of a business by intelligent use of available information collection for users to make wise decision [16], [17]. It was well known that some techniques and resources such as data warehouses, multidimensional models, and ad hoc reports are related to Business Intelligence [18]. Although these techniques and resources have served us well, they do not totally cover the full scope of business intelligence [19].

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III. DISCUSSION Sentimental analysis play a vital role in business intelligence and also organizations. Decision making is big issue always in many organizations.80%of information in companies are unstructured data .To get the relevant information from that unstructured information plays an main role for the analyst Information Retrieval concepts plays an main role in classifying unstructured data. By using this technique our work can be extended and the meaningful data can be retrieved. People get the others opinion to make some decision about product or services by this ways.  Finding opinions while purchasing the product  Finding the opinion of the competitor products  Finding opinions on tender result Finally getting an relevant information about product or services plays an main role in an organizations. The core objective of the paper is to develop a methodology to mine the useful information from the unstructured textual content in order to improve the business intelligence. The mining process can be achieved by new emerging technology, which is variant from data mining. With the help of text mining, the user can able to discover previously unknown knowledge in text, by automatically extracting information from different written resources developed in natural languages. It can be now familiar because of its approaches to information management, research and analysis. Thus, text mining is the extension of data mining and obtains the goal of extracting meaningful data from different sources of textual documents. In data mining, the collection of data is stored in the repository known as Data Warehouse. Likely, in text mining, the collection of documents is stored in the repository known as Document Warehouse. From this Document Warehouse, the text has to be extracted using text mining. IV. CONCLUSION AND FUTURE WORK In this literature survey paper it is observed that opinion mining play a vital role to make decision about product or services. Finding the relevant opinions expressed on the web, classifying them and filtering only the positive opinions is not helpful enough for the users. They will still have to sift through thousands of text snippets, containing relevant, but also much redundant information. Many organizations are carry out more research in unstructured data. To get the relevant information text mining and information retrieval concepts has been utilized. The work can be further extended to areas like neural networks, XML data information retrieval. In XML retrieval by using configuration techniques a data retrieval time can be optimized. V. REFERENCE [1]

[2]

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

Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan,“Thumbs up? Sentiment classification using machine learning techniques”, In Proceedings of the Conferenceon Empirical Methods in Natural Language Processing(EMNLP), pages 79–86, 2002. Dietmar Gräbnera, Markus Zankerb, Günther Fliedlb and Matthias Fuchsc “Classification of Customer Reviews based on Sentiment Analysis” In 19th Conference on Information and Communication Technologies in Tourism (ENTER), Springer, Helsingborg, Sweden, 2012. Turney, P, "Thumbs Up or Thumbs Down? Semantic orientation Applied to Unsupervised Classification of Reviews", ACL‟02, 2002. Mital K. Dalal,Mukesh A.Zaveri “Automatic Text Classification: A Technical Review” In International Journal of Computer Applications (0975 – 8887) Volume 28– No.2, August 2011 Kateryna Rybina “ Sentiment analysis of contexts around query terms in documents cin technical universitat Dresden, October 2012 Pang Bo, and Lee Lillian. Opinion Mining and Sentiment Analysis. 2008. Wenhao Zhang, Hua Xu , Wei Wan “Weakness finder : Find Product Weakness from Chinese reviews by using aspect based sentimental analysis” in Expert systems with application 2012 Guang Qiu, Xiaofei He, Feng Zhang, Yuan Shi, JiaJun Bu, Chun chen“DASA:Dissatisfaction –oriented Advertising Based on sentimental Analysis” in Expert Systems with Applications2010 Shumin Zhou, Jumei Ai, Congnian Xu, Bin Tang” The collection and processing platform of the peoples opinion Based on SMS and Internet” in IEEE 2007 Chinchor, N. (1992). MUC-4 Evaluation Metrics. In Proceedings of the Fourth Message Understanding Conference, pp. 22–29. Horacio Saggion “Extracting Opinions and Facts for Business Intelligence” http://www.nist.gov/tac/ M. Rushdi Saleh, M.T. Martín-Valdivia “Experiments with SVM to classify opinions in different domains” in Expert Systems with Applications 38 (2011) 14799–14804 Dietmar Gräbner “Classification of Customer Reviews based on Sentiment Analysis” in 19th Conference on Information and Communication Technologies in Tourism (ENTER), Springer 2012 Zhongwu Zhai “Clustering Product Features for Opinion Mining” University of Illinois at Chicago. Alexandra Balahur“ Challenges and solutions in the opinion summarization of user-generated content” © Springer Science+Business Media, LLC 2012 B. de Ville, “Microsoft Data Mining: Integrated Business Intelligence for e-Commerce and Knowledge Management”, Boston: Digital Press, 2001. P. Bergeron, C. A. Hiller, “Competitive intelligence”, in B. Cronin, Annual Review of Information Science and Technology, Medford, N.J.: Information Today, vol. 36, chapter 8, 2002. Bhujade, Vaishali, “Knowledge Discovery in Text Mining Technique using Association Rules Extraction”, Computational Intelligence and Communication Networks (CICN), International Conference on oct. 2011. M. J. A. Berry, G. Linoff, “Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management”, Wiley Computer Publishing, 2nd edition, 2004.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Hierarchical Decentralized Averaging for Wireless Packet Network 1

A R ASWATHA, 2RAHUL R, 3M PUTTARAJU Department of Telecommunication Engineering, Dayananda Sagar College of Engineering, VTU, Bangalore, India 3 Department of Electronics and communication Engineering, T John Institute of Technology, VTU, Bangalore, India Abstract: Describing and analyzing a hierarchical algorithm, for solving the distributed average consensus problem in wireless sensor networks and overhead of message. The algorithm deals the problem by recursively partitioning a given network into sub networks. Initially, nodes at the finest scale gossip to compute local averages. Using multi-hop communication and geographic routing to gossip between nodes that are not directly connected, local averages are used to compute global average. To attain hierarchical scheme with k levels this is competitive with state-of-the-art randomized gossip algorithms in terms of accuracy, message complexity, node memory. In networks this results in less congestion and resource usage by reducing message retransmissions. Simulations of the proposed scheme compared with theory and existing algorithms based on averaging along paths. Characterizing scaling laws or the rate at which the communication cost increases as a function of network size and to achieve two goals the longest distance a message travels should be much shorter than previous methods and also Distributing the computation evenly across network. Keywords: distributed signal processing, hierarchical processing, Wireless sensor Networks, Consensus algorithms. 1, 2

I. Introduction Distributed signal and information processing applications arise in a variety of contexts including Wireless sensor networks, smart-grid, mobile social networks and large-scale unmanned surveillance. Applications demand protocols and algorithms that are robust, fault-tolerant, and scalable. Energy-efficiency is an important factor [1]. When a system is using battery powered nodes or agents equipped with wireless radios for transmission. Such as in wireless sensor networks energy-efficiency require few message transmissions since consumes bandwidth, each wireless transmission consumes battery resources [2] Always there is a tradeoff between algorithmic simplicity and performance [3,4]. If we only allow pair wise communication between neighboring nodes, we cannot beat barriers. If we have the additional knowledge of geographical information for each node and its neighbors, we can make use of geographic routing [5] and with the added complexity of averaging [6] over paths we can bring the message complexity down to linear at the expense of messages having to travel potentially over hops [7]. However to improve upon the performance achievable using pair wise communication between neighboring nodes, additional complexity is introduced. In this, rather than averaging along paths, convergence is achieved faster when we decompose computation in a multiscale manner. The multiscale approach considered assumes that the nodes know their own and their neighborâ&#x20AC;&#x2122;s coordinates in the unit. Using geographic information, we derive a hierarchical algorithm that asymptotically achieves a communication cost of messages, however, in multiscale gossip, information is only exchanged between pairs, and there is no averaging along paths. At the expense of extra complexity for building the logical hierarchy, we achieve two important goals [8]. First, the longest distance a message travels in multiscale approach should be much shorter compared to geographic gossip or path averaging. Second, multiscale gossip must distribute the computation quite evenly across the network and does not overwhelm and deplete the nodes located closer to the center of the unit square as is the case for path averaging. Finally including a thorough set of experiments to evaluate the performance of multiscale gossip Primary measure of performance is communication cost the number of messages required to compute an estimate of accuracy. We are interested in characterizing the rate, or scaling laws at which the communication cost increases as a function of network size[9,10]. In the analysis of scaling laws for gossip algorithms [11,12], a common study measure of convergence rate is the averaging time. Challlenges: Self-Configuration and Adaptation, Energy Efficiency, Responsiveness, Robustness, Scalability, Heterogeneity, Systematic Design, Privacy and security. Disadvantages of existing methods: i. If message lost, large number of nodes affected

ii. If messages sent over many hops , size increases because accumulates information of intermediate nodes iii. Message size depends on â&#x20AC;&#x2022; length of path and

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―

network size

II. HELPFUL HINTS Multiscale gossip performs averaging in a hierarchical manner. At each moment only nodes in the same level of hierarchy do computations at a local scale and computation at one level begins after the previous level has finished. Decomposing the initial graph into sub graphs in hierarchically, we obtain order in the computation. for a specific decomposition it is possible to divide the overall work into a small number of linear sub-problems and thus obtain very close to linear complexity in the size of the network. Assume we have a random geometric graph G = (V,E) and each node knows its own coordinates in the unit square and the locations of its neighbours. Each node knows the total number of nodes n in the network and k levels desired of hierarchy levels. Figure 1 illustrates an example with k = 3. We use the convention that level k is the lowest level where the unit square is split into small cells. Level 1 is at top level where we have big cells. All cells at the same level have the same area. We split each cell into subcells is directed by a subdivision constant a = 2/3. If a cell contains n nodes, it is split into n1-a cells. Fig.1: Hierarchical multiscale subdivision of the unit square

Algorithm describes multi-scale gossip in a recursive manner. The initial call to the algorithm has as arguments, the vector of initial node values (xinit), the unit square (C = [0; 1] x [0; 1]), the network size n, the top level q = 1, the desired number of hierarchy levels k and the desired error tolerance € to be used by each invocation of randomized gossip. In down-pass unit square is split into smaller and smaller cells all the way to the C(k;) cells. After gossiping in the G(k;_) subgraphs in Line 15, the representatives adjust their values (Line 16). if k is large enough, each G(k;_) is a complete graph. Each node knows the locations of neighbours (needed for geographic routing), at level k we can compute the size of each G(k;_) graph which is needed for the reweighting. The uppass begins with the L(k;_) representatives forming the G(k-1;_) grid graphs (Line 8) and then running gossip in all of them in parallel. Between consecutive levels we use a = 2/3 to decide how many C(r+1;_) cells fit in each C(r;_) cell. Notice the pseudocode mimics a sequential single processor execution. However, it should be emphasized that the algorithm is intended for and can be implemented in a distributed fashion. The notation xinit(C) or xinit(L) indicates that we only select the entries of xinit corresponding to nodes in cell C or representatives L. Algorithm: MultiscaleGossip (xinit, C, n, q, k, €) 1: a = 2/3 2: if q < k then 3: Split C into mq+1 = n1-a cells: C(q+1,1),…,C(q+1,mq+1) 4: Select a representative node L(q+1,i) for each cell C(q+1,i), i €{1,…,mq+1} 5: for all cells C(q+1,i) do 6: call MultiscaleGossip(xinit(C(q+1,i)),C(q+1,i),na; q + 1; tol) 7: end for 8: Form grid graph G(q,.) of representatives L(q+1,i) 9: call Randomized Gossip(xinit(L(q+1;1:mq+1));G(q,.),€) 10: if q = 1 then 11: Spread value of L(2,i) to all nodes in each C(2,i) 12: end if 13: else 14: Form graph G(k;.) only of nodes in V (G) contained in C 15: call Randomized Gossip( xinit,G(k,.), € ) 16: Reweight representative values as : x(L(k,i)) = x(L(k,i)) │V (Gk)│mk-1 / │V (G)│ 17: end if Multiscale gossip has several advantages over Path Averaging. All the information relies on pair wise messages. In contrast, averaging over paths of length more than two has two main disadvantages First, if a

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message is lost, a large number of nodes are affected by the information loss. Second, when messages are sent to a remote location over many hops, they increase in size as the message body accumulates the information of all the intermediate nodes. The message size now depends on the length of the path and ultimately on the network size. Our messages are always of constant size and independent of the hop distance or network size. The ideal scenario for multiscale gossip is if computation inside each cell stops automatically when the desired accuracy is reached. This way no messages are wasted .However in practice for cells at the same level may need to gossip on graphs of different sizes that take different numbers of messages to converge. This needs for a node synchronization so that all computation in one level is finished before the next level can begin. To alleviate the synchronization issue, we can fix the number of randomized gossip iterations per level so that all computation between different sub graphs at the same level takes practically the same amount of time. III. RESULTS In addition to theoretical results, compare multiscale gossip with path averaging via simulation in MATLAB. The experiments, presented, suggest that multiscale gossip has superior performance for graphs of up to many thousands or nodes. Also include an evaluation in scenarios with unreliable transmissions. Fig. 2 shows the nodes co-ordinates initialization for considered no. of nodes in network (Initialized node)

Fig. 3 indicates splitting the area considered into smaller cell (Initial cell splitting)

Fig. 4 indicates gossiping of nodes among the splitted cells (Initialized nodes gossip at level 3)

Fig. 5 indicates the gossiping among the representative nodes of level 3 (Initialized nodes gossip at level 2)

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Fig. 6 indicates the gossiping among the representative nodes of level 2 (Initialized nodes gossip at level 1)

Fig. 7 shows the comparison graph for simulated hierarchy levels 3, 4& 5, for number of message transmission reduced with varying network size

Fig. 8 indicates the number of message transmission reduced with the varying hierarchy levels 3, 4&5

Fig. 9 indicates the simulated graph for the packet averaging for no. of message transmission reduced with network size.

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Fig 9 is compared with the fig. 7 and concluded that hierarchical algorithm Simulation results are better. PERFORMANCE METRICS: Message complexity, Accuracy, Congestion, Resource usage, Energy efficiency, robustness, scalability, self-configuration etc. In this paper the result is discussed for the 3, 4, and 5 gossiping level and compared with each other as shown in the above graphs. IV. CONCLUSION Compare multiscale gossip against path averaging which is in theory the fastest algorithm for gossiping on random geometric graphs. It is worth emphasizing that both algorithms operate under the same two assumptions. First, each nodes know the coordinates of itself and its neighborhood the unit square. Second, each node know the size of the network n. In path averaging this is implicit since each message needs to be routed back to the source through the same path. It is thus necessary that nodes have global unique Ids which are equivalent to knowing the maximum id and thus the size of the network. In multiscale gossip, network size is used for each node to determine its role in the logical hierarchy and also decide the number of hierarchy levels. Advantages are like Message size constant and independent of network size. Nodes update their estimate at each iteration. Less congestion and resource usage, Energy efficient and robustness. In this paper the individual and compared results, graphs is described and analyzed for the gossiping level 3, 4, and 5. V. References Shutao Sun , Simin He , Wen Gao, Bin Pang “Multiscale load adaptive scheduling for energy efficient transmission over wireless networks” Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003. 14th IEEE Proceedings [2] D. Kempe, A. Dobra, and J. Gehrke, “Gossip-based computation of aggregate information,” in Proc. IEEE Foundations of Computer Science, Cambridge, MA, Oct. 2003 [3] F. Benezit, A. Dimakis, P. Thiran, and M. Vetterli, “Gossip along the way: Order-optimal consensus through randomized path averaging,” in Proc. Allerton Conf. on Comm., Control, and Comp., Urbana-Champaign, IL, Sep. 2007. [4] A. Dimakis, A. Sarwate, and M. Wainwright, “Geographic gossip: Efficient averaging for sensor networks,” IEEE Trans. Signal Processing, vol. 56, no. 3, pp. 1205–1216, Mar. 2008. [5] R. Sarkar, X. Yin, J. Gao, F. Luo, and X. D. Gu, “Greedy routing with guaranteed delivery using ricci flows,” in Proc. Information Processing in Sensor Networks, San Francisco, April 2009. [6] F. Cattivelli and A. Sayed, “Hierarchical diffusion algorithms for distributed estimation,” presented at the IEEE Workshop on Statist. Signal Process., Cardiff, Wales, U.K., Aug. 2009. [7] B. Oreshkin, M. Coates, and M. Rabbat, “Optimization and analysis of distributed averaging with short node memory,” To appear IEEE Trans. Signal Processing, 2010. [8] Blywis, Reinecke, Gunes, “Gossip routing, percolation, and restart in wireless multi-hop networks” Wireless Communications and Networking Conference (WCNC), 2012 IEEE [9] Steffens, C. ;Pesavento, M. “A physical layer average consensus algorithm for wireless sensor networks”, 2012, IEEE [10] MengZheng ;Goldenbaum, M. ; Stanczak, S. ; Haibin Yu “Fast average consensus in clustered wireless sensor networks by superposition gossiping” Wireless Communications and Networking Conference (WCNC), 2012 IEEE [11] K. Tsianos and M. Rabbat, “Fast decentralized averaging via multi-scale gossip,” in Proc. Intl. Conf. on Distributed Computing in Sensor Systems, Santa Barbara, CA, June 2010. [12] K. Tsianos and M. Rabbat, “Multiscale Gossip for Eifficient Decentralized Averaging in Wireless Packet Networks” in IEEE Transactions on Signal Processing, Vol. 61, No. 9, May 1, 2013 Author Profile Dr.A.R.Aswatha, received B.E Degree from Mysore University in 1991, M.Tech Degree from M.I.T Manipal in 1996, M.S. Degree from B.I.T.S. Pilani in 2002, Ph.D degree in 2010 from Dr. M.G.R University. Currently he is working as Professor and Head Department of Telecommunication Engineering, Dayanand Sagar College of Engineering, Bangalore, India. His main research Interests include Analysis and design of Low Power VLSI Circuits, Embedded System Design and Image Processing. Email: aswath.ar@gmail.com. Mr. Rahul R, received B.E Degree in Electronics and Communication from Visvesvaraya Technological University in 2012, Currently Pursuing M.Tech Degree in Digital Communication and Networking from Visvesvaraya Technological University in 2014, Department of Telecommunication Engineering, Dayanand Sagar College of Engineering, Bangalore, India. Main Interests in Networking, Digital Communications. Email: rahulrm26@gmail.com. Dr. M.Puttaraju, received B.E Degree from Bangalore University in 1975, M.Tech Degree from Mysore University in 1982, Ph.D Degree in 2012, he is working as Professor and Head Department of Electronics and Communication T.Jhon Institute of Technology, Bangalore, India. Research Interests include Design of Low Power VLSI Circuits and Image Processing. Email: ml.mputtaraju@gmail.com [1]

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Improvement of Quality through Six Sigma: A Case Study R.M.Belokara, Harish Kumar Bangab, Jagbir Singhc, Pratik Belokard a,b,c Production Engineering Department d Chemical Engineering Department a,b,c PEC U&T Chandigarh, dUICET Chandigarh a,b,c,d India _________________________________________________________________________________________________________________________

Abstract— The business objectives are to provide a high quality product to the customer at lower cost and earliest delivery. Six Sigma is a main tool that helps to achieve these objectives. The aim of Six Sigma methodologies is to improve the quality by reducing the number of defects as much as possible. This paper describes the application of Statistical methods in quality improvement after application of Six Sigma .This case study was conducted at Caparo Maruti Ltd, BAWAL which mainly manufactures various sheet metal parts and components associated with automobiles. In this, our focus is mainly on manufacturing of one of these sheet metal parts i.e. Cross member Front (Fr.) Lower (Lwr.) for G model (Maruti Suzuki SWIFT). Pareto Diagram was used to analyze various defects which occur in this sheet metal part and Fishbone diagram is used to explain the causes for defects. The case study recommended measures for Quality Improvement. Keywords—Define, Measure, Analyze, Improve and Control (DMAIC), Defects per Million Opportunities (DPMO), Six Sigma. I. Introduction Six Sigma approach was first introduced and developed at Motorola in early 1990s. Six Sigma has been launched all over the world. Later many companies testified its pivotal role in their success [1]. Well-known examples of Six Sigma companies include Motorola, General Electric, Allied Signal (now Honeywell), ABB, Lockheed Martin, Polaroid, Sony, Honda, American Express, Ford, Lear Corporation and Solectron. Six Sigma is a business improvement approach that seeks to identify and eliminate causes of errors or defects or failures in It is a quality management philosophy and system to reduce the variations, defects and keep the defects out of customer specifications by changing the culture of the organization. The traditional quality management approaches, including Statistical Quality Control (SQC), Zero Defects and Total Quality Management (TQM), have been key players for many years, while Six Sigma is one of the more recent quality improvement initiatives to gain popularity and acceptance in many industries across the globe. Six Sigma differs from other quality programs in its top-down drive in its rigorous methodology that demands detailed analysis, fact-based decisions, and a control plan to ensure ongoing quality control of a process. Six Sigma enables organizations to become more ambidextrous by switching structure, act organically when being challenged by new ideas and operate mechanically in focusing on efficiency [3]. Six Sigma is one of the important driving forces for the organizations to improve their quest for business improvement philosophy. Six Sigma has proved to be a popular approach in driving out variability from processes through the use of statistical tools. Six Sigma allows almost zero defects in all situations. The critical success factors are top management commitment, availability of resources, well designed education and training programmes for appropriate tools and techniques. Sigma, σ, is the Greek symbol for the statistical measurement of dispersion called standard deviation [4]. For this situation, 99.9999998 % of the product or service will be between specifications, and the nonconformance rate will be 0.002 per million, shown below in table I [4]. According to Six-sigma philosophy, processes rarely stay centered; the center tends to shift above and below the nominal target, O. Fig. 1 also shows a process that is normally distributed, but has shifted within a range of 1.5 σ above and 1.5 σ below the target. For the diagrammed situation 99.99966 % of the product or service will be between specifications and the nonconformance rate will be 3.4 ppm (Parts per million), shown below in table II [4]. Whenever process achieves a level not more than 3.4 defects per million opportunities (DPMO), the process has achieved SixSigma level [4]. II Six Sigma Methodology One of the most common methodologies used in Six Sigma is DMAIC methodology: Define Measure, Analyze, Improve and Control. This is briefly described below [5].

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TABLE I NONCONFORMANCE RATE WHEN PROCESS IS CENTERED Specification Limit(Sigma Level)

Percent Conformance

Nonconformance Rate(Parts per million)

± 1σ ± 2σ ± 3σ ± 4σ ± 5σ ± 6σ

68.7 95.45 99.73 99.9937 99.999943 99.9999998

313000 45500 2700 63 0.57 0.002

TABLE II NONCONFORMANCE RATE WHEN PROCESS IS OFFCENTERED ± 1.5 σ Specification Limit(Sigma Level) ± 1σ ± 2σ ± 3σ ± 4σ ± 5σ ± 6σ

Percent Conformance 30.23 69.13 93.32 99.379 99.9767 99.99966

Nonconformance Rate(Parts per million) 697700 308700 66810 6210 230 3.4

Define: The problem is stated in this phase. The product or process to be improved is identified. Customer needs are identified and translated into Critical to Quality Characteristics (CTQs). The problem/goal statement, the project scope, team roles and milestones are developed. A high-level process is mapped for the existing process. Measure: The process performance is estimated in this phase to find the present Sigma level. The key internal processes that influence the CTQs are identified and the defects generated relative to the identified CTQs are measured. Analyze: This phase is used to find the reasons for poor performance. The objective of this phase is to understand why defects are generated. Brainstorming and statistical tools are used to identify key variables that cause defects. The output of this phase is the explanation of the variables that are most likely to affect process variation. Finally the recommended solutions are implemented in the next phase - Improve. Improve: The objective of this phase is to confirm the key variables and quantify the effect of these variables on the CTQs. It also includes identifying the maximum acceptable ranges of the key variables, validating the measurement systems and modifying the existing process to stay within these ranges. Control: The objective of this phase is to ensure that the modified process now enables the key variables to stay within the maximum acceptable ranges, using tools like Statistical Process Control (SPC), simple checklists or we can say this phase is used to find the deviations from the recommended method to the actual progress and reasons for deviations will be analyzed. A. Define Phase This project was carried at Caparo Maruti Limited BAWAL, which mainly manufactures various sheet metal parts and components associated with automobiles. One of these automobile parts which are manufactured in this company is Cross Member Front Lower for model G (Maruti Suzuki SWIFT). The main material used in manufacturing the Cross Member part is cold rolled continuous annealed steel material in the form of metal sheet. These metal sheets are bought from their vendor companies mainly from Tata Steel, Bhushan Steel and Essar etc. Major steps for making the Cross Member part, we have to do various operations on the metal sheets in different compartments or shops i.e. press shop and weld shop. Drawing, Trimming & piercing (11 holes) and Trimming, Piercing & Down Flanging (13 holes) operations are done in press shop. Projection and Spot welding are done in weld shop. The process flow diagram (PFD) is shown in Fig. 2. In this project the management wants to improve the process quality. Even though there are lot of checks at various stages the nonconformance rate is 0.0357%. Management desires to estimate the Sigma level and improve the process from present level[68] B. Measure Phase All the finished Cross Member parts were tested for quality by using visual inspections. Mainly three types of inspections are conducted on all the parts before dispatching to customers. These three types of inspections are (a) on processing visual inspection (b) fitment inspection by manually checking fixture (c) pre- dispatch visual inspection. Data on the basis of these inspections was collected for 5 months. Out of the 187525 units tested, 187458 units passed all the tests and 67 failed in tests. This detail is shown in Table III. Cross Member parts tested for 3 inspections out of which major defects come due to welding failure are in (a) M8 Nut failure & miss, (b) Profile NG, (c) M6 Nut miss, (d) Spot Burn, (e) Flange Damage, (f) Spot Dent. Some minor defects come during press shop operations which are as below: (a) Crack (b) Necking

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Fig. 2 Process flow chart of Cross Member front lower Defects per Million opportunities DPMO 

67  1000000  45 187525  8

i.e Sigma Level of Approximately 5.463 (From Table IV) A. Analyze Phase The Pareto diagram and Line diagram reveals major defect is M8 Nut failure as shown in Fig. 3 & Fig. 4 respectively. This defect occurs during welding operation in weld shop. M8 Nuts weld on Cross Member part with the help of projection welding which plays essential role for assemble automobile subassemblies part. The potential failure mode of M8 nut are (a) peel strength to tension less than required values (b) thread damage (c) nut position shifted w r t centre of hole (d) nut miss. Some of potential effects due to above mentioned failure mode are (a) subassemblies will become functionally impaired & weak (b) problem in bolt tightening (c) pitch of mounting holes will be effected. These potential effects of failures occur throughout the life of the Cross Member part which is very critical mainly for maintenance, accidental & safety purposes throughout the life. If we are able to eliminate the major failure M8 Nut fail, the defective level can be brought down to 0.0181 % from the present level of 0.0357 %. The principle of Pareto diagram also states that investment in improvement of vital few yields better than compared to investment in trivial many. Hence Cross Member defects in M8 Nut failure was taken up for investigation. The major causes for M8 Nut failure was (1) Unskilled operator or bad workmanship, (2) Operator negligency or improper handling of part during operation, (3) Variation in welding current, (4) Variation in welding time, (5) Variation in electrode force, (6) Variation in electrode & tip alignment, (7) Fiber bush wear out (8) Manually operated process, (9) Presence of carbon between electrodes, (10) More carbon presence in component. Fishbone diagram reveals all the major causes of M8 Nut fail & Miss as shown in Fig. 4. M8 Nut failure account 0.0175 %[9-10] C. Improve Phase The causes for M8 Nut failure were analyzed and found that it is due to the bad workmanship or unskilled operator only. The material used is high quality cold rolled continuous annealed steel sheets as a raw material bought from their reputed vendor companies, mainly from Tata Steel Ltd. and others. Firstly, this raw material must be deeply examined before it is introduced in the manufacturing process. Hence defects due to material will be ruled out. The improvement can be brought into the process by proper education & training to the workers by which we can improve quality of our product. The same thing was recommended to the management. If only the M8 Nut failures in Cross Member parts were eliminated, the improvement in Sigma level will be from 5.463 to 5.632[11] Defects per Million opportunities, DPMO 

34  1000000  23 187525  8

TABLE III CROSS MEMBER FRONT LOWER FAILURE DATA Period

01-8-2012 to 31-12-2012

Total Units Tested Units Passed Units Failed Units Failed in M8 Nut Fail & Miss

187525 187458 67 (0.0357%) 33

% Failed in 49.3

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Units Failed in Profile NG

17.9

Units Failed in M6 Nut Miss Units Failed in Spot Burn Units Failed in Flange Damage Units Failed in Spot Dent Units Failed in Crack Units Failed in Necking

10 7 4 1 _ _

14.9 10.4 6.0 1.5 _ _

S. No. 1 2 3 4 5 6 7 8 9 10 11

TABLE IV LEVEL OF SIGMA PERFORMANCE Conformance DPMO (Defects per million Level (%) opportunities) 30.85 69.15 93.3 97.73 99.38 99.795 99.87 99.94 99.977 99.997 99.99966

691500 308500 66800 22700 6200 2050 1300 600 230 30 3.4

Sigma Level 1 2 3 3.5 4 4.375 4.5 4.75 5 5.5 6

Fig. 4 Line Diagram

Fig. 3 Pareto Diagram

Fig. 5 Fishbone Diagram i.e. Sigma level of Approximately 5.632 (From Table IV) Defective level can also be brought down to 0.0181% (From 0.0357 %).

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D. Control Phase Once the solution is implemented, the next step is to place the necessary control to assure that the improvements are maintained long-term. This involves monitoring the key process metrics to promote continuous improvement III. CONCLUSIONS Pareto diagram revealed major failures in M8 Nut test. 49% Cross Member parts failed in M8 Nut test. Hence substantial improvement in the process can be achieved through the elimination of M8 Nut failures. The reasons for M8 Nut failures were identified and solutions were recommended to the management for improvement. This may improve the process from Sigma level of 5.463 to 5.632. IV. SCOPE FOR FUTURE WORK It can also be referred as future scope. Application of Six Sigma in Indian Small scale Industries has large scope, but due to lack of knowledge and awareness on latest quality tools and statistical techniques among the management, its implementation is very limited. In this context, application of Six Sigma methodology on one of the chronic problems uncovered the scope of following further study and research in Indian industries. a) Six Sigma can also be implemented over other SME industries such as foundries, power looms, rolling mills etc. to improve the productivity level. b) Application of DMAIC methodology at other areas such as, accounts receivable, shortening development time of the new products, reducing customer complaints etc. and ultimately deploying Six Sigma company-wide. c) Six Sigma implications can be studied and explored over different service organizations like healthcare, safety care, transportation, traffic management etc.

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

V. REFERENCES Hutchins, D. (2000), “The power of Six Sigma in practice”, Measuring Business Excellence, Vol. 4 No. 2, pp. 26-33. Jiju Antony, “Some pros and cons of six sigma: an academic perspective”, The TQM Magazine 2004, Volume 16, Number 4, pp (303-306). Schroeder, R.G., Linderman, K., Liedtke, C., Choo, A.S., 2008. Six Sigma: definition and underlying theory. Journal of Operations Management 26, 536–554. Prasada Reddy G.P., Reddy V.V., Reddy M., Ravindra, “Six Sigma approach to Quality Improvement- A Case Study”, Industrial Engineering Journal 2011, Volume – II & Issue No.22. Sunil Thawani, “Six Sigma- Strategy for Organizational Excellence”, Total Quality Management July-August 2004, Vol. 15, No. 5-6, pp (655- 664). Blakeslee Jr., J.A., 1999. Implementing the Six Sigma solution. Qualit Progress 32 (7), 77–85. Clifford, L., 2001. Why you can safely ignore Six Sigma. Fortune 14 (2), 140. Fiedler, T., 2004. Mopping up profits: With 3M sitting on solid earnings, CEO James McNerney handled his fourth annual meeting like a contented company veteran. Star Tribune, Metro ed., May 12, Minneapolis, MN. Hahn, G.J., Doganaksoy, N., Hoerl, R., 2000. The evolution of Six Sigma. Quality Engineering 12 (3), 317–326. Harry, M.J., Schroeder, R., 2000. Six Sigma: The Breakthroug Management Strategy Revolutionizing the World’s Top Corporations, Doubleday, NY. Hoerl, R.W., 2001. Six Sigma Black Belts: what do they need to know? Journal of Quality Technology 33 (4), 391–435.

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net An Efficient Face Recognition Technique Using PCA and Artificial Neural Network 1

KARTHIK G, 2SATEESH KUMAR H C Department of Telecommunication Engineering, Dayananda Sagar College of Engineering, VTU, Bangalore, India Abstract: Face recognition is one of the biometric tool for authentication and verification. It is having both research and practical relevance. . Face recognition, it not only makes hackers virtually impossible to steal one's "password", but also increases the userfriendliness in human-computer interaction. Facial recognition technology (FRT) has emerged as an attractive solution to address many contemporary needs for identification and the verification of identity claims. A facial recognition based verification system can further be deemed a computer application for automatically identifying or verifying a person in a digital image.The two common approaches employed for face recognition are analytic (local features based) and holistic (global features based) approaches with acceptable success rates. In this paper, we present a hybrid features based face recognition technique using principal component analysis technique.Principal component analysisis used to compute global feature while the local feature are computed configuring the central moment and Eigen vectors and the standard deviation of the nose,mouth and eyes segments of the human face as the decision support entities of Artificial neural network. Keywords: face recognition; analytic approach; holistic approach; hybrid features; artificial neural network; central moment; eigen vectors; standard deviation I. Introduction Biometrics refers to a science of analyzing human body parts for security purposes. The word biometrics is derived from the Greek words bios (life) and metrikos (measure).Biometric identification is becoming more popular of late owing to the current security requirements of society in the field of information, business, military, e-commerce and etc. For our use, biometrics refers to technologies for measuring and analyzing a person's physiological or behavioral characteristics. These characteristics are unique to individuals hence can be used to verify or identify a person. In general, biometric systems process raw data in order to extract a template which is easier to process and store, but carries most of the information needed. Face recognition is a nonintrusive method, and facial images are the most common biometric characteristics used by humans to make a personal recognition. Human faces are complex objects with features that can vary over time. However, we humans have a natural ability to recognize faces and Indentify person at the spur of the second. Of course, our natural recognition ability extends beyond face recognition too. In Human Robot Interface [3] or Human Computer Interface (HCI), the machines are to be trained to recognize and identify and differentiate the human faces. There is thus a need to simulate recognition artificially in our attempts to create intelligent autonomous machines. Recently face recognition is attracting much attention in the society of network multimedia information access. Basically, any face recognition system can be depicted by the following block diagram. Fig. 1: Basic blocks of a face recognition system.

Pre-processing Unit

Feature Extraction

Training and Testing

1) Pre-processing Unit: In the initial phase, the image captured in the true colour format is converted to gray scale image and resized to a predefined standard and noise is removed. Further Histogram Equalization (HE) and Discrete Wavelet Transform (DWT) are carried out for illumination normalization and expression normalization respectively [4]. 2) Feature Extraction: In this phase, facial features are extracted using Edge Detection Techniques, Principal Component Analysis (PCA) Technique, Discrete Cosine Transform (DCT) coefficients, DWT coefficients or fusion of different techniques [5]. 3) Training and Testing: Here, Euclidean Distance (ED), Hamming Distance, Support Vector Machine (SVM), Neural Network [6] and Random Forest (RF) [7] may be used for training followed by testing the new images or the test images for recognition.

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The popular approaches for face recognition are based either on the location and shape of facial attributes such as the eyes, eyebrows, nose, lips and chin, and their spatial relationships, or the overall analysis of the face image that represents a face as a weighted combination of a number of canonical faces. In the former approach the local attributes of the face are considered in training and testing while the latter approach reckons in the information derived from the whole.The local features based approach demand a vast collection of database images for effective training thus increasing the computation time. The global technique works well with frontal view face images but they are sensitive to translation, rotation and pose changes. Since, these two approaches do not give a complete representation of the facial image, hybrid features based face recognition system using principal component analysis and artificial neural network is designed. This paper presents the face recognition method using PCA that extracts the geometrical features of the biometrical characteristic of the face such as eyes, nose, and mouth and the overall analysis of the whole face. After the pre-processing stage, segments of the eyes, nose and mouth are extracted from the faces of the database. These blocks are then resized and the training features are computed. These facial features reduce the dimensionality by gathering the essential information while removing all redundancies present in the segment. Besides, the global features of the total image are also computed. These specially designed features are then used as decision support entities of the classifier system configured using the Artificial neural network. II. Principle Component Analysis Features of the face images are extracted using PCA in this purposed methodology. PCA is dimensionality reduction method and retain the majority of the variations present in the data set. It capture the variations the dataset and use this information to encode the face images. It computes the feature vectors for different face points and forms a column matrix of these vectors. PCA algorithm steps are shown in Fig 2. Fig. 2: Features Extraction using PCA by computing the Eigenface Images

PCA projects the data along the directions where variations in the data are maximum. The algorithm is follows as:  Assume the m sample images contained in the database as B1, B2, B3………Bm.  Calculate the average image, Ø, as: Ø= ∑ Bl /M, where 1< L<M, each image will be a column vector the same size.  The covariance matrix is computed as by C = BTB where B = [O1 O2 O3….Om].  Calculate the eigenvalues of the mcovariance matrix C and keep only k largest eigenvalues for dimensionality reduction as λk = ∑ n=1(UKT On).  Eigenfaces are the eigenvectors UK of the covariance matrix C corresponding to the largest eigenvalues.

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

All the centered images are projected into faceTspace on eigenface basis to compute the projections of the face images as feature vectors as: w = U O = UT (Bi - Ø), where 1< i<m. PCA method computes the maximum variations in data with converting it from high dimensional image space to low dimensional image space. These extracted projections of face images are further processed to Artificial Neural Networks for training and testing purposes. III. Eigenvector with Highest Eigen Value An eigenvector of a matrix is a vector such that, if multiplied with the matrix, the result is always an integer multiple of that vector. This integer value is the corresponding Eigenvalue of the eigenvector. This relationship can be described by the equation: M × u = × u, where u is an eigenvector of the matrix M is the matrix and is the corresponding Eigenvalue. Eigenvectors possess following properties: • They can be determined only for square matrices. • There are n eigenvectors (and corresponding Eigenvalues) in an n × n matrix. • All eigenvectors are perpendicular, i.e. at right angle with each other. The traditional motivation for selecting the Eigenvectors with the largest Eigenvalues is that the Eigenvalues represent the amount of variance along a particular Eigenvector. By selecting the Eigenvectors with the largest Eigenvalues, one selects the dimensions along which the gallery images vary the most. Since the Eigenvectors are ordered high to low by the amount of variance found between images along each Eigenvector, the last Eigenvectors find the smallest amounts of variance. Often the assumption is made that noise is associated with the lower valued Eigen values where smaller amounts of variation are found among the images . IV. Artificial Neural Network Artificial neural networks, commonly referred to as “neural networks”, has been motivated right from its inception by the recognition that the brain computes in an entirely different way from the conventional digital computer . A neural network is built-up on neurons, which are the basic information treating units. These do a weighted linear combination of their inputs and pass the sum through an activation function of sigmoid type, which acts as a switch and propagates a given input activation further or suppresses it. In order to be able to detect faces, a neural network must first be trained to handle this task. There are differ types of ANN. Some of them are Kohonen networks, Radial Basis Function and Multilayered Perceptron. The multilayered feed forward neural network is shown in Figure 3. It consist of three layers namely input layer, hidden layer and output layer. These layers of processing elements make independent computation of data and pass it to another layer. The computation of processing elements is completed based on weighted sum of the inputs. The output is then compared with the target value and calculation of the mean square error is carried out which is processed back to the hidden layer to tune its weights. Iteration of this process occurs for each layer in order to minimize the error by continually tuning the weight of each layer. Hence, it is known as the back propagation. The iteration process carried out till the error falls below the threshold level. Fig. 3: Multilayered feed-forward network configuration

In face recognition system that uses ANN, the configuration works in the following frames:Input to Feed Forward Network: - Here the parameters are selected to perform required Neural Networks operation i.e. the number of input layers, hidden layers and output layers. These input neurons receives the data from the training set of face images.

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Back Propagation and weight Adjustment: - The input layer processes the data to the hidden layer. The hidden layer computes the data further and then passes it to the output layer. Output layer compares it with that of target value and obtain the error signals. These errors are sent back for weight adjustments of each layer to minimize the error as shown in Fig. 4. Fig. 4: Back Propagation of multilayered ANN

Mathematical Operation: - It performs the mathematical function on the output signal. The functions can be log-sigmoid, threshold function and Tangent hyperbolic function. If the output values of the function are same as that of the output values of the Tested face, the face is detected. Hence, the Neural Networks provides the response to the input which is alike as the training data. V. IMPLEMENTATION PROCESS In this work, PCA technique is used to extract the features of the face images. PCA extracts the variations in the features of face images which contains the highest information with decomposed dimensions. Fig5. Basic blocks for Face Recognition

Extracted features calculate the eigenfaces.These eigenfaces are taken as input to the Artificial Neural Networks for training the neural networks. For testing basis, the eigenface of the tested image is provided as input to the neural networks that are trained and it finds the best match considering the threshold value for rejecting the unknown face images. VI. RESULTS We applied each feature extraction method with Artificial neural network on the SDUMLA-HMT face database. We ex-tracted PCA feature vectors with an application program coded using Matlab 7.0.Tests were done on a PC with Intel Pentium D 2.8-GHZ CPU and 1024-MB RAM. In this study, standard SDUMLA-HMT database images (10 poses for each of 40 people) were converted into JPEG image format without changing their size. For both feature extraction methods a total of six train-ing sets were composed that include varying pose counts (from 1 to 6) for each person and remaining poses are chosen as the test set. Our training sets include 40, 80, 120, 160, 200 and 240 images according to chosen pose count. For each person, poses with the same indices are chosen for the corresponding set. The results obtained are tabulated in Table 1 which shows that the proposed technique is more efficient than face recognition technique using PCA and Support Vector Machine(SVM) Table1: Improvement from the Face Recognition System using PCA and SVM Type of technique

Recognition rate (in %)

Error rate on the evaluation set (in %)

Half total error rate on the evaluation set (in %)

Verification rate at 1% FAR (in %):

1% FAR on the test set equals (in %)

PCA and SVM

85.52

6.64

6.12

50.00

54.14

PCA and ANN

92.99

3.03

2.70

95.49

94.95

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Fig. 6: Recognition rate

Fig. 7: Error rate

Fig. 8: Verification rate

VII. CONCLUSION In this paper, a new Face recognition method is presented. The new method was considered as a combination of PCA, and Artificial neural network. We used these algorithms to construct efficient face recognition method with a high recognition rate. Proposed method consists of following parts: image preprocessing that includes histogram equalization, normalization and mean centering, dimension reduction using PCA that main features

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that are important for representing face images are extracted,and artificial neural network algorithm is employed to train the database. The ANN takes the features vector as input, and trains the network to learn a complex mapping for classification.Simulation results using SDUMLA-HMT face datasets demonstrated the ability of the proposed method for optimal feature extraction. Hence it is concluded that this method has the recognition rate more than 90 %. REFERENCES [1]

[2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

K. Ramesha and K. B. Raja, “Dual transform based feature extraction for face recognition,” International Journal of Computer Science Issues, 2011, vol.VIII, no. 5, pp. 115-120.G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of LipschitzHankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. Khashman, “Intelligent face recognition: local versus global pattern averaging”, Lecture Notes in Artificial Intelligence,4304, Springer-Verlag, 2006, pp. 956 – 961. Abbas, M. I. Khalil, S. Abdel-Hay and H. M. Fahmy,“Expression and illumination invariant preprocessing technique for face recognition,” Proceedings of the International Conference on Computer Engineering and System, 2008, pp. 59-64. S. Ranawade, “Face recognition and verification using artificial neural network,” International Journal of Computer Applications, 2010, vol. I, no. 14, pp. 21-25. Bernd Heisele, Y Purdy Ho, and Tomaso Poggio, “Face recognition with support vector machines: global versus componentbased approach”, in Proc. 8th International Conference on Computer Vision, pages 688–694, 2001. Peter Belhumeur, J. Hespanha and David Kriegman, “Eigenfaces versus Fisherfaces: Recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 1997, vol. XIX, no. 7, pp.711-720. K. Ramesha , K. B. Raja, K. R. Venugopal and L. M. Patnaik, “Feature extraction based face recognition, gender and age classification,” International Journal on Computer Science and Engineering, 2010, vol. II, no. 01S, pp. 14-23. Albert Montillo and Haibin Ling, “Age regression from faces using random forests,” Proceedings of the IEEE International Conference on Image Processing, 2009, pp.2465-2468. H. Murase and S. K. Nayar, “Visual learning and recognition of 3-D objects from appearance,” Journal of Computer Vision, vol. XIV, 1995, pp. 5-24. M. Turk and A. P. Pentland, “Face recognition using Eigenfaces,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591. Peter Belhumeur, J. Hespanha and David Kriegman, “Eigenfaces versus Fisherfaces: Recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 1997, vol. XIX, no. 7, pp.711-720.

Author Profile Mr. Karthik G, received B.E Degree in Electronics and Communication from Visvesvaraya Technological University in 2011, Currently Pursuing M.Tech Degree in Digital Communication and Networking from Visvesvaraya Technological University in 2014, Department of Telecommunication Engineering, Dayananda Sagar College of Engineering, Bangalore, India. Main Interests in Networking, Digital Communications.

Mr. H.C.SATEESH KUMAR H C is a Associate Professor in the Dept of Telecommunication Engg, Dayananda sagar College of Engg, Bangalore. He obtained his B.E. degree in Electronics Engg from Bangalore University. His specialization in Master degree was “Bio-Medical instrumentation from Mysore University and currently he is pursuing Ph.D. in the area of Image segmentation under the guidance of Dr.K.B.Raja, Associate Professor, Dept of Electronics and Communication Engg, University Visvesvaraya college of Engg, Bangalore. His area of interest is in the field of Signal Processing and Communication Engg.

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net An Overview of Changing Trend of Traditional Retailing to i-Retail in India Saha Shantanu1, Rathore Arvind2 Research Scholar1,2 Indian School of Mines, Dhanbad- 826004, (INDIA) Abstract: Retailing may be distinguished as the sale of goods and services from producers / manufacturers or businesses to the end-users. Retailing in simple term can be understood as, selling of goods to consumers, usually in small quantities and not for resale. It is one of the mainstays of economy of any country and in India, it accounts for approximate 10% of the GDP. Thus, the growth of the Indian economy is quiet dependent on the growth of its retail sector. Organized online retail is a new prodigy in India and the market is growing exponentially. With economic growth, resulting in rising per capita income, bringing Indian masses into the consuming classes. The organized retail sector, especially online retail is enticing more and more existing shoppers into its ambit. Compared to traditional retailing, organised online retail in India is highly sporadic, out of order and is at a nascent stage. This article emphasises on online retail service and e-commerce. The key focus of the paper is nurturing online retail sector as an effective tool for business excellence and also for growth of the country at large. An effective retail is expected to help India orient itself effectively for gaining competitive advantage expressed in contemporary business format. Keywords: e-shopping, online retail, segmentation, FDI I Introduction Since early 1990s, the globalization of retail capital was becoming an important phenomenon, dramatically altering both the commercial landscape and nature of consumer society in the emerging markets of East Asia, Latin America, Central and Eastern Europe [18]. Retail has played a major role world over in increasing productivity across a wide range of consumer goods and services. Retail is being the second largest industry in the USA, both in number of establishments and number of employees. Appropriately, the emergence of electronic commerce (‘e-commerce’) was seen to be revolutionary, involving an ‘Upsetting Technology’[6] that had the capacity to bypass (or dis intermediate) incumbent firms, refigure the competitive basis of markets, and in turn, stabilize the emerging economic geographies of globalizing retail [27]. Inspite of being at a nascent stage worldwide, technology of Internet-based distribution system continue to refigure and transform the retail industry, albeit in an evolutionary manner rather than a revolutionary. However, e-retailing is no longer regarded as a threat, sufficient to displace incumbent firms in the retail industry, but, at the same time, it is by no means an insignificant or transient phenomenon. Indeed, e-retail has arguably underpinned a broader shift towards a ‘new economy’, characterized by the networking and moulding of online and offline forms of distribution and consumption. It is important to know how internet retailing differs from conventional retailing method. Adversely there is no accepted classification of different types of retailing in the literature [3]. However, one common distinction is between retailing, where customers come to stores to buy goods and in e-retailing, like direct mail, telephone and television sales, brings stores to the customers [13]. A global management & consultation firm, places India at 6th on a global retail development index. The country has the highest per capita outlets in world i.e. 5.5 outlets per 1000 population. Therefore, approximate 7% of the population in India is engaged in retailing as compared to approximate 20% in the USA [20]. This should not worry the statisticians, as with rising income, Indian consumers have started spending more on non-food items, compared to food products. Thus, there exist an opportunity for online retailers to explore and fathom the potential. In this paper, we discuss the concept of iretailing, as well as examine the various opportunities and challenges related to the Indian context. The paper is divided into six sections. The second section deals with i-retailing in Indian context. The third section includes the description of the emergence of i-Retailing in India. The fourth and fifth sections include Segmentation of eRetailing and subsequently challenges of i-Retailing in India and improvement needed is discussed. In the sixth section, the general conclusion is drawn in the last section. II I-retailing in Indian context In India, the accelerated growth in modern retail is expected to continue for next few years. With consumer demand and business potential, there is a rapid growth in online retail outlets. India's copious ‘Young’

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population and high domestic consumption have manifested favourably to the growth of the sector. According to [24] and others, the market of 21st century will be dominated by multimedia and multi-channel, with customers having a wide range of media and channel options to obtain goods and services. The factors responsible for the development of online retail sector in India can be broadly summarized as:a)

Liberalization of the Indian economy has led to opening up of online market for consumer goods and has helped the Multinational Corporation like e-bay.com and Amazon.com etc. to make significant inroads into this vast consumer market by offering a wide range of choices to the Indian consumers. The Internet Revolution is making the Indian consumer more accessible to growing influences of domestic and foreign retail chains. As India continues to get strongly integrated with the world economy, riding the waves of globalization, the online retail sector is bound to take big leaps in years to come. There is a shift in Consumer Demand for foreign brands. Rising in Income and improvements in easy accessibility are enlarging consumer markets and accelerating the convergence of consumer tastes.

c) d)

As in [11] report, Indian online retail sector is estimated to have a market size of approximately more than 50 billion INR. Most of the organised online retailing in the country is at its nascent stage and more or less been concentrated mainly in the metros. India is one of the last Asian economy, to liberalize its retail sectors, thus, organised e-retailing in the country has a huge potential, waiting to be harnessed, more so, with the growing awareness, the price war, product quality and services coupled with heavy discounts, free delivery / shipment and host of other features have attracted probable buyers towards online shopping. It is expected that, the organized e-retailing industry will continue to grow rapidly, especially with the help of joint ventures and proper distribution channel partners, thereby, increasing their footprints in smaller cities and B-class towns. III Emergence of i-retailing formats in India The following format may be cited as opportunities available to the i-retail firms, for adoption of formats / models to increase their level of penetration into the market. I.

Prominent Format- This type of market (hypermarket, supermarkets, convenience store) are already prevailing in India, besides, online e-retailing and virtual market is a new phenomenon for country like India. Traditional retailers are trying to reinvent their business by introducing online format of retailing, as well as value added services such as credit facility and free home delivery.

II.

Food Retailing- When it comes to food retailing, there is lack and variety of e-retailers in this sector. The reasons could be that, traditional retailers, who operate small single outlet, mainly using family labour, dominate the sector. However, consumers with disposable income prefer to visit supermarkets / convenience store for higher standards of hygiene and attractive ambience.

III.

Health & Beauty Products- Off late, with increase in come and emergence of neo middle class in the country, spending on health and beauty products have increased many folds. The e-retail business is expected to grow significantly in near future due growing quality consciousness among buyers for such products.

IV.

Clothing & Footwear- The traditional footwear and clothing outlets stock limited range of cheap and popular items, in contrast, e-shopping gives a choice of wide diversity under one roof.

Home Furniture & Household Goods- In our country, small time retailers dominate the market. In spite of having a huge potential, very few modern e-retailers have established specialized e-retail for these products. However there is considerable potential for the entry and expansion of specialized eretailing chains in the country.

VI.

Leisure & Personal Goods- Consumer expenditure on leisure and personal goods in the country have increased manifold. There are specialized e-retailers for each category of product such as books, music & etc.

Model of i-Retailing Internet retailing is commonly termed as e-retailing or â&#x20AC;&#x2DC;e-tailingâ&#x20AC;&#x2122;. It actually covers retailing, using variety of different technologies or media with a choice for the Internet retailer to use one or more of the available technologies. To understand e-retailing more effectively, a particular model has been examined as shown in Fig 1 [25].

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Figure 1- Characteristics of Traditional and Internet-Based Media [15]

In some cases, an internet retailer may combine these technologies with elements of traditional store and direct mail models. Whereas, in some other forms, new technologies are used to replace components of store or direct mail retail service (Figure 2). Some of the parallels between an Internet retail site and a ‘real’ store are shown in Table 1. Figure 2- Internet Retail Model

The Critical Factors for successes in direct mail business are:(a) Use of Customer Databases. (b) Easy Ordering. (c) Quick Delivery. Operational Elements which, the Internet retail model shares with both, the retail store and direct mail models are:(a) Billing of Customers. (b) Relationships with Suppliers. Many components of Internet retail and traditional retail models are common. Indeed, the most successful Internet retailers are being those, who are able to successfully transfer critical elements from traditional retailing to the Internet, such as customer service and product displays. Table 1 – Retail Store Activities and Internet Retail Equivalents [23] ‘Real’ Store Activity

Store Promotion

Internet Retail Equivalent Product descriptions, Information pages, Gift services, Search function, Sales clerk on the phone/mail Special offers, Online games and lotteries, Links to other sites of interest, Appetiser information

Store Window Displays

Home page

Store Atmosphere

Interface consistency, Store organisation, Interface and Graphics quality

Aisle Products

Featured products on hierarchical levels of the store

Store Layout

screen depth, browse and search functions, indices, image maps

Store Location

Website links

Checkout Cashier

On-line shopping basket and/or order form Limited to image quality and description, potential for sound and video applications

Sales Clerk Service

Look and Touch of the Merchandise

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Furthermore, traditional retailers are facing increasing competition from two new sources i.e. dis-intermediation by suppliers and new intermediaries in the form of internet retailers [22] Table 2 below:Table 2- Advantages and Disadvantages of Retailers, e-Tailers and Suppliers Possible Advantages

Possible Disadvantages

1. Established Retailers Existing brand name

Channel conflict

Existing customer base

Cultural conflict

Existing supplier contacts Existing distribution system Existing store

2. Start-Ups Knowledge of internet technology and market

Lack well-known brand name

Less constrained by existing systems and culture

Lack customer base Lack supplier contacts Lack distribution system

3. Suppliers Existing brand name

Channel conflict

Existing customer base Existing distribution system

Cultural conflict

In an aeon when volatility is a dominant characteristic of the global market, firms in all sectors of the economy have become more flexible—undergoing a number of structural changes in the face of increasing global competition [5]. Large retailers have also continued to invest heavily in the area of market analysis, research, and advertising, all in an effort to determine who buys, what they buy and where they live [4]. According to the Assoc ham report in 2013, web shopping have increased 250% than what it was five years back, which in turns means 5000 crore INR of transactions. It’s been noticed that, 71% of the ‘Neo Middle Class’ prefers buying items online in comparison to conventional markets [19] see figure 3. Figure 3 - i-next 2013

Beauty Product 4% Jewellery 6%

Health & Fitness Products 5%

Product Sold off include Gift Article 22%

Toys 6%

Home Appliances 6% Hotel Romms 8% Computer and Peripherals 13%

Electronic Gadgets 16% Apparel 14%

Thus, the Pattern of Spending through internet looks like:(a) 57% of the buyers, buys to avail the discount facility. (b) 14% of them goes for gifts. (c) Balance 29% are miscellaneous. The Reasons for Online Shopping are:(a) Convenient and Time saving. (b) Wider Variety. (c) Avoiding Crowd. (d) Cheaper to Buy Online.

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Key Findings(a)

(b)

Age Group. (i) 38% buyers on the net are from age group of 18-28 years. (ii) 52% are from 29-38 years of age. (iii) 8% are from 39-49 years age. (iv) 2% belongs to 50-60 years of age. Preferences. (i) 78% Males preferred online shopping. (ii) 22% Females preferred online shopping.

IV Segmentation of e-retailing Transactions in traditional retailing are intrinsically open to scrutiny and are at risk of imitation and appropriation by competitors. There are three particular, yet interrelated, form of segmentation of the retail business. They are:1. Societal Segmentation- Social establishments are paramount to the birthplace of the firm and industry in question and instrumental in improving and development in provincial market and ventures abroad. 2. Network Segmentation- Alludes to the system; advance of the firm and around the business sector. Its impact on other firm and how others impact in the industry (e.g. the impact of inter-firm and extra-firm relations on their conduct). 3. Territorial Segmentation- It manages how firms are acquired in better places at spatial scales and face to face time and retention of firms inside the social order which are represent and compelled by the administrative schema and the social dynamism of the aforementioned spots (e.g. the degree and nature of relationships between e-retailer and retailer, purchasers and controllers). [12] alludes to, as the 'Placing Firms' inquiry. That is to say, the nature of e-retail identified with the particular places and showcases its characteristics. Hess' terms it as, key to investigate the persuasive relationship between types of societal dug in (The Birth place of the retail) and manifestations of territorial limits (or the attributes of the host situations in which the e-retail work). Utilizing this conceptualization of separate market to shed light on the organizational challenges, which electronic trade postures for the e-retail, we not only focussing on Business-to-Customer (B2C) e-trade, but also, highlighting the percentage of challenge innate to the advancement of business-to-business (B2B) e-trade in the domain of retail supply and item sourcing. B2C e-business postures ability of e-retailers to play immaculate and enter worldwide advertises without bringing about the same levels of investments. e-Trade was seen to engage a type of 'Pure Play' (or non-store based) e-retailers, who could use the Internet to serve national and worldwide customer, showcasing his products without bearing the 'Set Up' and "Gathered" sunk costs / investments (Clark and Wrigley, 1997) of embedded store systems. E-trade intended to detour the accepted channels of retail conveyance and thereby, serves to open up and refigure the organizational challenges of the retail business. V Challenges of i-retailing and improvement needed I-Retailing as an industry in India has a long way to go. To make it a flourishing and a thriving industry, the key challenges are:a) How best to capture and protect this potential source of local innovation and to transfer ‘best practices’ (notably, allusive knowledge) through its intra-firm networks, thereby fostering a process of what might be termed ‘reflexive’ or ‘hybridized’ retail globalization [8]. b) The task of ‘knowledge management’ in the e-retail is made even more challenging by the large number of ‘learning centers and asymmetries in knowledge, capabilities and interpretive frameworks across the firm [4]. c) The pure play e-retailers (especially those in sectors such as food and clothing) had fundamental flaws in their business models [21] their ‘fulfilment’ expenses of picking and delivery were rapidly demonstrated to be ‘killer costs’, whilst issues of ‘tangibility’ and ‘sociality’ resulted in considerable consumer resistance to switching from existing channels [28]. d) The e-retailers faced a competitive response from store-based retailers, who began to re-model themselves into a multichannel organizational form as ‘bricks and clicks’ retailers [10]. e) The threat of pure play e-retailers to the e-retail has so far proved to be negligible. Certain pure plays (e.g. Amazon) have flourished, but only pose a competitive challenge to the e-retail in particular sectors of general merchandise. f) e-Commerce has yet to make a noticeable impact on the landscape of grocery retailing which, despite some significant niche operators [17] continues to be dominated by traditional store-based transactions.

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Given Tesco’s relative success in the nascent online grocery market, the other e-retailers are now actively seeking to integrate B2C e-commerce into their transnational store networks. They have also, to varying degrees, begun to adopt another form of e-commerce, which is oriented around B2B (business-to-business) transactions, particularly in the area of retail supply and product sourcing. It is suggested that, a shift towards a multi-channel paradigm in the retail industry, which is emblematic of a broader shift towards ‘networked’ forms of organization will be the bedrock in the ‘new economy’ [7]. Improvement Needed for i-Retail India consists of 29 states and 18 official languages. A bulk of its population, 66.1 %, lives in rural areas. The lack of adequate infrastructure makes it virtually impossible to reach this virtually untapped market. Distribution, or the lack of it, is a major hindrance for retailers in India. The lack of quality infrastructure across the country and a non-existent distribution sector results in inefficient logistics systems. Infrastructure is the weakest link in India’s path to progress and there is an urgent need to address issues plaguing this area. Transportation is a major concern, deteriorating railway system and limited highway network / freight corridors add to the problem. In contrast to the global standards, the average load carried by trucks in India is around 7 tons, which is very low. Lack of a distribution sector and specialized distribution companies is a major obstacle for e-retailers to fully utilize India’s retail potential. Meanwhile, private logistics companies offering specialized services, like, refrigerated transport and warehouse facilities across the country, along with timely distribution of supplies to retail outlets will create some of the much needed back-end support for retailers to enhance operational performance. (a) If addressed urgently and seriously, infrastructure can translate into India’s biggest opportunity [14]. However, most Indian retail players are under serious pressure to make their supply chains more efficient in order to deliver the level of quality and services that consumers are demanding. (b) Logistical challenges, constant changes in consumer preferences & patterns, crowded marketplaces, efficient customer responsiveness and swiftly evolving retail formats are the hallmarks of today’s retail environment in India. These factors pose a huge challenge for that all-important key to push growth in this hostile environment requiring an efficient and adaptable supply chain management system. (c) In last 2-3 years, several retailers, ranging from F&B operators to apparel, have implemented Supply Chain Management (SCM) solutions to improve core business processes such as global sourcing, distribution, logistics, innovation, transparency and visibility in financials & inventory, and compliance & management of point of sale (POS) data. Going ahead, India’s FMCG and e-retail sectors are likely to see an increase in adoption of SCM. We feel that fraud is going to be one of the retail sector’s primary challenges in future. Fraud and theft, including pilferage by employees, shoplifting, vendor frauds and inaccuracy in supervision & administration will cost the Indian retail very dearly. VI Conclusion This paper has been a pioneering effort in developing an understanding of the recent transformation of i-retail concept. We have laid the necessary groundwork for a more detailed analysis of the increasing concentration of retailing and its locational consequences. Retailing is inherently a geographical phenomenon and market demand, rules the locational dynamics of retailers, which are largely controlled by accessibility. The interaction of threshold ranges from products they sell, cluster dynamics, consumer attitudes and perceptions. India is certainly not alone with regard to structural changes in its e-retail industry and their spatial consequences. Literatures tend to examine the adoption of e-commerce by small-to medium sized businesses. However, as we have attempted to show, it is still possible and indeed, conceptually fruitful, to interrogate the organizational challenge of e-commerce from the perspective of embeddedness. In this context, i-retailing remains considerable scope for theoretical and empirical research into the changing organisational and geographical contours of the firms. Scope for Further Research The scope for further research on the subject as identified and Recommended could be:(a) Case 1- The concept of i-retailing has been adopted by the many retailers in India. The consumers in India are also showing positive response towards online shopping. Considering interest among consumers towards online shopping, future research studies may identify various factors and processes that affects consumer’s buying behaviour towards online shopping. (b) Case 2- Scope of FDI in e-Retail in India. References [1] [2] [3]

Amin, A. 1994. Post-Fordism: models, fantasies and phantoms of transition. In: Amin, A. (Ed.), Post-Fordism: A Reader. Blackwell, Oxford, pp. 1-40. ASSOCHAM report state of ecommerce in India retrieved from http://www.assocham.org/arb/general/Comscore_%20ASSOCHAM-report-state-of- ecommerce-in-india.pdf. Brown, S. (1986) Retail classification: a theoretical note. The Quarterly Review of Marketing 11(2), 12–16.

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[4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]

Baker, S., Baker, K., 1993. Market mapping: How to Use Revolutionary New Software to Find Analyze and Keep Customers. McGraw-Hill, New York. Bluestone, B., Harrison, B., 1982. The Deindustrialization of America. Basic Books, New York. Christensen, C.M., 1997. The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business School Press, Boston, MA. Castells, M., 2001. The Internet Galaxy: ReXections on the Internet, Business and Society. Oxford University Press, Oxford Currah, A.D., Wrigley, N., 2004. Networks of organizational learning and adaptation in retail TNCs. Global Networks 4, 1–23. Clark, G.L., Wrigley, N., 1997. Exit, the Wrm and sunk costs: reconceptualising the corporate geography of disinvestment and plant closure. Progress in Human Geography 21, 338–358. Currah, A.D., 2002. Behind the web store: the organizational and spatial evolution of multi-channel retailing in Toronto. Environment and Planning A 34, 1411–1441. CII perspective on FDI retrieved from http://cii.in/WebCMS/Upload/Annexure%20II%20CII%20Perspective%20on%20FDI%20(2).pdf. Dicken, P., 2000. Places and Xows: situating international investment. In: Clark, G.L., Feldman, M., Gertler, M.S. (Eds.), The Oxford Handbook of Economic Geography. Oxford University Press, Oxford, pp. 275–291. Davies, G. (1995) Bringing stores to shoppers — not shoppers to stores. International Journal of Retail and Distribution Management 23(1), 18–23. Ernest and Young, India at the cusp of digital revolution retrieved from http://www.ey.com/IN/en/Newsroom/Newsreleases/Press-Release_India-at-the-cusp-of-a-digital-revolution Hoffman, D. and Novak, T. (1996) Marketing hypermedia computer mediated environments: conceptual foundations. Journal of Marketing, 50–68. Massey, D., 1984. Spatial Division of Labour: social structure and the geography of production. Macmillan, London. Murphy, A., 2004. The web, the grocer and the city. In: Graham, S. (Ed.), The Cybercities Reader. London, Routledge, pp. 226– 230. Neil Wrigley, Andrew Currah, Globalizing retail and the ‘new e-economy: The organizational challenge of e-commerce for the retail TNCs, Geoforum Volume 37, Issue 3, May 2006, Pages 340–351. Online shopping makes special appearance this Diwali, i-next- vol 7, issue 314, Kanpur Sunday 3rd November 2013, page 10-11. Pest Analysis of Retail Industry in West Bengal. StudyMode.com. Retrieved 12, 2007, from http://www.studymode.com/essays/Pest- Analysis-Of-Retail-Industry-In-128953.html Ring, L.J., Tigert, D.J., 2001. The decline and fall of Internet grocery retailers. International Journal of Retail and Distribution Management 28, 417–444. Sarkar, M.B., et al. (1997) Intermediaries and cybermediaries: a continuing role for mediating players in the electronic marketplace.Journal of Computer-Mediated Communication 3(1). Spiller, P., and Lohse, G.L., 1997-8. A Classification of Internet Retail Stores. International Journal of Electronic Commerce. 2(2), 29-56. Schultz, D.E. (1996) The inevitability of integrated communications. Journal of Business Research 37, 139–146. Stephen Chen, Fiona Leteney (2000) Get Real! Managing the Next Stage of Internet Retail, European management journal vol.18, no. 5, pp 519-528. Thornton, J., Marche, S., 2003. Sorting through the dot.bomb rubble: how did the high-prowl e-tailers fail? International Journal of Information Management 35, 121–138. Wrigley, N., Lowe, M.S., Currah, A.D., 2002. Progress report 2: retailing and e-tailing. Urban Geography 23, 180–197. Wrigley, N., 2000. The globalization of retail capital: themes for economic geography. In: Clark, G.L., Feldman, M., Gertler, M.S. (Eds.), The Oxford Handbook of Economic Geography. Oxford University Press, Oxford, pp. 292–313. Weiss, M.J., 2000. The Clustered World: How We Live, What We Buy, and What It All Means About Who We Are. Little, Brown, Boston, MA. Walker, R., 2000. The geography of production. In: Sheppard, E., Barnes, T.J. (Eds.), A Companion to Economic Geography. Blackwell, Oxford, pp. 113–132.

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International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net PROCESS FLY ASH EFFECT ON HARDEN PROPERTIES OF SELF COMPACTING CONCRETE 1

Mr U. N. SHAH, 2Dr C. D. Modhera Ph. D Scholar, 2Professor, SVNIT, Surat, Gujrat, India __________________________________________________________________________________________ Abstract: This research investigated the Self compacting concrete (SCC) with different amount of process fly ash. As it is a remarkable innovation in concrete technology due to its self flow ability but it requires the large amount of powder content. During study cement is replaced by the process fly ash in variation of 30 to 70 percentage and effect of process fly ash on harden properties of Self Compacting Concrete is studied. During the study Compressive strength, and split tensile strength were carried out according to Indian standards. Result shows the reduction in the strength of the concrete as the fly ash percentage increases. 1

Key Words: Self Compacting Concrete, Fly Ash, Compressive strength, Split tensile strength. __________________________________________________________________________________________ I. Introduction Self-compacting concrete (SCC) is milestone in concrete research. SCC is a highly flow able and, nonsegregating concrete. SCC can spread in to place, fill the formwork and encapsulate the reinforcement without any mechanical vibration for consolidation. SCC was developed by Prof. Okamura at the University of Tokyo, Japan during the year 1986 for improve the quality of construction and also to overcome the problems durability aspect of concrete. It was first completed in 1988 and named as ‘‘High Performance Concrete’’, and later it proposedas ‘‘Self Compacting High Performance Concrete’’. In India, since last many years fly ash is utilized partially in concrete as cement replacement instead of dumping it as a wastematerial. This is a economically viable solution as a replacement of cement in partly as pozzolana, because of its lowers water demand forsimilar workability, reduces bleeding, and lower evolutionof heat. It is already in practice as a particularly in mass concreteapplications and large volume placement to control expansiondue to heat of hydration and also helps in reducingcracking at early ages.High-volume fly ash concrete has emerged as construction material in its own right. This type of concrete normallycontains more than 50% fly ash by mass of total cementations materials [1]. Many researchers have used high volumesof Class C and Class F fly ashes in concrete. In this article,an effort has been made to present the results of aninvestigation carried out to study the effect ofreplacementof cement with high volumes of Class F fly ash on the properties of concrete. II. Experimental Program A. Materials Various materials like cement, fly ash, coarse aggregate, fine aggregate, water, super plasticizer, velocity modifier agent are require to prepare the self compacting concrete. All materials are discussed in brief as follows. Cement: Ordinary Portland cement (53grade) with specific gravity of 3.14 conforming to IS 12269-1987 (ASTM C 150 - 85A). Fly ash: DIRK India’s processed Class F fly ash used, with a properties like specific gravity 2.3 and fineness is less than 18% retained on 45 micron sieve, confirming to IS1727:1967 and (ASTM C 618). Fine aggregate: Locally available river sand of specific gravity 2.7, bulk density 1800kg/m3 which confirms the Zone II as per IS: 2386 (Part I). Coarse aggregate: Crushed granite course aggregate of 10 mm down size with specific gravity of 2.8 and bulk density of 1450 kg/m3 confirms to ASTM C 33-86. Water: Potable water confirms to ASTM D 1129, for mixing the concrete and curing of the specimens. High range water reducing admixtures (HRWRA): Polycarboxylic ether (PCE) based super-plasticiser confirms to ASTM C 494-92 Type A and Type F in aqueous form to enhance workability and water retention. Viscosity modifying admixture (VMA): A polysaccharidebased VMA, to enhancesegregation resistance, toimprove the viscosity and to modify cohesiveness ofthe mix. B. Mix design In the study, powder content consist cement and fly ash and other materials such as coarse aggregate, fine aggregate, water, super plasticizer, VMA in various proportions. Study conducted on five different mixes as a

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ordinary Portland cement is partially replaced with fly ash. Fly ash is replaced in different percentages from 30% to 70%i.e. 30%, 40%, 50%, 60% and 70%. A detail mixproportion is listed in table- 1. A mix with 30% fly ash content is labeled as A30 and in the same manner all other mixes are also labeled.

Table 1: Mix proportion of SCC mixes Mix

W/(C+FA) Ratio

Water Lit/

Cement kg/

Fly Ash kg/

Fine Aggregates kg/

A 30 A 40 A 50 A 60 A 70

0.34 0.34 0.34 0.34 0.34

190.4 190.4 190.4 190.4 190.4

392 336 280 224 168

168 224 280 336 392

973.0 973.0 973.0 973.0 973.0

Coarse Aggregates kg/ 595.33 595.33 595.33 595.33 595.33

V.M.A. Lit/

S.P. Lit/

0.56 0.56 0.56 0.56 0.56

5.6 5.6 5.6 5.6 5.6

C. Tests on Hardened SCC Forty five cubes of (150 x 150 x 150 mm) and forty five cylinders (100 mm in height, 200 mmin diameter) were subsequently cast withoutvibration for five different mixes. The cubes, cylinders were stored in the lab after casting. After thumb impression, all specimens were de-molded, marked and cured in the water at room temperature untilthe date of testing. Every testing was completedon three samples and the average value reported.Tests on the cast specimens include compressive strength, splitting tensilestrength test. Test results are given in Table 2 and Table 3 respectively. Table: 2 Compressive Strength of SCC

Table: 3 Split Tensile Strength of SCC

Split Tensile Strength (Mpa)

Compresive Stregth (MPa)

III. Discussion on the result A. Compressive Strength of concrete The effect of process flyash as replacement of cement is shown in the fig.1. Using relatively higher percentage of fly ash replacement of cement reduce the compressive strength of concrete. Study shows the strength reduction from 50 Mpa to 25 Mpa for the fly ash replacement of 30 percentages to 70 percentages at the age of 28 days. 7 Days 28 Days 56 Days 7 Days 28 Days 5.00 60.00 4.50 50.00 4.00 40.00 3.50 3.00 30.00 2.50 20.00 2.00 10.00 1.50 0.00 1.00 A30 A40 A50 A60 A70 A30 A40 A50 A60 A70 Design Mix Design Mix Fig. 1: Compressive strength of SCC

Fig. 2: Split Tensile strength of SCC

B. Split Tensile Strength of concrete Split tensile strength study with different percentage of process fly ash as replacement of cement is shown in the fig. 2. Using relatively higher percentage of fly ash replacement of cement reduce the Split tensile strength of

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concrete. Study shows the strength reduction from 3.89Mpa to 2.39Mpa for the fly ash replacement of 30 percentages to 70 percentages at the age of 28 days. C. Relationship Between compressive strength and split tensile strength of SCC Based on the result of the compressive strength and the split tensile strength of various grades of different SCC mixes at ages of 28 days curing, graph are plotted with tensile strength vs. compressive strength and suitable linear relations are developed between tensile strength and compressive strength for various grades of SCC ranges from 25 to 50 Mpa. Fig.3 shows the relationship between split tensile strength and compressive strength.

Fig. 3: Relationship between Compressive strength and Split Tensile strength of SCC The proposed equation is y = 0.063x + 0.657 Where depended variable Y is Split Tensile Strength in Mpa and Independed variable X is compressive strength in Mpa. Table 4: Comparison of proposed relation with the experimental results. Sr No.

As per test results Compressive Strength (Mpa)

Split Tensile Strength (Mpa)

Percentage error

20.34

1.9

1.94

1.98

24.07

2.1

2.17

3.37

25.86

2.39

2.29

4.54

29.86

2.29

2.54

9.77

31.3

2.64

2.63

0.42

32.45

2.71

2.70

0.32

32.65

2.79

2.71

2.81

37.37

3.01

0.37

38.07

3.12

3.06

2.11

40.44

2.92

3.20

8.88

43.99

3.63

3.43

5.88

46.79

3.31

3.60

8.17

49.75

3.89

3.79

2.61

50.37

4.12

3.83

7.56

56.81

4.41

4.24

4.1

Average percentage error

As per proposed equation

4.19

IV. Conclusions SCC mixes are prepaid for different amount of flyash as a cement replacement, ranging from 30 to 70 percentages. During the study reduction in compressive strength observed as the fly ash percentage got increase. It is observed that the split tensile strength of SCC got reduced as the flyash percentage increased. About 60 percentage split tensile strength got reduced at the age of 28 days.

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3. 4.

A single relation is developed for the tensile strength of all design mix of SCC, which is given by y = 0.063x + 0.657. The percentage error of the proposed relation in comparison to the experimental result is found to be less than 4.19% on an average, which shows the reliability of the proposed equation. References

[1]. [2]. [3]. [4]. [5]. [6].

Jagadish V, Sudharshan MS, Ranganath RV. 2003, Experimental study for obtaining self-compacting concrete. Indian Concrete Journal, vol 77 (8) pp.1261–1266. Khatib J.M., 2008, Performance of self-compacting concrete containing fly ash, Construction and Building Materials 22, pp. 1963–1971. Mazaheripour H., Ghanbarpour S., Mirmoradi S.H., Hosseinpour I. 2011, The effect of polypropylene fibers on the properties of fresh and hardened lightweight self-compacting concrete, Construction and Building Materials 25, pp. 351–358. Miao Liu, 2010, Self-compacting concrete with different levels of pulverized fuel ash, Construction and Building Materials 24, pp. 1245–1252. Ozawa K, Maekawa K, Okamura H. 1989, Development of the high performance concrete., Proc JSI vol. 11(1) pp. 699–704. Specification and Guidelines for Self-Compacting Concrete, EFNARC, Surrey UK, Feb -2002.

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net A CRITICAL REVIEW ON DIFFERENT HEAT EXCHANGERS USED FOR HEAT TRANSFER BETWEEN TWO FLUIDS Avinash D. Jadhav1, Tushar A. Koli2, Vijay H. Patil3 Department of Mechanical Engineering GFâ&#x20AC;&#x2122;s Godavari College of Engineering, Jalgaon, INDIA Abstract: Heat exchangers are those devices which are used to transfer heat from hot fluid to cold fluid which are of either same or different phases. Heat exchangers are used in wide range for different types of industrial and domestic applications. Some of the heat exchangers are mixing type and some are non-mixing type. The difference between the mixing and non-mixing is that in non-mixing type the fluids are separated by metal wall. In non-mixing type as there is used a metal wall to separate the two different fluids, the heat transfer takes place by convection in each fluid and by conduction through the walls, so that in the analysis of heat exchanger, it is necessary to calculate overall heat transfer coefficient U. Keywords: Overall heat transfer coefficient, area density, tube spacing, I.

Introduction

To achieve a particular engineering objective, it is very important to apply certain principles so that the product development is done economically. This economic is important for the design and selection of good heat transfer equipment. The heat exchangers are manufactured in different types, however the simplest form of the heat exchanger consist of two concentric pipes of different diameters known as double pipe heat exchanger. In this type of heat exchanger, one fluid flows through the small pipe and another fluid flows through the space between both the pipes. The flows of these two different fluids, one is at higher temperature called hot fluid and another is at lower temperature called cold fluid, can be in same or in opposite directions. If the flows are in same direction then the heat exchanger is called as parallel flow heat exchanger and if the flows are in opposite direction then the heat exchanger is called as counter flow heat exchanger.

(a) Parallel flow (b) Counter flow Fig. 1: Double pipe heat exchangers with different flow and their respective temperature profile. The further development is done in the heat exchangers to facilitate them in different applications as per requirement. These heat exchangers are different from the conventional heat exchangers such that they have large heat transfer surface area per unit volume and are known as compact heat exchangers. For the compact heat exchangers,

The heat exchangers having area density

are known as compact heat exchangers

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II. 1. 2. 3. 4. 5. 6.

Followings are some examples of heat exchangers used in different applications:

Intercoolers and preheaters; Economisers and superheaters; Condensers and boilers in steam plant; Condensers and evaporators in refrigeration units; Regenerators; Automobile radiators. III.

Different types of heat exchangers used

Following are the different types of heat exchangers used based on the various applications. 1. Shell and tube heat exchanger: Shell and tube heat exchangers are commonly used in the chemical and process industries. These devices are available in a wide range of configurations as defined by the Tubular Exchanger Manufacturers Association (TEMA). The applications of single-phase shell-and-tube heat exchangers are quite large because these are widely in chemical, petroleum, power generation and process industries. In essence, a shell and tube exchanger is a pressure vessel with many tubes inside of it. One process fluids flows through the tubes of the exchanger while the other flows outside of the tubes within the shell. The tube side and shell side fluids are separated by a tube sheet. In these heat exchangers, one fluid flows through tubes while the other fluid flows in the shell across the tube bundle.

Fig. 2: Shell and tube type of heat exchanger. The baffles are primarily used in shell-and-tube heat exchangers for supporting the tubes and for inducing cross flow over the tubes, resulting in improved heat transfer performance. To induce turbulence outside the tubes it is customary to employ baffles that cause the liquid to flow through the shell at right angles to axes of the tubes. In these heat exchangers, the shell-side flow is complicated for two reasons, the first is the approximately sinusoidal overall flow pattern as the fluid flows through the tube bundle, and the second is the influence of the various leakages through the clearances required for the construction of the exchangers. The various tube arrangements are as shown in fig. given below.

Fig. 3: Various tubes arrangements in shell and tube type of heat exchanger. 2. Condenser: Condensers are the types of heat exchangers used to condense a substance from its gaseous to its liquid state. In this process, the hot fluid (or gases) gives its latent heat to the cold fluid and comes to the liquid state. The

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condensers are used for industrial as well as domestic purpose. They are available for various ranges of size and shape. For example the condensers used in domestic refrigerator and air conditioners are quite small and the condensers used in power plants are of bigger sizes. Use of cooling water or surrounding air as the coolant is common in many condensers. The main use of a condenser is to receive exhausted steam from a steam engine or turbine and condense the steam. Fig. is showing water cooled condenser and air cooled condenser as well.

(a) Water cooled condenser

(b) Air cooled condenser Fig. 4: Water cooled condenser

3. Economiser and air-preheater: Economiser is a type of heat exchanger commonly used in a steam power plant particularly to heat the water entering the boiler so that the fuel consumption is reduced. Econimiser can be used anywhere. The economiser not only pre-heats the feed water but also lowers the temperature of the flue gases flowing to the atmosphere. Economisers are basically tubular heat transfer surfaces used to preheat the boiler water before it enters the drum. The term economizer comes from early use of such heat exchangers to reduce operating cost or economize on fuelby recovering extra energy from flue gas. The different tubular arrangement of the economiser is as shown in fig. 5.

(a) In-line arrangement

(b) Staggered arrangement

Fig. 5: Economiser air-preheater tube arrangement Air-preheater is the also a type of heat exchanger and used in the steam power plant just like the econimiser. The difference between these two is that the air-preheater is used to preheat the air entering the furnace. The purpose is that the efficiency of the boiler and hence steam power plat increases as the fuel consumption decreases. 4. Radiator: Radiators are the types of heat exchangers used to transfer thermal energy from one medium to another for the purpose of cooling and heating. The major applications of radiators are inautomobiles, buildings, and electronics.The radiator is always a source of heat to its environment, although this may be for either the purpose of heating this environment, or for cooling the fluid or coolant supplied to it, as for engine cooling. The radiators used in the cars and heavy vehicles are the compact heat exchangers. In compact heat exchangers, the two fluids usually move perpendicular to each other, and such flow configuration is called cross-flow.the crossflow is said to be unmixed since the plate fins force the fluid to flow through a particular interfinspacing and prevent it from moving in the transverse direction (i.e., parallel to the tubes).

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Fig. 6 below shows the cross flow arrangement of heat exchangers. Generally in the cross flow heat exchangers, both the fluid remains unmixed.

Fig. 6: Cross flow heat exchanger.

IV.

Fig. 7: Car radiator.

Coclusion

The review indicates that Heat Exchangers are the heat transfer devices which are used in different applications. The heat exchangers can be used to recover the resources like water as it is converted into the steam which is condensed by using the condenser. Heat exchangers also useful for the economical running of industries and to control the pollution as in case of economizer and air pre-heater. The heat exchangers are also be used for cooling purpose as in case of radiators. So it is obvious that the heat exchangers are the useful tools in the industries. V.

References

[1]

Apu Roy, D.H.Das, “CFD Analysis of a Shell and Finned Tube Heat Exchanger for Waste Heat Recovery Applications”, International Journal of Mechanical & Industrial Engineering, Volume-1 Issue-1, 2011

[2]

S. S. Shinde, P. V. Hadgekar, S. Pavithran, “Comparative Thermal Analysis of Helixchanger with Segmental Heat Exchanger Using Bell delaware Method”, International Journal Of Advances In Engineering & Technology, May 2012. ISSN: 2231-1963

[3]

Manish Baweja, Dr. D. N. Bartaria, “A Review on Performance Analysis of Air-Cooled Condenser under Various Atmospheric Conditions”, International Journal of Modern Engineering Research, Volume-3, Issue-1, Jan-Feb 2013.

[4]

A. D. Patil, P. R.Baviskar, M. J.Sable, S. B.Barve , “Optimization of Economiser Design for the Enhancement of Heat Transfer Coefficient”, International Journal of Applied Research in Mechanical Engineering, Volume-1, Issue-2, 2011.

[5]

P. S. Amrutkar, S. R. Patil, “Automotive Radiator Performance-Review”, International Journal of Engineering and Advanced Technology, Volume-2, Issue-3, Feb-2013

Acknowledgments Firstly the author would like to thank her parents for their best wishes and Special thanks to Assistant Professor Tushar A. Koli of Mechanical engineering Department, NMU Jalgaon, India for his guidance about the selected topic. The author greatly expresses her thanks to all people whom will concern to support in preparing this paper.

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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 highly efficient method for denoising of an image using gradient histogram preservation Sainath1, Nagarathna2 Department of telecommunication, Dayananda Sagar College of Engineering, VTU, Bangalore, India 2 Department of telecommunication, Dayananda Sagar College of Enginnering, VTU, Bangalore, India 1

Abstract: Image denoising is one of the basic problems in low level vision. It is one of the best methods to evaluate various statistical image modeling methods. One of the most important problems in image denoising is how to preserve the fine scale texture structures while removing noise. To address this problem, we propose a texture enhanced image denoising (TEID) method by using the gradient distribution of the denoised image to be nearly close to the estimated gradient distribution of the original image .Some natural image priors, such as nonlocal self-similarity prior, sparsity prior, and gradient based prior, have been used extensively for noise removal. The denoising algorithms based on these priors tend to remove the detailed image textures, degrading the image visual quality. A gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Keywords: Similarity prior, Sparsity prior, Gradient based prior, Gradient histogram preservation

I. Introduction The main aim of image denoising is to estimate the best clean image x from its noisy observation y. One commonly used observation model is y = x + v , where v is additive white Gaussian noise. Image denoising is a fundamental yet still active topic in image processing and low level vision, while it is an ideal test bed to evaluate various statistical image modeling methods. In general, we hope that the denoised image should look like a natural image, and therefore the statistical modeling of natural image priors is crucial to the success of image denoising. With the rapid development of digital imaging, the resolution of imaging sensor is getting higher and higher. On one hand, more fine texture features of the object and scene will be captured; on the other hand, captured high resolution image is more prone to noise because the smaller size of each pixel makes the exposure less sufficient. However, suppressing noise while preserving textures is difficult to achieve simultaneously, and this has been one of the most challenging problems in natural image denoising. Unlike large scale edges, the fine scale textures have much higher randomness in local structure and they are hard to characterize by using a local model. Considering the fact image are homogeneous that texture regions in and are usually composed of similar patterns, statistical descriptors such as histogram are more effective to represent them. Actually, in literature of texture representation and classification global histogram of some local features is dominantly used as the final feature descriptor for matching. Meanwhile, image gradients convey most of semantic information in an image and are crucial to the human perception of image visual quality. All these motivate us to use the histogram of image gradient to design new image denoising models. (1) A novel image denoising framework, i.e., TEID, is proposed, which preserves the gradient distribution of the original image. The existing image priors can be easily incorporated into the proposed framework improve the quality of denoised image. (2) A histogram specification function is developed to ensure the gradient histogram of denoised image being close to the reference histogram, resulting in a simple yet effective GHP based TEID algorithm. (3) A simple solid algorithm is presented to estimate the gradient histogram from the given noisy image, making TEID practical to implement. II. Denoising with gradient histogram preservation In this, we present the image denoising model by gradient histogram preservation with sparse nonlocal regularization, and then present an effective histogram specification algorithm to solve the proposed model for texture enhanced image denoising. Given a clean image x, the noisy observation y of x is usually modeled as y = x + v eq (1) where v is the additive white Gaussian noise (AWGN) with zero mean and standard deviation . The goal of image denoising is to estimate the desired image x from y. One popular approach to image denoising is the variational method, in which the denoised image is obtained by (1)

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where R(x) denotes some regularization term and μ is a positive constant. The specific form of R(x) depends on the used image priors. One common problem of image denoising methods is that the image fine scale details such as texture structures will be over-smoothed. An over-smoothed image will have much weaker gradients than the original image. Intuitively, a good estimation of x without smoothing too much the textures should have a similar gradient distribution to that of x. With this motivation, we propose a gradient histogram preservation (GHP) model for texture enhanced image denoising. Our intuitive idea is to integrate the gradient histogram prior with the other image priors to further improve the denoising performance. Suppose that we have an estimation of the gradient histogram of x, denoted by hr (the estimation) method will be discussed in Section 4). In order to make the gradient histogram of denoised image x nearly the same as the reference histogram hr, we propose the following GHP based image denoising model:

Where F denotes an odd function which is monotonically non-descending in (0, +∞), hF denotes the histogram of the transformed gradient image |F (∇x)|, and ∇ denotes the gradient operator. By introducing the transform F, we can usethe alternating method for image denoising. Given F, we can fix ∇x0 = F(∇x), and use the conventional denoising methods to update x. Given x, we can update F simply by the histogram operator introduced Thus, with the introduction of F, we can easily incorporate gradient histogram prior with any existing image priors R(x). The sparsity and NSS priors have shown promising performance in denoising, and thus we integrate them into the proposed GHP model. Specifically, we adopt the sparse nonlocal regularization term proposed in the centralized sparse representation (CSR) model, resulting in the following denoising model:

where λ is the regularization parameter, D is the dictionary and α is the coding coefficients of x over D. Let’s explain more about the model in Eq. (3). Let xi =Rix be a patch extracted at position i, i = 1, 2, . . . , N, where Ri is the patch extraction operator and N is the number of pixels in the image. Each xi is coded over the dictionary D, and the coding coefficients is αi . Let be the concatenation of all αi and then x can be reconstructed by

In Eq. (3), βi is the nonlocal means of _i in the sparse Coding domain. With the current estimate ˆx, we use the blocking matching method as in to find the non local neighbors of then βi is computed as the weighted average (5) where the weight is defined as

From the GHP model with sparse nonlocal regularization in Eq. (3), one can see that if the histogram regularization parameter μ is high, the function F (∇x) will be close to ∇x. Since the histogram hF of |F (∇x)| is required to be the same as hr , the histogram of ∇x will be similar to hr, leading to the desired gradient histogram preserved image denoising. Next, we will see that there is an efficient iterative histogram specification algorithm to solve the model in Eq. (3). III. Iterative histogram specification algorithm Eq. (3) is minimized iteratively. As in , the local PCA bases are used as the dictionary D. Based on the current estimation of image x, we cluster its patches into K clusters, and for each cluster, a PCA dictionary is learned. Then for each given patch, we first check which cluster it belongs, and then use the PCA dictionary of this cluster as the D. We propose an alternating minimization method to solve the problem in Eq. (3). Given the transform function F, we introduce a variable g = F (∇x), and update x (i.e., α) by solving the following subproblem:

To get the solution to the above sub-problem, we first use a gradient descent method to update x:

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Then, the coding coefficients αi are updated by: By using Eq. (5) to obtain βi, we further update αi by :

Once the estimate of image x is given, we can update F by solving the following sub-problem:

Finally, we summarize our proposed iterative histogram specification based GHP algorithm in algorithm 1. It should be noted that, for any gradient based image denoising model, we can easily incorporate the proposed GHP in it by simply modifying the gradient term and adding an extra histogram specification operation

IV. Reference gradient histogram algorithm To apply the model in Eq. (3), we need to know the reference histogram hr, which is supposed to be the gradient histogram of original image x. In this section, we propose a one dimensional deconvolution model to estimate the histogram hr. Assuming that all pixels in the gradient image ∇ x are independent and identically distributed (i.i.d.), we can view them as the samples of a scalar variable, denoted by x. Then the normalized histogram of ∇ x can be regarded as a discrete approximation of the probability density function (PDF) of x. For the additive white Gaussian noise (AWGN) v, we can readily model its elements as the samples of an i.i.d. variable, denoted by v. Since v ∼ N _0, _2_ and let g = ∇ v, one can obtain that g is also i.i.d. Gaussian with PDF

Then the PDF py is:

If we use the normalized histogram hx and hy to approximate px and py, we can rewrite Eq. (14) in the discrete domain as:

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where â&#x160;&#x2014; denotes the convolution operator. Note that hg can be obtained by discretizing pg, and hy can be computed directly from the noisy observation y. Obviously, the estimation of hx can be generally modelled as a deconvolution problem:

VI.

Fig.1:

Histogram of noiseless

Fig.4: denoise image

Results

Fig. 2: Histogram of noise image

Fig. 5: noised image

VII. Conclusion In this paper, we used a novel gradient histogram preserving (GHP) model for texture-enhanced image denoising (TEID). The GHP model can preserve the gradient distribution by pushing the gradient histogram of the denoised image toward the reference histogram, and thus is promising in enhancing the texture structure while re moving random noise. To implement the GHP model, we proposed an efficient iterative histogram specification algorithm V References [1]

H. C. Burger, C. J. Schuler, and S. Harmeling. Image denoising: can plain neural networks compete with bm3d? Proc. CVPR,

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[2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

2012. T. S. Cho, C. L. Zitnick, N. Joshi, S. B. Kang, R. Szeliski, and W. T. Freeman. Image restoration by matching gradient distributionsIEEE T-PAMI, 34(4):683–694, 2012. J. Jancsary, S. Nowozin, and C. Rother. Loss-specific training of non-parametric image restoration models: a state of the art. Proc. ECCV, 2012. W. Dong, L. Zhang, G. Shi, and X. Wu. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE T-IP, 20(7):1838–1857, 2011. H. Attouch, J. Bolte, P. Redont, and A. Soubeyran. Prox-imal alternating minimization and projection methods for nonconvex An approach based on the Kurdyka-Lojasiewicz inequality. Mathematics of Operations Re-search, 35(2):438–457, 2010 V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola. From Local kernel to nonlocal multiple-model image denoising. IJCV, 86(1):1–32, 2010. T. S. Cho, N. Joshi, C. L. Zitnick, S. B. Kang, R. Szeliski, and W. T. Freeman. A content-aware image prior. Proc. CVPR, 2010. M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries.. IEEE T-IP, 15(12):3736–3745, 2006 R. Fergus, B. Singh, A. Hertzmann, S. Roweis, and W. T. Freeman. Removing camera shake from a single photograph. Proc. ACM SIGGRAPH, 2006. W. Dong, L. Zhang, and G. Shi. Centralized sparse representation for image restoration. Proc. ICCV, 2011. I. Daubechies, M. Defriese, and C. DeMol. An iter-ative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math., 57(11):1413–1457, 2004 V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola. From Local kernel to nonlocal multiple-model image denoising. IJCV, 86(1):1–32, 2010.

About Authors Mr. Sainath G, received B.E Degree in Electronics and Communication from Pune University in 2012, Currently Pursuing M.Tech Degree in Digital Communication and Networking from Visvesvaraya Technological University in 2014, Department of Telecommunication Engineering, Dayanand Sagar College of Engineering, Bangalore, India. Main Interests in Networking, Digital Communications. Mrs Nagarathna received B.E Degree in Electronics and Communication from Visvesvaraya Technological University. She received M.Tech. Degree in Digital Communication and Networking from Visvesvaraya Technological University. She is currently working as Assistant Professor in telecom department of Dayanand Sagar College of Engineering.

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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 Testing Methodology to test Online Examination Application developed in SAP ABAP Shwetank Sharma #1, Ayushi Chhabra#2, Sanjay Ojha#3 School of Management, Centre for Development of Advanced Computing (CDAC), Noida, Uttar Pradesh, India __________________________________________________________________________________________ Abstract: Development of any application or system requires it to be tested for its proper functioning in all its areas. Online Examination Application is developed in SAP ABAP to conduct and manage the examination via an online environment. Testing this application is necessary to identify any bugs hampering its proper working. Manual testing is performed on Online Examination Application, to identify as many bugs as possible and produce the application that can be used to effectively conduct the exams. Keywords: LAN, SAP, ERP, OEA, ABAP, Testing, Manual Testing. __________________________________________________________________________________________ I. Introduction Online Examination Application has been developed as a system which conducts the exams online. This system is developed using SAP ABAP. SAP is a multinational software corporation based in Germany that makes enterprise software to manage business operations and customer relations. ABAP (Advanced Business Application Programming), a high level language created by SAP for building business applications. This project is developed to test the Online Examination Application. Testing a software system means identifying the defects in the software, that is hampering the proper working of the system. Testing can be conducted at various levels such as unit level, system level etc., according to the requirements of the project. Also, the different types of testing can be conducted such as functional or non-functional testing, confirmation or regression testing etc. This project aims at testing the Online Examination Application, so that the new users can successfully register for the exams and can give the exams online. Testing is necessary for this system as various checks are applied at different screens of the system. For instance, the registration page of the Online Examination Application consists of various fields to be filled in by the user. These include email address, first and last name, contact number etc. Testing ensures that there are not any special characters used in email address field, or the date entered in date of birth field is not invalid. Similarly, in Main Exam page, all the buttons such as instruction button, submit button, question number button, next and previous buttons should work properly. This is ensured with the help of testing. II. Theoritical Framework The Online Examination Application is developed using SAP ABAP. SAP system was introduced as an Enterprise resource planning (ERP) software designed to coordinate all the resources, information and activities to automate the business process. It stands for Systems Applications and Products and as a business software, it integrates all applications running in an organization. These applications represent various modules on the basis of which business areas are jointly executed to accomplish the overall business logic. The integration is done by SAP by creating centralized database for all applications running in an organization. [1] ABAP stands for Advanced Business Application Programming. It is a fourth generation language, first developed in 1980’s. It was originally used to develop the SAP R/3 system. Later, it was used to enhance SAP applications, by using it for creating customized reports and interfaces. [1] A. Candidate Module The candidate module is used to register the new candidates and for taking their exam. It is divided into two areas: 1) Registration page The registration page is used by the candidate to register him / her for the examination by providing all the necessary details as asked in the registration form. For successful registration, the following checks are applied on the registration form. [2]  There should be no special character in the email address field.  There should be no alpha numeric character in first name field of student.  There should be no alpha numeric character in last name field of student.  There should be no special character in the address of the student.

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 There should be no alpha numeric character in city field.  There should be only numeric characters in Contact Number & Pin code field.  The age of the student appearing for exam should not be less than 18 years and more than 50 years & it should be in between 1950 and 1995. 2) Main Exam Page The main exam page is the page where the candidate gives his/her exam. As soon as the candidate enters this page, the timer starts. The time duration for the exam is 20 minutes including the time for reading the instructions. Once the timer gets terminated then the candidate is navigated out of the test window [2]. Other features of this page are as follows:  On clicking the instructions button, the instructions are displayed for taking up the exam.  On clicking at the Start Exam button the candidate can start taking up the exam. Once the start button is clicked, it is disabled. The first question appears when Submit button is clicked.  There are navigation buttons, Next and Previous to navigate between the questions. However, there is no such provision where the candidate can switch to any of the unanswered question from the current question i.e. the non-highlighted question cannot be jumped to from any of the highlighted ones [2].  Every question has options for answers as radio buttons. When a radio button is clicked to answer the question, the response is submitted to the database and is saved there. If the users want to change the response then it can also be done. [2,4] B. Administrator Module The Administrator module is used to upload the questions papers for the exams and for displaying the result. 1) Uploading Questions Module This module is used to upload the questions. The upload question module asks for delimited text containing questions with answer which can be stored on the system or any removable drives. After selecting the file, a message will pop up confirming the upload of questions successfully. 2) Diplay Result Module The Display Result Module is used to display the result of the candidates who have appeared for the exam. Result can be displayed on screen or as a PDF file. III. Practical Application For conducting testing, a procedure was followed which consisted of preparing the test cases and then executing them on the actual system. The test cases are shown from table 1-15 along with their execution results from figures 1 to 15. The testing of the entire application is done for all four modules: Registration page, Main Exam page, Uploading Questions Page and Display Result Page. Table 1: Test Case 1

Figure 1: Registration Page All field Test.

Test Case ID: 1 Test Case Title: Registration page all fields test Description: It will check whether the system shows error or not if all the fields are not entered by the user while registration Test Execution Date:13 Nov 2013 Steps: 1. Click on the Registration Page Shortcut. 2. In the registration page, leave at least one field blank while fill all the other fields. 3. Execute or press F8. Expected Results: If even one field is left blank by the user then the system should show an error that all the fields are necessary to be filled

Figure 2: out put when All fields are not Filled.

Actual Result: It shows an error ‘You are required to fill all details’ Status(Pass/Fail): Pass Figure No: 1,2 ,3,4

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The registration page requires all the fields to be filled for a successful registration. However, when first time the testing was performed on registration page, the candidate was getting registered even all fields were not filled by him/her. The constraints were applied after first time testing.[3] So the following screenshots were taken: Figure 3: while registration, only field is Filled.

2. In the registration page, enter the month greater than 12 in the date of birth field. 3. Execute or press F8. Expected Results: The month in the date of field should be between 1-12 only. If it is greater than 12, then an error should be displayed that it is invalid. Actual Result: error is displayed as ‘Invalid date’ when month is entered greater than 12. Status(Pass/Fail): Pass Figure No: 5

Similar to checking the month entered in the date of birth field, dates and year checks were also done. As per the constraints applied, an error was shown if the date was not input between 1 to 31, including the upper and lower limits, and the year if not input between 1950 and 1995.[6,8] The email address field was also tested such that it does not contain any symbols or special characters. Table 3: Test Case 3

Figure 4: Database showing that the candidate is registered Even if only one field has been filled while registration.

Figure 5: Registration Page Month check test

Table 2: Test Case 2 Test Case ID: 2 Test Case Title: Registration page Month test Description: It will check whether the month entered by the user in the date of birth field of registration page is valid or not. Test Execution Date: 13 Nov 2013 Steps: 1. Click on the Registration Page Shortcut.

Test Case ID: 3 Test Case Title: Registration page special characters in email id test Description: It checks that no special characters, except for one ‘@’, are added in the email address field of the registration page. Test Execution Date: 13 Nov 2013 Steps: 1. Click on the Registration Page Shortcut. 2. In the registration page email address field, enter special characters like £, $, % etc. 3. Execute or press F8. Expected Results: If special characters other than one single @ are entered, then error should be displayed. Actual Result: The error is displayed : ‘Do no fill unwanted characters’. Status(Pass/Fai)l: Pass Figure No: 6,6

Figure 6: Email Address Field constraints check test

Figure 7: Output for Email address field constraints check test

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The first name or last name constraints were applied that they should not contain alphanumeric characters or any other special characters. Table 4: Test Case 4 Test Case ID: 4 Test Case Title: Registration page special characters in last name test Description: It checks that whether there are any special characters in the first or last name fields in the registration page. Test Execution Date: 13 Nov 2013 Steps:

Steps: 1. Click on the Exam Main Page Shortcut 2. The Main Exam page is displayed. Click on ‘Intsructions’ Button. Expected Results: When the Instructions Button is clicked, the Instructions should be displayed. Actual Result: The instructions are displayed when the user clicks the instruction button Status(Pass/Fail): Pass Figure No:10

Figure 10: Output of Main Exam page Instructions button

1. Click on the Registration Page Shortcut. 2. In the registration page first or last name field, enter special characters like £, $, % etc.. 3. Execute or press F8. Expected Results: If special characters are entered in the first name or last name fields, then error should be displayed. Actual Result: The error is displayed correctly. Status(Pass/Fail): Pass Figure No: 8,9

Figure 8: Last Name Constraint check test Table 6: Test case 6 Test Case ID: 6 Test Case Title: Main Exam page Start button test Description: To start the exam, the start button is clicked by the user. This test will check that when start button is clicked, whether first question is displayed or not and the start button is disabled or not. Test Execution Date: 14 Nov 2013 Steps:

Figure 9: Output of last name constraints check test.

1. 2.

Click on the Exam Main Page Shortcut. Click the ‘Start Exam’ button.

Expected Results: When the start Button is clicked, the system should display the first question and the start button should be disabled. Actual Result: The first question appears on the click of the start exam button, and the start button is disabled. Status(Pass/Fail): Pass Figure No:11

Testing the Main Exam page Testing the Main Exam page includes testing whether the Instruction button, Start button, Navigation Buttons such as previous or next and Question number buttons etc are working properly or not. Table 5: Test Case 5

Figure 11: Output of Main Exam page Start Exam button

Test Case ID: 5 Test Case Title: Main Exam page Instruction Button test Description: It will be checked that when the Instructions Button is clicked on the main exam page, the instructions are displayed or not. Test Execution Date: 14 Nov 2013

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Table 7: Test Case 7 Test Case ID: 7 Test Case Title: Radio Buttons working in Main Exam page test Description: It will check that whether clicking the radio button for selecting the answers will submit the user response or not. Test Execution Date: 14 Nov 2013 Steps: 1. Click on the Exam Main Page Shortcut. 2. Click on start exam. 3. Click a radio button. Expected Results: Once the radio button is clicked, it should submit the response to the database Actual Result: The response is submitted successfully Status(Pass/Fail): Pass Figure No: 12,13

Steps: 1. Click on the Upload Questions Shortcut. 2. Enter the name of the file which contains the questions. 3. Click open. Expected Results: The questions should be uploaded when the file name is given. Actual Result: The questions are uploaded according to the file name given. Status(Pass/Fail): Pass Figure No: 14,15,16

Figure 14: Uploading the file

Figure 12: Radio Buttons working in Main Exam page

Figure 15: Question Numbers uploaded Figure 13: Output showing responses submitted successfully to the database

Figure 16: particular question showing its choices and correct answer.

From Figure 12, it can be seen that there were more than one user at a particular point of time. The testing was done with about 15 users giving exams under ideal conditions and the exam was successfully accomplished. Hence the system at least could hold the load of 15 users at a time. [7] Testing the Uploading Questions page The Uploading Questions Module is used to upload the question papers for the exam. Table 8: Test Case 8 Test Case ID: 8 Test Case Title: Uploading questions test Description: It will check that the file containing the questions can be successfully uploaded or not. Test Execution Date: 14 Nov 2013

Testing the Disply Result Page The Display Result page is tested to show that the result of a particular candidate is displayed at a specified location.

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Table 9: Test Case 9

Figure 17: Result of specific user displayed successfully.

Test Case ID: 9 Test Case Title: Display Result test Description: It will check that whether the result is displayed on the specific location or not. Test Execution Date: 14 Nov 2013 Steps: 1. Click on Display Result Shortcut. 2. Provide the information such as registration id, location for result and exam date. Expected Results: The result should be displayed in the PDF format at the specified location. Actual Result: The result is displayed at the specified location. Status(Pass/Fail): Pass Figure No: 17

IV. Conclusion It can be concluded that testing plays a very important role in the life cycle of the project. Testing has helped to identify the various bugs in the software. Testing the Online Examination Application has removed almost all the possible bugs, and put the system into a steady state. V. Functioning Of OEA System is operated at a high level of efficiency and all the teachers and user associated with the system understands its advantage. The system solves the overhead associated manual type of examination. References [1] [2] [3] [4] [5] [6] [7] [8]

SAP ABAP/4 Coveres SAP ECC 6.Black Book, Kogent Learning Solutions Inc., Dreamtech Press, 2012. Kapil, Shwetank Sharma, Sanjay Ojha, “Online Examination Application using SAP ABAP”, unpublished. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1579150&queryText%3Dsoftware+testing http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=183282&pageNumber%3D2%26queryText%3Dsoftware+testing http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1579129&pageNumber%3D2%26queryText%3Dsoftware+testin g http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1546565&pageNumber%3D2%26queryText%3Dsoftware+testin g http://help.sap.com/saphelp_47x200/helpdata/en/49/c3d8a4a05b11d5b6ef006094192fe3/frameset.htm http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1579150&queryText%3Dsoftware+testing

Acknowledgments We would firstly like to thank Almighty, and our parents for believing in us and for their constant encouragement while carrying out this research. Their support was instrumental in the success of this research. We also take this opportunity to express our profound gratitude and deep regards to the Head Of Management of CDAC, Ms. Mary Jacintha, and all other faculty of School of Management, for their cordial support and guidance, which helped us in completing this task through various stages. The blessing, help and guidance given by them time to time shall carry us a long way in the journey of life on which we are about to embark.

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Comparative Review of Scheduling and Migration Approaches in Cloud Computing Environment MS. ALANKRITA AGGARWAL1, RAJJU2 Assistant Professor, 2 Research Scholar M.tech, Department of Computer Science & Engineering, HCTM Technical Campus, Kaithal, India. __________________________________________________________________________________________ Abstract: Cloud computing is one of most essential and popular distributed environment that resides all the services and product at some centralized location. The Task management is the key role in cloud computing systems task scheduling problems are main which relate to the efficiency of the whole cloud computing facilities. Scheduling in cloud means selection of best suitable resources for task execution. An effective scheduling that give the better resource utilization to the process so that user can get better services is also a challenge in cloud computing environment. Several methods have be given to solve the problem of effective scheduling in cloud computing. In this paper we present a study on different scheduling algorithms for effective resource utilization and migration. ______________________________________________________________________________________ 1

I. INTRODUCTION Cloud computing is an emerging technology that combines a large amount of computer resources in to a virtual place so as to provide an on-demand computing facility to users. The cloud system will provide its computing resources to users according to the user request, in which the amount and capacity of computer resources are highly configurable [5]. Definition of cloud computing provided by the US National institute of standards and technology (NIST). “Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, application, and services) that can be rapidly provisioned and released with minimal management effort or services provider interaction.” [12]. One of the key promises of cloud computing is an enormous level of flexibility for scaling up or scaling down software and hardware infrastructure without huge upfront investments. Hence, it is expected that any cloudbased infrastructure should have three characteristics: ability to acquire transactional resources on demand, resources publication through a single provider, and mechanisms to bill users on the basis of resource utilization [12]. It includes any subscription based or pay-per-use, where it can obtain network storage space and computer resources. Pay-as-you-go is the means of payment of cloud computing only paying for the actual consumption of resource [4]. Because of its elasticity, cloud computing is suitable for the execution of complicated computational task and scientific simulation, which may require a spike of computational resources, e.g., computing nodes and storages [5]. Cloud computing service models can generally be classified into three categories: Infrastructure as a service (Iaas), platform as a service (PaaS), Software as a Service (SaaS) [12]. Scalability is a prominent quality for all these categories of system. There are mainly three deployment models of cloud computing: private cloud, public cloud and hybrid cloud [9].Virtualization in cloud refer to multi-layer hardware platforms, operating systems, storage devices, network resources etc., the first prominent feature of virtualization is the ability to hide the technical complexity from users, so it can improve independence of cloud services. Secondly, physical resources can be efficiently configured and utilized, considering that multiple application are run on the same machine. Thirdly, quick recovery and fault tolerance are permitted [4].

Fig. 1: Service and Deployment model

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Task Scheduling is the most effective and important task for a computer system that basically decides the order of the process execution when different processes are kept in a queue. Scheduling is defined as a task that defines the execution of the system under the time constraint. Time is actually the order of the execution of the processes. Co-scheduling defines a process of execution of more than one process simultaneously on different processors. In a cloud based system, this scheduling approach is quite common. Task scheduling involves process of assigning of task to available resources on the basis of task characteristics and requirement. Task scheduling is a necessary process as it schedules task on the basis of the requirement of user. One requirement can be to minimize completion time to complete execution within deadline by reducing the monetary cost [2]. According to a simple classification, job scheduling algorithms in cloud computing can be categorized into two main groups; Batch mode heuristic scheduling algorithms (BMHA) and online mode heuristic algorithms. II. EXISTING SCHEDULING AND MIGRATION METHODS A Multiple-objective Workflow Scheduling Framework for Cloud Data Analytics: Orachum Udomkasemsub, Li Xiaorong, Tiranee Achalakul [5] proposed a workflow scheduling framework that can efficiently schedule series workflow with multiple objectives onto a cloud system. In this Artificial Bee Colony (ABC) is used to create an optimized scheduling plan. Conflicts among objectives can also be resolved by using Pareto-based technique. The proposed framework aims to deal with complex workflows for data analytics by handling various workflow structures, short term and long term scheduling, dependency mapping on a heterogeneous computing environment, and most importantly multiple objectives that may contradict one another. Experimental result show that this method is able to reduce 57% cost and 50% scheduling time within a similar makespan of HEFT/LOSS for a typical scientific workflow like Chimera-2. Deadline and Cost based Workflow Scheduling in Hybrid Cloud: Nitish Chopra, Sarbjeet Singh [2] defined a algorithm for cost optimization by deciding which resources should be taken on lease from public cloud to complete the workflow execution within deadline. The author develop a level based scheduling algorithm which executes task level wise and it uses the concept of sub-deadline which is helpful in finding best resources on public cloud for cost saving and also complete workflow execution within deadlines. In this workflow are represented by DAG. Defined work focuses on scheduling in hybrid cloud. In this deadline and monetary cost are considers as the main factor for scheduling tasks and resources in hybrid cloud environment. Author define the sub-deadline for each task which finds from the application deadline using a percentage method: Sub-deadline for the task = percentage of share of task in application * deadline of the application + deadline of task’s predecessor. Author compared it with the min-min algorithm it shows that makespan of proposed level based algorithm is almost double than min-min makespan but the cost incurred is around 3 times lesser then the cost incurred in min-min. Pre-emptive Scheduling of On-line Real Time Services With Task Migration for Cloud Computing: R. Santhosh, T. Ravichandran [4] defined a new scheduling approach to focus on providing a solution for online scheduling problem of real-time tasks using “Infrastructure as a Service” model offered by cloud computing. This scheduling method sensibly aborts the task when it misses its deadline. In this paper a preemptive online scheduling with task migration algorithm is proposed in order to minimize the response time and to improve the efficiency of the task. Whenever a task misses its deadline, it will be migrated the task to another virtual machine. This will improve the overall system performance and maximizes the total utility. Simulation result shoes that the proposed algorithm can significantly perform the EDF and Non Preemptive scheduling algorithm. An Efficient Multi Queue Job Scheduling for Cloud Computing: AV. Karthick, Dr. E. Ramaraj, R. Ganapathy Subramanian [1] : defined a multi queue scheduling algorithm to reduce the cost of both reservation and ondemand plans using the global scheduler. This methodology depicts the concept of clustering the jobs based on burst time. This method overcome the problem of fragmentation during scheduling and reduces the starvation with in the process. The proposed MQS method gives more important to select job dynamically in order to achieve the optimum cloud scheduling problem and hence it utilize the unused free space in an economic way. Simulation result show that MQS algorithm gives better and satisfactory result when compared to the traditional algorithm. An Energy and Deadline Aware Resource Provisioning Scheduling and Optimization Framework for Cloud Systems: Yue Gao, Yanzhi Wang, Sandeep K. Gupta, Massoud Pedram [3] defined the problem of global operation optimization in cloud computing from the perspective of the cloud service provider (CSP). The goal is to provide the CSP with a versatile scheduling and optimization framework that aims to simultaneously maximize energy efficiency and meet all user deadlines, which is also powerful enough to handle multi-user

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large scale workloads in large scale cloud platforms. Two types of workload models have been adopted in cloud computing systems: independent batch requests and task graphs with dependencies. In this paper we model the workloads from multiple users as a collection of disjoint task graphs. As for the cloud platform model, it is fully capable of reflecting server resource capacity and energy efficiency heterogeneities. Server communication bottlenecks are also taken into account. This fine-grained treatment of the hardware resources and user workloads provides opportunities for deadline-oriented application acceleration via parallel execution and global energy cost minimization, but also requires additional effort in admission control, resource provisioning, virtual machine placement and task scheduling. In this paper the author propose "Guided Migrate and Pack" (GMaP) as a unified scheduling and optimization framework for the CSP that addresses these issues in a holistic fashion. GMaP is also flexible in search space sizing and algorithm run time control. Experimental results show that when GMaP is deployed for the CSP, global energy consumption costs improves by over 23% when servicing 30 - 50 users, and over 16% when servicing 60 - 100 users. III. CONCLUSION With the emerging of cloud computing, cloud workflow systems are designed to facilitate the cloud infrastructure to support large scale distributed collaborative e-business and e-science applications. In this paper we have analyze various scheduling algorithm. Existing scheduling algorithm gives high throughput and cost effective but they do not consider reliability and availability. So we need algorithm that improves availability and reliability in cloud computing environment. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

[14] [15] [16] [17] [18]

A V. Karthick,” An Efficient Multi Queue Job Scheduling for Cloud Computing”, 2014 IEEE world congress on computing and communication technology. Nitish Chopra. “ Deadline and Cost based Workflow Scheduling in Hybrid Cloud” 2013 IEEE, 978-1-4673-6217-7/13. Yue Gao, “ An Energy and Deadline Aware Resource Provisioning, Scheduling and Optimization Framework for Cloud Systems” 2013 IEEE, 978-1-4799-1417-3/13. R. Santhosh,” Pre-emptive Scheduling of On-line Real Time services With Task Migration for Cloud Computing”, 2013 IEEE conference on PRIME. Orachum udomkasemsub, “ A Multiple- Objective Workflow Scheduling Framework for Cloud Data Analytics” JCSSE 2012, IEEE 978-1-4673-1921-8/12. Damien Borgetto," Energy-efficient and SLA-Aware Management of IaaS Clouds", e-Energy 2012, May 9-11 2012, Madrid, Spain. ACM 978-1-4503-1055-0/12/05. Balaji Viswanathan," Rapid Adjustment and Adoption to MIaaS Clouds", Middleware 2012 Industry Track, December 3-7, 2012, Montreal, Quebec, Canada. ACM 978-1-4503-1613-2/12/12. Shigeru Imai, Thomas Chestna, Carlos A. Varela, “Elastic Scalable Cloud Computing Using Application-Level Migration”, 2012 IEEE/ACM Fifth International Conference on Utility and Cloud computing. Kejiang Ye, Xiaohong Jiang, “VC-Migration: Live Migration of Virtual Clusters in the Cloud”, 2012 ACM/IEEE 13th International Conference on Grid Computing. Hadi Goudarzi, Mohammad Ghasemazar, and Massoud Pedram, “SLA-based Optimization of Power and Migration Cost in Cloud Computing”, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. Michael Menzel," CloudGenius: Decision Support for Web Server Cloud Migration", WWW 2012, April 16–20, 2012, Lyon, France. ACM 978-1-4503-1229-5/12/04. Muhammad Ali Babar," A Tale of Migration to Cloud Computing for Sharing Experiences and Observations", Waikiki, Honolulu, HI, USA. ACM 978-1-4503-0582-2/11/05. Sumit Kumar Bose," CloudSpider: Combining Replication with Scheduling for Optimizing Live Migration of Virtual Machines Across Wide Area Networks", 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-07695-4395-6/11© 2011 IEEE. Anton Beloglazov," Energy Efficient Resource Management in Virtualized Cloud Data Centers", 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing 978-0-7695-4039-9/10© 2010 IEEE. J. Brandt," Using Cloud Constructs and Predictive Analysis to Enable Pre-Failure Process Migration in HPC Systems", 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing 978-0-7695-4039-9/10© 2010 IEEE. Anton Beloglazov," Energy Efficient Allocation of Virtual Machines in Cloud Data Centers", 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing 978-0-7695-4039-9/10 © 2010 IEEE. Takahiro Hirofuchi," Enabling Instantaneous Relocation of Virtual Machines with a Lightweight VMM Extension", 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing 978-0-7695-4039-9/10© 2010 IEEE. Kento Sato," A Model-Based Algorithm for Optimizing I/O Intensive Applications in Clouds using VM-Based Migration", 9th IEEE/ACM International Symposium on Cluster Computing and the Grid 978-0-7695-3622-4/09© 2009 IEEE.

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

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net A Policy Driven Architecture for Effective Service Allocation in Cloud Environment 1

Mansi Goyal, 2Richa Chhabra 1 Student, 2Faculty ITM University, Gurgaon, Haryana, India _________________________________________________________________________________________ Abstract: A Cloud Environment provides the integration of multiple clients and server in a distributed environment. But in this environment the cloud servers are limited and there are number of cloud clients. To perform the effective cloud service allocation, some rule oriented model is required that can perform the analysis on the cloud server features as well as client characteristic analysis. In this work, a policy based architecture is shown that covers the cloud service allocation along with location identification and migration assistance. Keywords: Load Balancing, Cloud Scheduling, Request Scheduling ____________________________________________________________________________________ I. INTRODUCTION A cloud computing is distribution system that provides the integrated virtual environment. The presented work is defined as the integrated system that combines the cloud service, network system and the application software in an integrated environment. The cloud system is the shared system in which the resources and the services are shared in the effective service environment [1][2].

Figure 1: Basic Client Service Interconnection Model Here figure 1 is showing the basic integration model. As we can see, the client and server both are connected to the web based system in a generic integrated environment. In this environment, the service provider avail the services to the clients under the characterization analysis so that the distribution of the services to the client will be effective. In this environment, different kinds of cloud servers are available under the characteristics specification such as public availability, private restricted access and the limited secure access. As the user enter to the system, it basically connected to the intermediate layer where it get the information about all the available services along with cloud server specifications. But as the number of clients over the system increases, the challenges associated with the cloud system also increases. These challenges include the scheduling of the client requests, client service allocation, load balancing, security etc. To perform he effective cloud service allocation, there is the requirement of some effective mechanism that can perform the effective identification of the cloud and client characteristics. To handle these client requests, there is the requirement of some reliable and efficient service allocation is required. The cloud computing is one of the most effective architecture available over the web and mobile system to provide the sharing of services and the resources. It also improves the cloud system efficiency and the throughput. The distributed cloud system is capable to handle the multiple requests in an integrated environment along with independent resource specifications or the shared resources. These resources include the memory specification, storage area definition etc. The effectiveness of the cloud system can be

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achieved to gain the effective turnaround time, wait time etc. The cloud system is able to handle the multiple requests in the cloud environment as well as provide the integrated distributed cloud environment so that the processes present in the job queue will be processed effectively. This cloud system is having the different service allocation architecture to provide the effective distribution of the services to the clients. These allocation processes are also defined under the scheduling mechanism. In this section the exploration to the scheduling system is defined. A. Scheduling in Distributed Cloud When a distributed cloud system is generated, one of the challenges is to decide the order of client request processing. There are number of scheduling approaches that are either handled by the centralized cloud controller or some independent cloud system controller. These requests or jobs will be handled under the cloud system specification. The centralized controller will manage the allocation of these services in an effective way so that the effective generation of the cloud system will be performed. The objective of the scheduling processes is to manage the relation between the cloud system and the clients so that the resource allocation and the process execution will be done effectively. The distributed cloud system is also defined under the cost estimation so that the resource management in such system will be effective and adaptive. It will also explore the fault tolerance, scalability, reliability to the system. In this paper, the cloud environment exploration is been defined under the cloud service allocation process. In this section, the cloud system is defined with basic model specification. This section also defined the scheduling approach and service allocation system in cloud environment. In section II, the work defined by the earlier researchers is explored. In section III, the service allocation model is explained. In section IV, the conclusion derived from the work is discussed and presented. II. RESEARCH METHODOLOGY Lot of work is already done in the area of resource allocation and the process scheduling. Some of the earlier work done in same area is presented here. In year 2006, Vikki Tang has defined a work to reduce the instruction scheduling under the dynamic compilers. Author defined a scheduling approach under the feedback analysis so that effective allocation will be done. The presented framework is defined to benefit the instruction scheduling under multi threaded server applications [1]. In year 2013, Lichen Weng has defined a work on multithreaded Distributed Cloud system to perform the dynamic modelling. The paper describes the design under three steps. At first, author converts a scheduling policy to dynamic to evaluate the runtime of pattern mapping. The another step is to define the regression model to achieve the scheduling policy to identify the changing behavior of the threading system. The main objective of author was to define a scalable heuristic approach for estimating the growth of the system count[2]. Hsiang-Yun Cheng is defined as an analytical model to achieve the task scheduling under the analytical modelling. Author estimated the potential aspects under the memory and bandwidth analysis to restrict the number of task. Author implemented the scheduling under the real hardware [3]. In year 2013, Vishakha gupta has performed the performance analysis for the functionality analysis under asymmetric platforms. Author has performed the analysis under the heterogeneity under the utility and applicability analysis. Author has defined the work under the workload anlaysis and defined it under different processes and different configuration for the resource analysis [4]. Morris A. Jette defined the characteristics analysis under the scheduling process for multi programmed environments. Author defined a time and space slicing mechanism for the parallel programming and defined the concurrent job execution under single Distributed Cloud environment. Author has defined a performance analysis system under the utilization and responsiveness under different computing platforms[5]. Another work for the hetrogenous scheduling policies for real time multi Distributed Cloud system is considered for the multimedia mapping for design space. Author has defined a suitable scheduling policy so that system energy can be minimized. The presented framework includes the analysis on energy reduction approaches for dynamic power management [6]. Another work on power management for multi-core architecture for the process scheduling is defined for the process estimation under platform evaluation. Author defined the effectiveness and scalability of the system. Author highlighted the scalability limitations for the thread scheduling algorithm for small scale multi Distributed Cloud system. Author has defined the scheduling overhead without loss of accuracy [7]. In Year 2005, Rony Ghattas presented some approach to improve the functionality of the micro Distributed Cloud system under the energy and power constraints. This system was defined under low bit system and to enhance the system performance. The main advantage of the system is to reduce the cost and complexity of this new micro Distributed Cloud system along with the reduction of power consumption [8]. In Year 2003, Andrei Terechko defined the scheduling under the high level language with some variable definition with global values. Author defined the long range and large impact schedule for the compiler optimization for local values under the scheduling units. The paper has defined three main algorithms for assigning the values to different cluster under the multi pass scheduling approach under the variable definition. Author also defined the performance measures for optimizing the algorithm [9]. In Year 2004, Andrew Riffel also defined a multi pass partitioning problem with recursive denominator split along with heuristic algorithm so

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that the robustness over the approach will be achieved. This paper redefines the MPP as a scheduling problem and uses scheduling algorithms that allow incremental resource estimation and pass computation in effective time[10]. Another work on improvement over the energy efficiency was presented by Hiroshi Sasaki. The proposed method groups several instructions as a single issue unit and reduces the required number of ports and the size of the structure for dispatch, wakeup, select, and issue. The present paper describes the micro architecture mechanisms and shows evaluation results for energy savings and performance [11]. Flavius Gruian presented an addresses scheduling approach for reduced energy of hard real-time tasks with fixed priorities assigned in a rate monotonic or deadline monotonic manner. The approach Author describes can be exclusively implemented in the RTOS. It targets energy consumption reduction by using both on-line and off-line decisions, taken both at task level and at task-set level [12]. Martin Schoeberl performed the investigation on the overhead analysis on object oriented operations. Author also presented the work so that the overhead over the system will be reduced as well as the dispatch and field access will be done effectively. Author presented this work for a real time embedded system. The main objective presented by the author to reduce the hardware cost and to optimize the application output [13]. In Year 2000, Jared Stark presented work on instruction scheduling for pipelined processing. Author defined the work to improve the pipelined scheduling. Author has defined the technique to eliminate the ability to improve the execution of dependent instruction under the consecutive cycles. The presented approach by the author has defined the frequency check with the sacrifice of IPC [14]. III. SCHEDULING APPROACHES In the distributed cloud environment, the scheduling approach is having the importance to resolve the load balancing problem. To perform the distributed load balancing, the parallel queue handling on the the intermediate layer is performed. While performing work on distributed queuing, cooperative and noncooperative process analysis will be performed. The analysis of the queue elements will be performed under the different parameters. These parameters include the response time analysis, wait time analysis, resource availability, resource requirement etc. In the second scheduling mechanism, the processes input by the users is maintained in a single global queue and scheduling is performed on this global queue initially and later on the process allocation to different clouds will be performed. In the centralized cloud computing environment, the different considerations are taken while performing the scheduling. These considerations are shown in figure 1. Transfer Policy

Selection Policy

Scheduling Policies

Location Policy

Information Policy

Figure 1: Policies Under Scheduling Consideration A. Transfer Policy One of the most effective considerations of the scheduling scheme is the transfer policy. According to this policy, the job transfer can be performed from one cloud server to other. This approach is also called cloud migration policy. According to this policy, the cloud server analysis is performed under the client request. If the particular cloud server is not able to handle the request in such case, the cloud migration will be performed. To perform the migration, the transfer policy is used. According to this policy, the analysis of the cloud system is performed under the current acceptability of the client request on the server. If the server availability parameters are adapted to the request will be migrated. B. Selection Policy The selection policy is about the selection of the cloud server based on the user request parameters. If the user request parameters are adapted to the cloud server availability. This analysis will be performed on all the available cloud servers. The cloud server that is feasible to the user request parameters will be consider effective to the selection policy. The selection criteria is based on the scheduling algorithm such as the wait time analysis

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based will perform the cloud server allocation to the critical request first. The adaptive selection parameters are shown in figure 2. These parameters can be used individually or in group to take the effective scheduling decision so that the

Process Time Analysis

Wait Time Analysis

Resource Request Analysis Selection Parameters Resource Availability Analysis

Load Vector Analysis

Job Priority Analysis

Figure 2: Selection Policy Parameters C. Location Policy Location policy is about the decision of the process execution server. Some specialized user request requires the availability of some specialized servers such as database server. The location policy also depends on the specialized attributes such as the physical location of the physical location of the server, the language domain of the processing etc. Sometimes, the utilization of the server cannot be performed even if the server is available because of the location boundation specified by the client. Generally the location policy is either user specific or adaptive. Location Policy

User Specific

Feature Adaptive

Figure 3: Location Policy Parameters The user specific parameters includes the requirement specification in terms of server country specification, language specification etc. The feature adaptive specification is identified by the model itself based on the requirement and the availability analysis. The load balancing machismo is also the parameter for the location adaptive assignment. D. Information Policy The information policy is about the extraction of the information related to the cloud server as well as the relative environmental vector. This policy deals on two main ends i.e. client side and the server side adaptation as shown in figure 4. The client side adaption will capture the request related information such as dead line criticality evaluation, process sharing policy analysis etc. IV. CONCLUSION In this paper, an exploration to the cloud service scheduling mechanism is explored. The complete scheduling approach is defined under some policy specifications. These policies define the rules for the process generation and its execution on the cloud server. The paper has explored the policy parameters as well as its inclusion as the effective stage in the process execution mode.

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REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

Vikki Tang," A Framework for Reducing Instruction Scheduling Overhead in Dynamic Compilers". Lichen Weng," Scheduling Optimization in Multicore Multithreaded MicroDistributed Clouds through Dynamic Modeling", CF’13, May 14–16, 2013, Ischia, Italy. ACM 978-1-4503-2053-5 Hsiang-Yun Cheng," An Analytical Model to Exploit Memory Task Scheduling", INTERACT-14, March 13, 2010, Pittsburgh, PA, USA ACM 978-1-60558-921-3/10/03 Vishakha Gupta," Kinship: Efficient Resource Management for Performance and Functionally Asymmetric Platforms", CF’13, May 14–16, 2013, Ischia, Italy. ACM 978-1-4503-2053-5 Morris A. Jette," Performance Characteristics of Gang Scheduling in Multiprogrammed Environments", 1997 ACM 0-89791985-8/97/0011 Minyoung Kim," Design Space Exploration of Real-time Multi-media MPSoCs with Heterogeneous Scheduling Policies", CODES+ISSS’06, October 22–25, 2006, Seoul, Korea. ACM 1-59593-370-0/06/0010 Jonathan A. Winter," Scalable Thread Scheduling and Global Power Management for Heterogeneous Many-Core Architectures", PACT’10, September 11–15, 2010, Vienna, Austria. ACM 978-1-4503-0178-7/10/09 Rony Ghattas," Energy Management for Commodity Short-Bit-Width Microcontrollers", CASES’05, September 24–27, 2005, San Francisco, California, USA. ACM 1-59593-149-X/05/0009 Andrei Terechko," Cluster Assignment of Global Values for Clustered VLIW Distributed Clouds", CASES’03, Oct. 30 – Nov. 1, 2003, San Jose, California, USA. ACM 1-58113-676-5/03/0010 Andrew Riffel," Mio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling". Hiroshi Sasaki,"Energy-Efficient Dynamic Instruction Scheduling Logic through Instruction Grouping", ISLPED’06, October 4– 6, 2006, Tegernsee, Germany. ACM 1-59593-462-6/06/0010 Flavius Gruian," Hard Real-Time Scheduling for Low-Energy Using Stochastic Data and DVS Distributed Clouds", ISLPED’01, August 6-7, 2001, Huntington Beach, California, USA. ACM 1-58113-371-5/01/0008 Martin Schoeberl," Architecture for Object-Oriented Programming Languages", JTRES ’07 September 26-28, 2007 Vienna, Austria ACM 978-59593-813-8/07/09 Jared Stark," On Pipelining Dynamic Instruction Scheduling Logic", 0-7695-0924-X/2000© 2000 IEEE

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