Advances in Intelligent Systems and Computing

Volume 1108

Series Editor

Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences,Warsaw, Poland

Advisory Editors

Nikhil R. Pal, Indian Statistical Institute, Kolkata, IndiaRafael Bello Perez, Faculty of Mathematics, Physics and Computing,Universidad Central de Las Villas, Santa Clara, CubaEmilio S. Corchado, University of Salamanca, Salamanca, SpainHani Hagras, School of Computer Science and Electronic Engineering,University of Essex, Colchester, UKLászló T. Kóczy, Department of Automation, Széchenyi István University,Gyor, HungaryVladik Kreinovich, Department of Computer Science, University of Texasat El Paso, El Paso, TX, USAChin-Teng Lin, Department of Electrical Engineering, National ChiaoTung University, Hsinchu, TaiwanJie Lu, Faculty of Engineering and Information Technology,University of Technology Sydney, Sydney, NSW, AustraliaPatricia Melin, Graduate Program of Computer Science, Tijuana Instituteof Technology, Tijuana, MexicoNadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro,Rio de Janeiro, BrazilNgoc Thanh Nguyen , Faculty of Computer Science and Management,Wrocław University of Technology, Wrocław, PolandJun Wang, Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publicationson theory, applications, and design methods of Intelligent Systems and IntelligentComputing. Virtually all disciplines such as engineering, natural sciences, computerand information science, ICT, economics, business, e-commerce, environment,healthcare, life science are covered. The list of topics spans all the areas of modernintelligent systems and computing such as: computational intelligence, soft comput-ing including neural networks, fuzzy systems, evolutionary computing and the fusionof these paradigms, social intelligence, ambient intelligence, computational neuro-science, artificial life, virtual worlds and society, cognitive science and systems,Perception and Vision, DNA and immune based systems, self-organizing andadaptive systems, e-Learning and teaching, human-centered and human-centriccomputing, recommender systems, intelligent control, robotics and mechatronicsincluding human-machine teaming, knowledge-based paradigms, learning para-digms, machine ethics, intelligent data analysis, knowledge management, intelligentagents, intelligent decision making and support, intelligent network security, trustmanagement, interactive entertainment, Web intelligence and multimedia.

The publications within “Advances in Intelligent Systems and Computing” areprimarily proceedings of important conferences, symposia and congresses. Theycover significant recent developments in the field, both of a foundational andapplicable character. An important characteristic feature of the series is the shortpublication time and world-wide distribution. This permits a rapid and broaddissemination of research results.

** Indexing: The books of this series are submitted to ISI Proceedings,EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/11156

S. Smys • João Manuel R. S. Tavares •

Valentina Emilia Balas • Abdullah M. IliyasuEditors

Computational Visionand Bio-Inspired ComputingICCVBIC 2019

123

EditorsS. SmysDepartment of CSERVS Technical CampusCoimbatore, India

João Manuel R. S. TavaresFaculty of EngineeringFaculdade de Engenhariada Universidade do PortoPorto, Portugal

Valentina Emilia BalasFaculty of EngineeringAurel Vlaicu University of AradArad, Romania

Abdullah M. IliyasuSchool of ComputingTokyo Institute of TechnologyTokyo, Japan

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-3-030-37217-0 ISBN 978-3-030-37218-7 (eBook)https://doi.org/10.1007/978-3-030-37218-7

© Springer Nature Switzerland AG 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

We are honored to dedicate the proceedingsof ICCVBIC 2019 to all the participants andeditors of ICCVBIC 2019.

Foreword

It is with deep satisfaction that I write this foreword to the proceedings of theICCVBIC 2019 held in RVS Technical Campus, Coimbatore, Tamil Nadu, onSeptember 25–26, 2019.

This conference was bringing together researchers, academics, and professionalsfrom all over the world, experts in computational vision and bio-inspiredcomputing.

This conference particularly encouraged the interaction of research students anddeveloping academics with the more established academic community in aninformal setting to present and to discuss new and current work. The papers con-tributed the most recent scientific knowledge known in the field of computationalvision, soft computing, fuzzy, image processing, and bio-inspired computing. Theircontributions helped to make the conference as outstanding as it has been. The localorganizing committee members and their helpers put much effort into ensuring thesuccess of the day-to-day operation of the meeting.

We hope that this program will further stimulate research in computationalvision, soft computing, fuzzy, image processing, and bio-inspired computing andprovide practitioners with better techniques, algorithms, and tools for deployment.We feel honored and privileged to serve the best recent developments to youthrough this exciting program.

We thank all authors and participants for their contributions.

S. SmysConference Chair

vii

Preface

This conference proceedings volume contains the written versions of most of thecontributions presented during the conference of ICCVBIC 2019. The conferenceprovided a setting for discussing recent developments in a wide variety of topicsincluding computational vision, fuzzy, image processing, and bio-inspired com-puting. The conference has been a good opportunity for participants coming fromvarious destinations to present and discuss topics in their respective research areas.

ICCVBIC 2019 conference tends to collect the latest research results andapplications on computational vision and bio-inspired computing. It includes aselection of 147 papers from 397 papers submitted to the conference from uni-versities and industries all over the world. All of accepted papers were subjected tostrict peer-reviewing by 2–4 expert referees. The papers have been selected for thisvolume because of quality and the relevance to the conference.

ICCVBIC 2019 would like to express our sincere appreciation to all authors fortheir contributions to this book. We would like to extend our thanks to all thereferees for their constructive comments on all papers; especially, we would like tothank to organizing committee for their hardworking. Finally, we would like tothank the Springer publications for producing this volume.

S. SmysConference Chair

ix

Acknowledgments

ICCVBIC 2019 would like to acknowledge the excellent work of our conferenceorganizing the committee, keynote speakers for their presentation on September25–26, 2019. The organizers also wish to acknowledge publicly the valuable ser-vices provided by the reviewers.

On behalf of the editors, organizers, authors, and readers of this conference, wewish to thank the keynote speakers and the reviewers for their time, hard work, anddedication to this conference. The organizers wish to acknowledge Dr. Smys,Dr. Valentina Emilia Balas, Dr. Abdul M. Elias, and Dr. Joao Manuel R. S. Tavaresfor the discussion, suggestion, and cooperation to organize the keynote speakers ofthis conference. The organizers also wish to acknowledge for speakers and par-ticipants who attend this conference. Many thanks are given to all persons who helpand support this conference. ICCVBIC 2019 would like to acknowledge the con-tribution made to the organization by its many volunteers. Members contribute theirtime, energy, and knowledge at local, regional, and international levels.

We also thank all the chair persons and conference committee members for theirsupport.

xi

Contents

Psychosocial Analysis of Policy Confidence ThroughMultifactorial Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Jesús Silva, Darwin Solano, Claudia Fernández, Ligia Romero,and Omar Bonerge Pineda Lezama

Human Pose Detection: A Machine Learning Approach . . . . . . . . . . . . 8Munindra Kakati and Parismita Sarma

Machine Learning Algorithm in Smart Farmingfor Crop Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19N. D. Vindya and H. K. Vedamurthy

Analysis of Feature Extraction Algorithm Using TwoDimensional Discrete Wavelet Transforms in Mammogramsto Detect Microcalcifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Subash Chandra Bose, Murugesh Veerasamy, Azath Mubarakali,Ninoslav Marina, and Elena Hadzieva

Prediction of Fetal Distress Using Linear and Non-linear Featuresof CTG Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40E. Ramanujam, T. Chandrakumar, K. Nandhana, and N. T. Laaxmi

Optimizing Street Mobility Through a NetLogoSimulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Jesús Silva, Noel Varela, and Omar Bonerge Pineda Lezama

Image Processing Based Lane Crossing Detection Alert Systemto Avoid Vehicle Collision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56B. S. Priyangha, B. Thiyaneswaran, and N. S. Yoganathan

Stratified Meta Structure Based Similarity Measure in HeterogeneousInformation Networks for Medical Diagnosis . . . . . . . . . . . . . . . . . . . . . 62Ganga Gireesan and Linda Sara Mathew

xiii

An Effective Imputation Model for Vehicle Traffic Data Using StackedDenoise Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71S. Narmadha and V. Vijayakumar

Fruit Classification Using Traditional Machine Learningand Deep Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79N. Saranya, K. Srinivasan, S. K. Pravin Kumar, V. Rukkumani,and R. Ramya

Supervised and Unsupervised Learning Applied to Crowdfunding . . . . 90Oscar Iván Torralba Quitian, Jenny Paola Lis-Gutiérrez,and Amelec Viloria

Contextual Multi-scale Region Convolutional 3D Networkfor Anomalous Activity Detection in Videos . . . . . . . . . . . . . . . . . . . . . . 98M. Santhi and Leya Elizabeth Sunny

Noise Removal in Breast Cancer Using Hybrid De-noising Filterfor Mammogram Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109D. Devakumari and V. Punithavathi

Personality Trait with E-Graphologist . . . . . . . . . . . . . . . . . . . . . . . . . . 120Pranoti S. Shete and Anita Thengade

WOAMSA: Whale Optimization Algorithm for Multiple SequenceAlignment of Protein Sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131Manish Kumar, Ranjeet Kumar, and R. Nidhya

Website Information Architecture of Latin American Universitiesin the Rankings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140Carmen Luisa Vásquez, Marisabel Luna-Cardozo, Maritza Torres-Samuel,Nunziatina Bucci, and Amelec Viloria Silva

Classification of Hybrid Multiscaled Remote Sensing Scene UsingPretrained Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . 148Sujata Alegavi and Raghvendra Sedamkar

Object Detection and Classification Using GPU Acceleration . . . . . . . . . 161Shreyank Prabhu, Vishal Khopkar, Swapnil Nivendkar, Omkar Satpute,and Varshapriya Jyotinagar

Cross Modal Retrieval for Different Modalities in Multimedia . . . . . . . . 171T. J. Osheen and Linda Sara Mathew

Smart Wearable Speaking Aid for Aphonic Personnel . . . . . . . . . . . . . . 179S. Aswin, Ayush Ranjan, and K. V. Mahendra Prashanth

Data Processing for Direct Marketing Through Big Data . . . . . . . . . . . 187Amelec Viloria, Noel Varela, Doyreg Maldonado Pérez,and Omar Bonerge Pineda Lezama

xiv Contents

Brain Computer Interface Based Epilepsy Alert System UsingNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193Sayali M. Dhongade, Nilima R. Kolhare, and Rajendra P. Chaudhari

Face Detection Based Automation of Electrical Appliances . . . . . . . . . . 204S. V. Lokesh, B. Karthikeyan, R. Kumar, and S. Suresh

Facial Recognition with Open Cv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Amit Thakur, Abhishek Prakash, Akshay Kumar Mishra, Adarsh Goldar,and Ayush Sonkar

Dynamic Link Prediction in Biomedical Domain . . . . . . . . . . . . . . . . . . 219M. V. Anitha and Linda Sara Mathew

Engineering Teaching: Simulation, Industry 4.0 and Big Data . . . . . . . . 226Jesús Silva, Mercedes Gaitán, Noel Varela,and Omar Bonerge Pineda Lezama

Association Rule Data Mining in Agriculture – A Review . . . . . . . . . . . 233N. Vignesh and D. C. Vinutha

Human Face Recognition Using Local Binary PatternAlgorithm - Real Time Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240P. Shubha and M. Meenakshi

Recreation of 3D Models of Objects Using MAV and Skanect . . . . . . . . 247S. Srividhya, S. Prakash, and K. Elangovan

Inferring Machine Learning Based Parameter Estimationfor Telecom Churn Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257J. Pamina, J. Beschi Raja, S. Sam Peter, S. Soundarya, S. Sathya Bama,and M. S. Sruthi

Applying a Business Intelligence System in a Big Data Context:Production Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268Jesús Silva, Mercedes Gaitán, Noel Varela, Doyreg Maldonado Pérez,and Omar Bonerge Pineda Lezama

Facial Expression Recognition Using Supervised Learning . . . . . . . . . . 275V. B. Suneeta, P. Purushottam, K. Prashantkumar, S. Sachin,and M. Supreet

Optimized Machine Learning Models for Diagnosis and Predictionof Hypothyroidism and Hyperthyroidism . . . . . . . . . . . . . . . . . . . . . . . . 286Manoj Challa

Plastic Waste Profiling Using Deep Learning . . . . . . . . . . . . . . . . . . . . . 294Mahadevan Narayanan, Apurva Mhatre, Ajun Nair, Akilesh Panicker,and Ashutosh Dhondkar

Contents xv

Leaf Disease Detection Using Image Processing and ArtificialIntelligence – A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304H. Parikshith, S. M. Naga Rajath, and S. P. Pavan Kumar

Deep Learning Model for Image Classification . . . . . . . . . . . . . . . . . . . . 312Sudarshana Tamuly, C. Jyotsna, and J. Amudha

Optimized Machine Learning Approach for the Predictionof Diabetes-Mellitus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321Manoj Challa and R. Chinnaiyan

Performance Analysis of Differential Evolution AlgorithmVariants in Solving Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . 329V. SandhyaSree and S. Thangavelu

Person Authentication Using EEG Signal that UsesChirplet and SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338Sonal Suhas Shinde and Swati S. Kamthekar

Artificial Neural Network Hardware Implementation:Recent Trends and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345Jagrati Gupta and Deepali Koppad

A Computer Vision Based Fall Detection Techniquefor Home Surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355Katamneni Vinaya Sree and G. Jeyakumar

Prediction of Diabetes Mellitus Type-2 Using Machine Learning . . . . . . 364S. Apoorva, K. Aditya S, P. Snigdha, P. Darshini, and H. A. Sanjay

Vision Based Deep Learning Approach for Dynamic IndianSign Language Recognition in Healthcare . . . . . . . . . . . . . . . . . . . . . . . 371Aditya P. Uchil, Smriti Jha, and B. G. Sudha

A Two-Phase Image Classification Approach with Very Less Data . . . . 384B. N. Deepankan and Ritu Agarwal

A Survey on Various Shadow Detection and Removal Methods . . . . . . . 395P. C. Nikkil Kumar and P. Malathi

Medical Image Registration Using Landmark RegistrationTechnique and Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402R. Revathy, S. Venkata Achyuth Kumar, V. Vijay Bhaskar Reddy,and V. Bhavana

Capsule Network for Plant Disease and Plant Species Classification . . . 413R. Vimal Kurup, M. A. Anupama, R. Vinayakumar, V. Sowmya,and K. P. Soman

xvi Contents

RumorDetect: Detection of Rumors in Twitter Using ConvolutionalDeep Tweet Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422N. G. Bhuvaneswari Amma and S. Selvakumar

Analyzing Underwater Videos for Fish Detection, Countingand Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431G. Durga Lakshmi and K. Raghesh Krishnan

Plant Leaf Disease Detection Using Modified SegmentationProcess and Classificatıon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442K. Nirmalakumari, Harikumar Rajaguru, and P. Rajkumar

Neuro-Fuzzy Inference Approach for Detecting Stagesof Lung Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453C. Rangaswamy, G. T. Raju, and G. Seshikala

Detection of Lung Nodules Using Unsupervised MachineLearning Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463Raj Kishore, Manoranjan Satpathy, D. K. Parida, Zohar Nussinov,and Kisor K. Sahu

Comparison of Dynamic Programming and Genetic AlgorithmApproaches for Solving Subset Sum Problems . . . . . . . . . . . . . . . . . . . . 472Konjeti Harsha Saketh and G. Jeyakumar

Design of Binary Neurons with Supervised Learning for LinearlySeparable Boolean Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480Kiran S. Raj, M. Nishanth, and Gurusamy Jeyakumar

Image De-noising Method Using Median Type Filter, Fuzzy Logicand Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488S. R. Sannasi Chakravarthy and Harikumar Rajaguru

Autism Spectrum Disorder Prediction Using MachineLearning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496Shanthi Selvaraj, Poonkodi Palanisamy, Summia Parveen, and Monisha

Challenges in Physiological Signal Extraction from CognitiveRadio Wireless Body Area Networks for Emotion Recognition . . . . . . . 504A. Roshini and K. V. D. Kiran

Pancreatic Tumour Segmentation in Recent MedicalImaging – an Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514A. Sindhu and V. Radha

A Normal Approach of Online Signature Using Stroke PointWarping – A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523S. Prayla Shyry, P. Srivanth, and Derrick Thomson

Contents xvii

Spectral Density Analysis with Logarithmic Regression DependentGaussian Mixture Model for Epilepsy Classification . . . . . . . . . . . . . . . 529Harikumar Rajaguru and Sunil Kumar Prabhakar

Comprehensive Review of Various SpeechEnhancement Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536Savy Gulati

An Approach for Sentiment Analysis Using Gini Indexwith Random Forest Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541Manpreet Kaur

Mining High Utility Itemset for Online Ad Placement UsingParticle Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 555M. Keerthi, J. Anitha, Shakti Agrawal, and Tanya Varghese

Classification of Lung Nodules into Benign or Malignantand Development of a CBIR System for Lung CT Scans . . . . . . . . . . . . 563K. Bhavanishankar and M. V. Sudhamani

Correlation Dimension and Bayesian Linear DiscriminantAnalysis for Alcohol Risk Level Detection . . . . . . . . . . . . . . . . . . . . . . . 576Harikumar Rajaguru and Sunil Kumar Prabhakar

A Smart and Secure Framework for IoT Device BasedMultimedia Medical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583Shrujana Murthy and C. R. Kavitha

Exploration of an Image Processing Model for the Detectionof Borer Pest Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589Yogini Prabhu, Jivan S. Parab, and Gaurish M. Naik

Measurement of Acid Content in Rain Water Using WirelessSensor Networks and Artificial Neural Networks . . . . . . . . . . . . . . . . . . 598Sakshi Gangrade and Srinath R. Naidu

Modified Gingerbreadman Chaotic Substitutionand Transformation Based Image Encryption . . . . . . . . . . . . . . . . . . . . 606S. N. Prajwalasimha, Sidramappa, S. R. Kavya, A. S. Hema,and H. C. Anusha

Thresholding and Clustering with Singular Value Decompositionfor Alcoholic EEG Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615Harikumar Rajaguru and Sunil Kumar Prabhakar

FPGA Implementation of Haze Removal Technique Basedon Dark Channel Prior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624J. Varalakshmi, Deepa Jose, and P. Nirmal Kumar

xviii Contents

Impulse Noise Classification Using Machine Learning Classifierand Robust Statistical Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631K. Kunaraj, S. Maria Wenisch, S. Balaji, and F. P. Mahimai Don Bosco

An Online Platform for Diagnosis of Childrenwith Reading Disability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645P. Sowmyasri, R. Ravalika, C. Jyotsna, and J. Amudha

Review on Prevailing Difficulties Using IriS Recognition . . . . . . . . . . . . 656C. D. Divya and A. B. Rajendra

Performance Analysis of ICA with GMM and HMMfor Epilepsy Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 662Harikumar Rajaguru and Sunil Kumar Prabhakar

Evaluating the Performance of Various Types of Neural Networkby Varying Number of Epochs for Identifying Human Emotion . . . . . . 670R. Sofia and D. Sivakumar

Bleeding and Z-Line Classification by DWT Based SIFT UsingKNN and SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679R. Ponnusamy and S. Sathiamoorthy

RNA-Seq DE Genes on Glioblastoma Using Non Linear SVMand Pathway Analysis of NOG and ASCL5 . . . . . . . . . . . . . . . . . . . . . . 689Sandra Binoy, Vinai George Biju, Cynthia Basilia, Blessy B. Mathew,and C. M. Prashanth

Evaluation of Growth and CO2 Biofixation by Spirulina platensisin Different Culture Media Using Statistical Models . . . . . . . . . . . . . . . 697Carvajal Tatis Claudia Andrea, Suarez Marenco Marianella María,W. B. Morgado Gamero, Sarmiento Rubiano Adriana,Parody Muñoz Alexander Elías, and Jesus Silva

Breast Cancer Detection Using CNN on Mammogram Images . . . . . . . 708Kushal Batra, Sachin Sekhar, and R. Radha

Segmentation Based Preprocessing Techniques for Predictingthe Cervix Type Using Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . 717M. B. Bijoy, A. Ansal Muhammed, and P. B. Jayaraj

Design and Implementation of Biomedical Device for MonitoringFetal ECG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727Rama Subramanian, C. Karthikeyan, G. Siva Nageswara Rao,and Ramasamy Mariappan

A Comparative Review of Prediction Methods for Pima IndiansDiabetes Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735P. V. Sankar Ganesh and P. Sripriya

Contents xix

Instructor Performance Evaluation Through MachineLearning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751J. Sowmiya and K. Kalaiselvi

Advanced Driver Assistance System Using ComputerVision and IOT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 768M. Hemaanand, P. Rakesh Chowdary, S. Darshan, S. Jagadeeswaran,R. Karthika, and Latha Parameswaran

Segmentation and Classification of Primary Brain TumorUsing Multilayer Perceptron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 779Shubhangi S. Veer (Handore), Anuoama Deshpande, P. M. Patil,and Mahendra V. Handore

Dynamic and Chromatic Analysis for Fire Detection and AlarmRaising Using Real-Time Video Analysis . . . . . . . . . . . . . . . . . . . . . . . . 788P. S. Srishilesh, Latha Parameswaran, R. S. Sanjay Tharagesh,Senthil Kumar Thangavel, and P. Sridhar

Smart Healthcare Data Acquisition System . . . . . . . . . . . . . . . . . . . . . . 798Tanvi Ghole, Shruti Karande, Harshita Mondkar, and Sujata Kulkarni

A Sparse Representation Based Learning Algorithmfor Denoising in Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809Senthil Kumar Thangavel and Sudipta Rudra

A Collaborative Mobile Based Interactive Frameworkfor Improving Tribal Empowerment . . . . . . . . . . . . . . . . . . . . . . . . . . . 827Senthilkumar Thangavel, T. V. Rajeevan, S. Rajendrakumar,P. Subramaniam, Udhaya Kumar, and B. Meenakshi

IoT Cloud Based Waste Management System . . . . . . . . . . . . . . . . . . . . 843A. Sheryl Oliver, M. Anuradha, Anuradha Krishnarathinam, S. Nivetha,and N. Maheswari

Vision Connect: A Smartphone Based Object Detectionfor Visually Impaired People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863Devakunchari Ramalingam, Swapnil Tiwari, and Harsh Seth

Two-Phase Machine Learning Approach for Extractive SingleDocument Summarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 871A. R. Manju Priya and Deepa Gupta

Sensor Based Non Invasive Dual Monitoring System to MeasureGlucose and Potassium Level in Human Body . . . . . . . . . . . . . . . . . . . . 882K. T. Sithara Surendran and T. Sasikala

A State of Art Methodology for Real-Time SurveillanceVideo Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894Fancy Joy and V. Vijayakumar

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Disease Inference on Medical Datasets Using Machine Learningand Deep Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 902Arunkumar Chinnaswamy, Ramakrishnan Srinivasan,and Desai Prutha Gaurang

Artificial Intelligence Based System for Preliminary Roundsof Recruitment Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 909Sayli Uttarwar, Simran Gambani, Tej Thakkar, and Nikahat Mulla

Eye Movement Event Detection with Deep Neural Networks . . . . . . . . . 921K. Anusree and J. Amudha

A Review on Various Biometric Techniques, Its Features, Methods,Security Issues and Application Areas . . . . . . . . . . . . . . . . . . . . . . . . . . 931M. Gayathri, C. Malathy, and M. Prabhakaran

Blind Guidance System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 942A. S. Nandagopal and Ani Sunny

A Novel Multimodal Biometrics System with Fingerprintand Gait Recognition Traits Using Contourlet DerivativeWeighted Rank Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 950R. Vinothkanna and P. K. Sasikumar

Implementation of Real-Time Skin Segmentation Basedon K-Means Clustering Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 964Souranil De, Soumik Rakshit, Abhik Biswas, Srinjoy Saha,and Sujoy Datta

An Image Processing Based Approach for MonitoringChanges in Webpages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974Raj Kothari and Gargi Vyas

Vehicle Detection Using Point Cloud and 3D LIDAR Sensorto Draw 3D Bounding Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 983H. S. Gagana, N. R. Sunitha, and K. N. Nishanth

An Experiment with Random Walks and GrabCut in One CutInteractive Image Segmentation Techniques on MRI Images . . . . . . . . . 993Anuja Deshpande, Pradeep Dahikar, and Pankaj Agrawal

A Comprehensive Survey on Web Recommendations Systemswith Special Focus on Filtering Techniques and Usageof Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1009K. N. Asha and R. Rajkumar

A Review on Business Intelligence Systems UsingArtificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1023Aastha Jain, Dhruvesh Shah, and Prathamesh Churi

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Image Completion of Highly Noisy Images Using Deep Learning . . . . . 1031Piyush Agrawal and Sajal Kaushik

Virtual Vision for Blind People Using Mobile Cameraand Sonar Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1044Shams Shahriar Suny, Setu Basak, and S. M. Mazharul Hoque Chowdhury

Study on Performance of Classification Algorithms Basedon the Sample Size for Crop Prediction . . . . . . . . . . . . . . . . . . . . . . . . . 1051I. Rajeshwari and K. Shyamala

Global Normalization for Fingerprint Image Enhancement . . . . . . . . . . 1059Meghna B. Patel, Satyen M. Parikh, and Ashok R. Patel

Recurrent Neural Network for Content Based Image RetrievalUsing Image Captioning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1067S. Sindu and R. Kousalya

Using Deep Learning on Satellite Images to IdentifyDeforestation/Afforestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1078Apurva Mhatre, Navin Kumar Mudaliar, Mahadevan Narayanan,Aaditya Gurav, Ajun Nair, and Akash Nair

An Application of Cellular Automata: Satellite Image Classification . . . 1085S. Poonkuntran, V. Abinaya, S. Manthira Moorthi, and M. P. Oza

Morlet Wavelet Threshold Based Glow Warm Optimized X-MeansClustering for Forest Fire Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1094B. Pushpa and M. Kamarasan

Advance Assessment of Neural Network for Identificationof Diabetic Nephropathy Using Renal Biopsies Images . . . . . . . . . . . . . 1106Yogini B. Patil and Seema Kawathekar

Advanced Techniques to Enhance Human VisualAbility – A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1116A. Vani and M. N. Mamatha

Cross Media Feature Retrieval and Optimization:A Contemporary Review of Research Scope, Challengesand Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1125Monelli Ayyavaraiah and Bondu Venkateswarlu

Conversion from Lossless to Gray Scale Image Using Color SpaceConversion Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137C. S. Sridhar, G. Mahadevan, S. K. Khadar Basha, and P. Sudir

Iris Recognition Using Bicubic Interpolation and MultiLevel DWT Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146C. R. Prashanth, Sunil S. Harakannanavar, and K. B. Raja

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Retinal Image Classification System Using Multi Phase Level SetFormulation and ANFIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154A. Jayachandran, T. Sreekesh Namboodiri, and L. Arokia Jesu Prabhu

FSO: Issues, Challenges and Heuristic Solutions . . . . . . . . . . . . . . . . . . 1162Saloni Rai and Amit Kumar Garg

Design of Canny Edge Detection Hardware Accelerator UsingxfOpenCV Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1171Lokender Vashist and Mukesh Kumar

Application of Cloud Computing for Economic Load Dispatchand Unit Commitment Computations of the Power System Network . . . 1179Nithiyananthan Kannan, Youssef Mobarak, and Fahd Alharbi

Hamiltonian Fuzzy Cycles in Generalized Quartic Fuzzy Graphswith Girth k . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1190N. Jayalakshmi, S. Vimal Kumar, and P. Thangaraj

Effective Model Integration Algorithm for Improving PredictionAccuracy of Healthcare Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1203P. Monika and G. T. Raju

Detection of Leaf Disease Using Hybrid Feature ExtractionTechniques and CNN Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1213Vidyashree Kanabur, Sunil S. Harakannanavar, Veena I. Purnikmath,Pramod Hullole, and Dattaprasad Torse

Fish Detection and Classification Using ConvolutionalNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1221B. S. Rekha, G. N. Srinivasan, Sravan Kumar Reddy, Divyanshu Kakwani,and Niraj Bhattad

Feature Selection and Ensemble Entropy Attribute WeightedDeep Neural Network (EEAw-DNN) for Chronic Kidney Disease(CKD) Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1232S. Belina V. J. Sara and K. Kalaiselvi

Swarm Intelligence Based Feature Clustering for ContinuousSpeech Recognition Under Noisy Environments . . . . . . . . . . . . . . . . . . . 1248M. Kalamani, M. Krishnamoorthi, R. Harikumar, and R. S. Valarmathi

Video-Based Fire Detection by Transforming to OptimalColor Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1256M. Thanga Manickam, M. Yogesh, P. Sridhar, Senthil Kumar Thangavel,and Latha Parameswaran

Image Context Based Similarity Retrieval System . . . . . . . . . . . . . . . . . 1265Arpana D. Mahajan and Sanjay Chaudhary

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Investigation of Non-invasive Hemoglobin Estimation UsingPhotoplethysmograph Signal and Machine Learning . . . . . . . . . . . . . . . 1273M. Lakshmi, S. Bhavani, and P. Manimegalai

Sentiment Analysis on Tweets for a Diseaseand Treatment Combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1283R. Meena, V. Thulasi Bai, and J. Omana

Design and Performance Analysis of Artificial Neural NetworkBased Artificial Synapse for Bio-inspired Computing . . . . . . . . . . . . . . . 1294B. U. V. Prashanth and Mohammed Riyaz Ahmed

Sentiment Analysis of Amazon Book Review Data Using LexiconBased Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1303Fatema Khatun, S. M. Mazharul Hoque Chowdhury, Zerin Nasrin Tumpa,SK. Fazlee Rabby, Syed Akhter Hossain, and Sheikh Abujar

MicroRNA-Drug Association Prediction by Known NearestNeighbour Algorithm and Label Propagation Using LinearNeighbourhood Similarities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1310Gill Varghese Sajan and Joby George

RF-HYB: Prediction of DNA-Binding Residues and Interactionof Drug in Proteins Using Random-Forest Modelby Hybrid Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1319E. Shahna and Joby George

Investigation of Machine Learning Methodologiesin Microaneurysms Discernment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1327S. Iwin Thanakumar Joseph, Tatiparthi Sravanthi, V. Karunakaran,and C. Priyadharsini

Segmentation Algorithm Using Temporal Features and GroupDelay for Speech Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1335J. S. Mohith B. Varma, B. Girish K. Reddy, G. V. S. N. Koushik,and K. Jeeva Priya

Efficient Ranking Framework for Information Retrieval UsingSimilarity Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1344Shadab Irfan and Subhajit Ghosh

Detection and Removal of RainDrop from ImagesUsing DeepLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1355Y. Himabindu, R. Manjusha, and Latha Parameswaran

A Review on Recent Developments for the Retinal VesselSegmentation Methodologies and Exudate Detection in FundusImages Using Deep Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 1363Silpa Ajith Kumar and J. Satheesh Kumar

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Smart Imaging System for Tumor Detection in Larynx UsingRadial Basis Function Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1371N. P. G. Bhavani, K. Sujatha, V. Karthikeyan, R. Shobarani,and S. Meena Sutha

Effective Spam Image Classification Using CNNand Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1378Shrihari Rao and Radhakrishan Gopalapillai

Unsupervised Clustering Algorithm as Region of Interest Proposalsfor Cancer Detection Using CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1386Ajay K. Gogineni, Raj Kishore, Pranay Raj, Suprava Naik,and Kisor K. Sahu

Assistive Communication Application for AmyotrophicLateral Sclerosis Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1397T. Shravani, Ramya Sai, M. Vani Shree, J. Amudha, and C. Jyotsna

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1409

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