INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021
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International Journal of Innovative Technology & Creative Engineering Vol.11 No.2 February 2021
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Dear Researcher, Greetings! Articles in this issue discusses about Cover Story: One day International conference on Computational Intelligence and Data Science for Sustainable Future, USE of VARIOUS INTEGRATED TECHNOLOGIES in PREVENTING FLOOD DISASTER, MIXED MODEL OF EXTREME LEARN MACHINE TREE AND RANDOM FOREST CLASSIFIER FOR PREDICTION OF ORAL CANCER. It has been an absolute pleasure to present you articles that you wish to read. We look forward many more new technologies in the next month. Thanks, Editorial Team IJITCE
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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA. Dr. S.Prasath Ph.D Assistant Professor, Department of Computer Science, Nandha Arts & Science College, Erode , Tamil Nadu, India Dr. P.Periyasamy, M.C.A.,M.Phil.,Ph.D. Associate Professor, Department of Computer Science and Applications, SRM Trichy Arts and Science College, SRM Nagar, Trichy - Chennai Highway, Near Samayapuram, Trichy - 621 105,
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Review Board Members Mr. Rajaram Venkataraman Chief Executive Officer, Vel Tech TBI || Convener, FICCI TN State Technology Panel || Founder, Navya Insights || President, SPIN Chennai Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE Professor, JSPM's Rajarshi Shahu College of Engineering, MCA Dept., Pune, India. Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India www.ijitce.co.uk
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021 Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168
DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE
Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America
Y. BenalYurtlu Assist. Prof. OndokuzMayis University
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021 Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India www.ijitce.co.uk
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021 RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579
Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 558123042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677
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Contents Cover Story: One day International conference on Computational Intelligence and Data Science for Sustainable Future
……………. [ 943 ]
USE of VARIOUS INTEGRATED TECHNOLOGIES in PREVENTING FLOOD DISASTER ……………. [ 944 ] MIXED MODEL OF EXTREME LEARN MACHINE TREE AND RANDOM FOREST CLASSIFIER FOR PREDICTION OF ORAL CANCER …………………………………. [ 948]
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Cover Story: One day International conference on Computational Intelligence and Data Science for Sustainable Future 26th FEBRUARY 2021
Dr.P.Periyasamy, Associate Prof, Department of CS&CA, SRM Trichy Arts and Science College, About the College SRM Trichy Arts and Science College, affiliated to Bharathidasan University, Tiruchirappalli was established in the academic year 2018-2019. The college is in continuous pursuit of educational excellence by providing a holistic learning envrironment for students with state of the art infrastructure & facilities. It has eight undergraduate programmes.
• Data Mining, Data Sciences • Big Data Analytics, Block Chain Technology • Image Processing, Deep learning • Machine Learning, Cloud Computing • Robotic Systems, Wireless, Mobile and Communication Technologies • Social Networks, Soft Computing • Web Mining, Biomedical and Medical Imaging • Cyber Security& Ethical Hacking • Green Computing, Video Streaming • 3D Printing • Virtual Reality • E-Learning • Any other relevant topics Important Dates
About the Department The Department of Computer Science and Applications was established in the year 2018 with the objective of imparting quality education in the field of Computer Science & Applictions. The goal of the department is to provide future IT Professionals with core competencies in computer science. The Department has modern facilities for teaching, learning and research. The Department assists students in developing a strong foundation in Computer Science by providing analytical, computational and problem-solving skills. The competitive industrial and social demands are well recognized by the committed team and its incessant effort is to bring the best out of every budding professional in the department by nurturing them on inter-personal skills, team building, leadership qualities and entrepreneurship skills.
Last date for Abstract : 18.02.2021 Full paper submission : 22.02.2021 Chief Guest : Dr.M.Senthil Kumar Professor University of Technology and Applied Sciences Ibra Sultanate of Oman Guest of Honour : Shri.R.Venkateswaran Head/HR M/S Tata Consultancy Services Chennai Felicitation : by Dr. S. Raghupathy, MBA, FRM., CQF., Ph.D., Executive Director, SRM Trichy Campus Dr. N. Balasubramanian, M.D, DD., Deputy Director, SRM Trichy Campus
About the Conference The aim of the Conference is to provide a platform to the academia, researchers and practitioners from both as well as industry to share the share cutting-edge developments in the field. This Conference invites academicians, researchers and engineers to initiate an outstanding research forum for sharing, exchanging and exploring new avenues of Computer Applications and related research and latest developments. All submitted papers will be under peer review and accepted papers will be published in the conference proceedings.
Papers Invited
Chief Patron
:
Co Patrons
:
Conference Chair
:
Dr.C.K.Kotravel Bharathi Principal
Organising Secretaries
:
Dr.G.Ilango Vice-Principal (Academic) Dr.S.Lawrence Leve Vice-Principal (Admin)
Conference Convener:
Research papers are invited on the vital and contemporary aspects of computational and information technologies. We encourage all types of high-quality contributions including theoretical, engineering and applied research papers. Authors are invited to submit original technical papers covering the topics of interest listed below: • Artificial Intelligence • Internet of Things (IoT)
Organizing Members CS&CA CS&CA
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Dr.R.Shivakumar Chairman Dr.S.Raghupathy Executive Director Dr.N.Balasubramanian Deputy Director
Mrs.S.Padmapriya Head, Department of CS & CA :
Dr.P.Thangavel Asst. Prof., Department of Dr.M.Chitra Devi Asst. Prof., Department of Dr.P.Periyasamy Associate Prof., Department of
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USE of VARIOUS INTEGRATED TECHNOLOGIES in PREVENTING FLOOD DISASTER J. Michael Antony Sylvia1, Dr.M.Pushpa Rani2 Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, India. 1 minusylvia@yahoo.co.in Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, India. 2 drpushpa.mtwu@gmail.com 800,000 human lives and 300 billion USD economic
INTERODUCTION Abstract - Monitoring and analyzing the environmental
losses. Extreme effect of meteorological events
parameters accurately predict the occurrence of flood
significantly reflects in the frequency and intensity of
disaster. Appropriate disaster warning in turn would
flood happenings [9]. To mitigate the occurrence of
support
take
causalities and environmental / infrastructure damage
precautionary measures. Traditional technologies step
and to pre-plan supportive measures these disasters
the
concerned
authorities
to
behind in efficient disaster management process. Hence
need
arouse
in
further
technological
development to accurately predict and warn people about the disaster occurrence. Recently developed disaster
management
systems
with
integrated
necessitates anticipatory process. Effective monitoring, early and accurate prediction and timely warning of flood status are essential for flood
disaster
management.
Internet
based
technologies such as IoT, Machine Learning, Deep
technology caters to this purpose. IoT is a widespread
Learning, Crowd Sourcing and Artificial Intelligence
technology which has embedded devices to gather
have
disaster
data from sensors and predict flood hazards. Other
management with few limitations. This paper surveys
than IoT use of analytics has also served the need in
the various existing integrated technologies used in
flood prediction and management. Due to rapid
enhanced
the
entire
process
of
disaster management process.
growth in internet and connectivity, information can
Keywords: Internet of Things, Machine Learning, Deep Learning, Crowd Sourcing, Artificial Intelligence, Disaster Warning, Disaster Management.
easily be shared among people. Implementation of recent methods integrating IoT highly benefits the performance of
disaster
management process.
Recent research works on related studies is reviewed in this paper.
I. INTRODUCTION Disasters mostly happen in areas of human livelihood. Either natural or man-made, mishaps have moved out of control of human resistive mechanisms. Over the past decade, flood the huge deadly natural disaster has become highly responsible for more than
II. LITERATURE SURVEY Abdullahi et. al in [7] developed a flood warning system. Sensor data was stored on ThingSpeak Cloud through NodeMCU ESP8266 for real time visualisation. Flood status was predicted using a two
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021 class Neural Network model of Microsoft Azure based
In data analysis module, flood data from the cloud
on a predefined rule. Prediction results showed 98.9%
is being preprocessed in order to eliminate the
of highest accuracy and 100% of precision with three
redundant features and extract the necessary ones to
hidden layers.
enhance further learning process at a minimum time.
Suresh et. al in [1] developed a flood prediction
Required features from the flood data are then
system where DNN was employed to predict the flood
recognized and extracted for further process in the
occurrence based on temperature and rainfall
next stages of feature extraction and selection.
intensity. Comparing with other ML classification algorithms such as SVM, Naive Bayes and KNN the proposed method DNN with multiple hidden layers outperformed with better accuracy of 89.71%.
Machine learning techniques are applied on the selected features to predict flood occurrence. In prediction of flood occurrence an immediate warning is sent through a mobile app.
M. Anbarasan et. al, in [2] designed a system for detecting flood disaster using IoT, big data and CDNN. Input is taken from big data and repeated data are reduced using HDFS map reduce() function. Data is preprocessed and a rule is generated. CDNN classifies the rule that is given as its input as chances of flood occurrence and no chances of flood occurrence. Comparative analysis shows better accuracy of 93.23%. In [4] Viet-Nghia Nguyen et. al, suggested a new modelling approach for spatial prediction in flash
Fig. 1. Stages of Flood Prediction
flood. Dataset from flood inventory map and geospatial database was trained and verified using
B. Flood Prediction using 2 Class NN
CHAID-RS-BBO model. Predictive analysis was done
Abdullahi et. al [7] uses Microsoft’s Azure Machine
with statistical metrics and the results show a highest
Learning with an in built 2-class neural network to
predictive performance with overall accuracy of 90%.
predict flood status according to a predefined rule. Real time monitored data is been gathered from two
III. FLOOD
DISASTER
MANAGEMENT
TECHNIQUES
sensors: water level sensor and pressure gauge sensor. Data collected is then stored in a ThingSpeak cloud platform for further process using Node MCU
A. Flood Prediction and Analysis
ESP8266. Pressure gauge meter proves the strong
Flood occurrence can be predicted using various
correlation between water flow rate and its pressure.
methods. Fig1 depicts the different phases involved in
Flood status of the new inputs is predicted through
flood prediction. Initial stage is to collect the sensor
Web service provided. Analysis shows highest
monitored
accuracy of prediction in using 3 hidden layers.
environmental
parameters
through
Raspberry pi microcontroller. Collected data is then transmitted and stored in cloud using Wifi module.
C. Flood Prediction and Forecasting using DNN Suresh et. al in [2] utilized DNN for flood prediction and forecasting. Dataset consisted of observed
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021 rainfall and minimum and maximum temperature.
TABLE I
Dataset is preprocessed and split as training and
COMPARISON OF INTEGRATED TECHNOLOGIES FOR FLOOD DISASTER MANAGEMENT
testing sets. The system is then defined and trained. DNN with multiple hidden layers is used to classify the dataset. If a high bias is seen then the model is not suitable and has to be redefined. Until lower bias is done the process is redone. Once lower bias is seen the model is tested and deployed.
Purpose Flood
Flood
Flood
gathered data from big data. Redundant data were
Flood
Implementing
missing
map value
reduce()
function.
imputation
Dataset
Accuracy
Sensor Data
98.9%
Temperature
Detecting
classifier to detect flood disaster. This system HDFS
Azure ML
DNN
and
Forecasting
M. Anbarasan et. al, in [1] employed CDNN
using
used
Prediction
Prediction and
D. Detecting Flood Disaster using CDNN
reduced
Technology
Level
Classification
89.71%.
Rainfall
Intensity
CDNN
Big data
93.23%.
Gait analysis
Sensor data
99.45%
and
normalization function the data was then preprocessed. On application of combination of attributes
Chart Title
method a rule was generated based on the preprocessed data. The rule generated was given as input to CDNN classifier to classify it as chances of
Azure ML
flood occurrence and no chances of flood occurrence.
DNN
Analysis show highest prediction accuracy.
CDNN
Gait Analysis
E. Flood Level Classification using Gait Analysis The gait characteristics in different flood levels are captured using smart phone sensors, which are then used to classify flooding levels. In order to accomplish this smart phone sensor data, reading have been
FIG. 2. GRAPHICAL REPRESENTATION OF ACCURACY
taken by 12 volunteers in pools of different depths, and have been used to train machine learning models
IV. CONCLUSION
in a supervised manner. Support vector machines,
This paper highlighted the review on various
random forests and naive bayes models have been
integrated technologies proposed for flood prediction
attempted, of which support vector machines perform
and forecasting in different literatures. Most of these
best with classification accuracy. The most relevant
innovations in the research progress is still in the
features
starting time and there is a huge path to penetrate
of
classification
match
the
intuitive
understanding of gait in different flooding levels.
through. Research works can be extended with intelligent based techniques for effective flood prediction and forecasting with minimum time and accuracy.
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021
1.
REFERENCES Suresh Sankaranarayanan, Malavika Prabhakar, Sreesta Satish, Prerna Jain, Anjali Ramprasad and Aiswarya Krishnan, “Flood Prediction Based on Weather Parameters Using Deep Learning”, Journal of Water and Climate Change, 2019.
2.
M. Anbarasan, Bala Anand Muthu, C.B. Sivaparthipan, Revathi Sundarasekar, Seifedine Kadry, Sujatha Krishnamoorthy, Dinesh Jackson Samuel R., A. Antony Dasel “Detection of Flood Disaster System based on IoT, Big Data and Convolutional Deep Neural Network”, Computer Communications, Elsevier, Pp. 150-157, November 2019.
3.
Ujjawal K.Panchal, Hardik Ajmani and Saad Y. Sait “Flooding Level Classification by Gait Analysis of Smartphone Sensor Data”, IEEE Access, Vol 7, Pp. 181678-181687, 2019.
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021
MIXED MODEL OF EXTREME LEARN MACHINE TREE AND RANDOM FOREST CLASSIFIER FOR PREDICTION OF ORAL CANCER Dr. M Natarajan
Assistant Professor, Department of Computer Science, Thanthai Hans Roever College, Perambalur, TamilNadu, India. E-mail ID: prof.mnrajan@gmail.com Dr. A. Muruganandam
Principal, Department of Computer Science, Sri Aravindar Arts and Science College, Vanur, Villupuram, TamilNadu, India. E-mail ID: murugandbc1976@gmail.com
Abstract- Oral Cancer is one of the deadliest
Keywords:
Extreme
Learning
Machine
diseases and most of the human are infected by
(ELM), Random Forest (RF) Classification,
this crucial disease in several parts of the world.
Support Vector Machine (SVM) Classification,
It may occur in any part of the oral cavity. The
Firefly Algorithm (FA), Genetic Algorithm (GA).
early detection and prevention of oral cancel is very critical issue but it can improve the survival
1. INTRODUCTION
chances considerably, allow for simple treatment and provided the better quality of life for
The Oral Cancer is also referred as
survivors. In existing system, the genetic
mouth cancer. The mouthy cancers are initially
algorithm is used for feature selection and the
started as lump, bump or patch in the mouth.
Support Vector Machine classifier algorithm is
Sometimes that does not go away after the few
used for classification to predict the oral cancer.
weeks are automatically happened either by you,
The feature selection and the classification is
your dentist or another doctor [1]. The most
performed separately so the time complexity of
mouth cancers are squamous cell carcinomas
the accuracy and prediction time quite complex
(cancer cells come from the cells lining all parts
So to solve this issue in proposed system the
of the inside of the mouth), but salivary gland
firefly algorithm is used for the feature selection
cancers and other types of cancers can arise in
and for the classification, mixed model of
the mouth as well.
Extreme Learn Machine (ELM) Random forest classifier technique is used to improve the
1.1 Pre-cancerous Oral Lesions
classification accuracy. The proposed system is tested with normal clinical data set which is
There are also a few common pre-
improved the classification accuracy and the
malignant lesions of which you should be aware.
prediction time compared to existing system.
❖ Leukoplakia ❖ Erythroplakia
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021
❖ Dysplasia
from the different institutes and additional clinical
❖ Lichen planus
variables
are
required
for
further
clinical
application of this recent approach. In [4] 1.2 Navigating Oral Cancers
presented
to
assess
In order to known about the several types of oral
awareness of final year dental undergraduates of
cancer, bellow mentioned an overview of the
medical universities and institutes Ukraine
basics of oral cancer.
concerning
oral
cancer
the
and
comprehensive
precancerous
❖ Buccal Cancer
lesions. In [5] presented the effect on survival of
❖ Lip Cancer
elective node dissection to improve the early
❖ Oral Salivary Gland Cancer
detection of oral cancer. In the prospective,
❖ Tonguage Cancer
randomized, controlled trial is evaluated the survival of elective node dissection between
In this paper, we will predict the oracle cancer by
therapeutic node dissection in patients with
using data Mining Techniques for improving the
lateralized T1 and T2 oral squamous cell
early detection of disease. The proposed Mixed
carcinomas. The primary and secondary analysis
Model of Extreme Learn Machine Random
is used to improve overall survival and disease-
Forest Classifier (MMELMRFC) to detect the oral
free survival respectively.
cancer. The proposed approach is increased the classification accuracy of detecting the oral cancer
3. METHODLOGY
3.1 Data- mining techniques in oral cancer 2. RELATED WORK
prediction
This section describes the previous work
Oral cancer prediction is certainly very
of various researchers in oral cancer using
complex
different
[2]
Estimating the probability of cancer occurrences
presented new approach for detecting cancer
in patients requires that many factors (both
and prevention by association rule mining. It is
genetic and non-genetic) are evaluated and
used to extract the association among several
properly weighted according to their significance
valuable data pertaining to clinical symptoms and
and/or other (contact sensitive) contribution
history of the cancer patients. In [3] Presented to
factors [7]. Some of the approaches in this
analyze the salivary metabolites and identify the
search include:
Data
Mining
Techniques.
In
and
non
deterministic
endeavor.
metabolic profiles specific to oral, breast and pancreatic. In this analysis is taken larger number of patient samples, particularly the data
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021
Support Vector Machine (SVM)
comparatively slower than other algorithms. It
The SVM is the supervised machine learning algorithm which is used for both classification and regression challenges. It mostly used to solve classification problems. In this algorithm, plot each data item as a point in ndimensional space value being the value of a
can effectively estimate missing values and hence is suitable for handling datasets with large number of missing values. 3.2
Data
techniques
in
prediction of oral cancer
particular coordinate. The SVM is used to simply co-ordinates the individual observation.
classification
The data mining classification techniques contained various methods. The different method is utilized for different purpose, each method has
Extreme Learning Machine (ELM) The ELM is increased the accuracy of classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multi layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need not be tuned [6]. These hidden nodes are randomly allocated and never updated or inherited from ancestors without being changed. In most cases, the weights of hidden nodes are usually learned in a single step, which essentially amounts to learning a linear model.
its own advantages and disadvantages. In the data mining classification is one of the most important tasks. It is used to maps the data in to predefine targets. It is a supervised learning as targets
for
predefined.
The
aim
of
the
classification is to build the classifier based on some cases with attributes to present the objects or one attribute to describe the group of the objects. The classifier is used to predict the group attributes of new cases from the domain-based values of other attributes. The most used classification algorithms are exploited in the microarray analysis is to belong four categories: IFTHEN Rule, Decision tree, Bayesian classifiers
Random Forest Classifier
and neural networks. Random forest is an ensemble classifier which consists of many decision trees and gives class as outputs i.e., the class’s output by
IF-THEN Rule: Rule
induction:
is
the
process
of
individual trees. Random forest is given many
extracting useful ‘if then’ rules from data based
numbers of classification trees without pruning.
on statistical significance. A Rule based system
Each classification tree is offered a specified
constructs a set of if-them-rules. Knowledge
number of votes for each class. Among all the
represents has the form.
trees, the algorithm chooses the classification with the greatest number of votes. Random forest runs
efficiently
on
large
datasets
but
is
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021
IF conditions THEN conclusion:
Bayesian classifiers and Native Bayesian
This type of rule is contained two phases.
From
a
Bayesian
viewpoint,
a
The rule antecedent (the IF part) is contained one
classification problem can be written as the
or more conditions about value of predictor
problem of finding the class with maximum
attributes where as the rule consequent (THEN
probability given a set of observed attribute
part) is contained a prediction about the value of
values. Such probability is seen as the posterior
a goal attribute. An accurate prediction of the
probability of the class given the data, and is
value of goal attribute will improve decision-
usually computed using the Bayes theorem,
making process. IF-THEN prediction rules are
estimating this probability distribution from a
very popular in data mining; they represent
training dataset is a difficult problem, because it
discovered knowledge at a high level of
may require a very large dataset to significantly
abstraction. Rule Induction Method has the
explore
potential to use retrieved cases for predictions.
Conversely,
Native Bayesian
is a simple
probabilistic
classifier
on
Decision Tree
theorem
all
the
with
possible
the
based (native)
combinations. Bayesian
independence
Decision tree derives from the simple
assumption. Based on that rule, using the joint
divide-and conquer algorithm. In these tree
probabilities of sample observation. Despite its
structures,
and
simplicity, the Native Bayes classifier is known to
branches represent conjunctions of features that
be a robust method, which shows on average
lead to those classes. At each node of the tree,
good performance in terms of classification
the attribute that most effectively splits samples
accuracy,
into different classes is chosen. To predict the
assumption does not hold.
leaves
represent
classes
also
when
the
independence
class label of an input, a path to a leaf from the root is found depending on the value of the predicate at each node that is visited. The most common algorithms of the decision trees are ID3
Artificial Neural Networks (ANN) An
artificial
neural
network
is
a
and C4.5. An evolution of decision tree exploited
mathematical model based on biological neural
for microarray data analysis is the random forest,
networks. It consists of an interconnected group
which uses an ensemble of classification trees.
of artificial neurons and processes information
Showed that the good performance of random
using a connectionist approach to computation.
forest for noisy and multi-class microarray data.
Neurons are organized into layers. The input layer consists simply of the original data, while the output layer nodes represent the classes. Then, there may be several hidden layers. A key feature of neural networks is an iterative learning
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021
process in which data samples are presented to Oral Cancer Training Data
the network one at a time, and the weights are adjusted in order to predict the correct class label. Advantages of neural networks include their high tolerance to noisy data, as well as their
Data Pre-processing
ability to classify patterns on which they have not been trained. In a review of advantages and disadvantages of neural networks in the context of microarray analysis is presented. 3.3 Proposed Architecture In this paper, the firefly algorithm is used
Feature Selection using Firefly Algorithm
Evaluate fitness value for firefly and select optimal features to train
Mixed model of ELM tree and Random forest Classifier
for feature selection for predicting the oral cancer. The firefly algorithm is the swarm intelligence-based
mete
heuristic
technique
which is inspired by the flashing behavior of fireflies. Each firefly is represented the set of attributes of the oral cancer. Initial population of
Classification
Testing Samples
fireflies is generating the operation of the prediction. The problem of the complexity
Classifier Evaluation
accuracy and the execution time overcome by the firefly algorithm. Algorithm:
Fig 1: Architecture for prediction of oral cancer
Step 1: Initialize the populations of fireflies (Threshold values) are initialized.
IV. RESULT AND DISCUSSION
Step 2: The intensity of the fireflies is calculated. The performance of proposed approach
Step 3: The attractiveness function of the firefly
is a Mixed Model of Extreme Learn Machine
is
(ELM)
determined. Step 4: The estimation of the distance (Update) between the two fireflies is measured. Step 5: The movement of firefly is constructed.
tree
and
Random
forest
classifier
(MMELMRFC) for prediction of oral approach is evaluated in terms of Accuracy, Precision, Recall,
F-Measure
and
Specificity.
The
experimental result shows that the preposed MMELMRFC approach is achieved better result than
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Existing
Genetic
Algorithm
Feature
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Selection
based
Support
Vector
Machine
Recall:
Classifier approach (GAFSSVMC). To evaluate the more effectiveness of the proposed method, the evaluation metrics such as Accuracy, Precision, Recall, F-Measure and
Recall value is evaluated according to the feature classification at true positive prediction and false negative.It is computed as follows:
Specificity are used, which is calculated using the following formulas.
True positive Recall = (True positive + False positive)
Accuracy: The accuracy is defined as the proportion of true results among the total number of cases
F-Measures:
examined. Accuracy can be calculated using this F-measure is calculated from the
formula:
precision and Recall. It is calculated as follows:
TP + TN Accuracy =
Precision x recall
TP + TN + FP + FN
F-Measure = 2 x Precision + recall Specificity:
Precision: Precision value is evaluated according to
Specificity is refer to the test ability to
positive
correctly detect patient without a condition.
prediction and false positive. It is calculated as
Specificity of a test is the proportion of healthy
follows:
patients known not to have the diisease, who will
the
feature
classification
at
true
test negative for it. Mathematically, this can be True positive
written as:
Precision =
No. of True Negatives True positive + False positive
Specificity = No. of True Negatives + No. of False Positives
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Table 1: Comparisons of Performance Result
V. CONCULUSION The main goal of this research is to find
Metrics
Genetic
Mixed model
Algorithm
of Extreme
Feature
Learn
Selection based
Machine
Support Vector
(ELM) tree
Machine
and Random
Classifier
Forest
approach
Classifier
(GAFSSVMC)
(HELMRFC)
out the cancer based on firefly algorithm which is important task because of the disease complex in nature. The performance of any algorithm relies upon the parameters used for the process. So, each process level the accuracy can be strengthened. Here performance is improved 11 percentage compare to existing. The prediction of the oral cancer using enhanced different techniques changes from time to time because of technological
Accuracy
85
95
Precision
82
94
Recall
80
90
advancement.
Initially,
the
classification method is enhanced by focusing on the heterogeneous data, feature selection based on Mixed Model of Extreme Learning Machine and Random Forest Classifier. In these methods the firefly optimization algorithm is identified the
FMeasure Specificity
82
93
83
95
more reliable features from oral cancer data and images. Ultimately these features are used in Mixed Model of Extreme Learning Machine and Random Forest Classifier to classify the oral cancer data, in order to predict the oral cancer from oral images.
Fig 2: Comparisons of Performance Result
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.2 FEBRUARY 2021 performance in operation-centric oral cancer patients using
VI. REFERENCES
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