Intelligent Learning for Computer Vision: Proceedings of Congress on Intelligent Systems 2020 PDF
Preview Intelligent Learning for Computer Vision: Proceedings of Congress on Intelligent Systems 2020
Lecture Notes on Data Engineering and Communications Technologies 61 Harish Sharma Mukesh Saraswat Sandeep Kumar Jagdish Chand Bansal Editors Intelligent Learning for Computer Vision Proceedings of Congress on Intelligent Systems 2020 Lecture Notes on Data Engineering and Communications Technologies Volume 61 Series Editor Fatos Xhafa, Technical University of Catalonia, Barcelona, Spain Theaimofthebookseriesistopresentcuttingedgeengineeringapproachestodata technologiesandcommunications.Itwillpublishlatestadvancesontheengineering taskofbuildinganddeployingdistributed,scalableandreliabledatainfrastructures and communication systems. The series will have a prominent applied focus on data technologies and communications with aim to promote the bridging from fundamental research on data science and networking to data engineering and communications that lead to industry products, business knowledge and standardisation. Indexed by SCOPUS, INSPEC, EI Compendex. AllbookspublishedintheseriesaresubmittedforconsiderationinWebofScience. More information about this series at http://www.springer.com/series/15362 Harish Sharma Mukesh Saraswat (cid:129) (cid:129) Sandeep Kumar Jagdish Chand Bansal (cid:129) Editors Intelligent Learning for Computer Vision Proceedings of Congress on Intelligent Systems 2020 123 Editors HarishSharma MukeshSaraswat Department ofComputer Science Department ofComputer Science& andEngineering Engineering andInformation Technology RajasthanTechnical University JaypeeInstitute ofInformation Technology Kota, Rajasthan, India Noida, Uttar Pradesh, India Sandeep Kumar Jagdish ChandBansal Department ofComputer Science Department ofMathematics andEngineering SouthAsianUniversity CHRIST (Deemed to beUniversity) NewDelhi, Delhi, India Bangalore, Karnataka, India ISSN 2367-4512 ISSN 2367-4520 (electronic) Lecture NotesonDataEngineering andCommunications Technologies ISBN978-981-33-4581-2 ISBN978-981-33-4582-9 (eBook) https://doi.org/10.1007/978-981-33-4582-9 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SingaporePteLtd.2021 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseof illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregard tojurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSingaporePteLtd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface This volume contains the papers presented at the first Congress on Intelligent Systems (CIS-2020), a world conference in virtual format organized by Soft Computing Research Society during September 05–06, 2020. The Congress on Intelligent Systems (CIS-2020) invited ideas, developments, applications, experi- ences, and evaluations in the field of Intelligent Systems from academicians, research scholars, and scientists. The conference deliberation included topics specified within its scope. The conference offered a platform for bringing forward extensiveresearchandliteratureacrossthearenaofIntelligentSystems.Itprovided an overview of the upcoming technologies. CIS-2020 provided a platform for leading experts to share their perceptions, provide supervision, and address par- ticipant’sinterrogationsandconcerns.CIS-2020received687researchsubmissions from 38 different countries, viz. Argentina, Australia, Bangladesh, Botswana, Canada, China, Colombia, Egypt, Ethiopia, France, India, Iran, Iraq, Italy, Japan, Latvia, Malaysia, Mauritius, Morocco, New Zealand, Oman, Pakistan, Palestine, Portugal, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sri Lanka, Taiwan, Tunisia, Turkey, Ukraine, United Arab Emirates, UK, USA, and Vietnam. The papers included topics pertaining to varied contemporary areas in technology, artificial intelligence, machine learning, and blockchain, etc. After a rigorous peer review with the help of program committee members and more than hundred external reviewers, 170 papers were selected for presentation and 45 papers selected for publication of this proceeding. CIS-2020 is a flagship event of Soft Computing Research Society, India. The conference was inaugurated by Prof. Kusum Deep and Prof. Atulya Nagar along with general chair Prof. Joong Hoon Kim, Prof. Jagdish Chand Bansal, Dr. Harish Sharma, and Dr. Mukesh Saraswat. The conference witnessed keynote addresses from eminent speakers, namely Prof. Maurice Clerc, Prof. Swagatam Das, Prof. Meng-Hiot Lim, Prof. Jonathan H. Chan, Prof. Mohammad Shorif Uddin, v vi Preface Prof. Andries Engelbrecht, Prof. Amir H Gandomi, Mr. Aninda Bose, Prof. Nishchal K. Verma, and Prof. Akhil Ranjan Garg. The organizers wish to thank editors from Springer Nature for their support and guidance. Kota, India Harish Sharma Noida, India Mukesh Saraswat Bangalore, India Sandeep Kumar New Delhi, India Jagdish Chand Bansal Contents Development of Inter-ethnic Harmony Search Algorithm Based on Inter-ethnic Reconciliation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 HyunWooJung,YoungHwanChoi,DonghwiJung,andJoongHoonKim ALow-CostEmbeddedComputerVisionSystemfortheClassification of Recyclable Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Karl Myers and Emanuele Lindo Secco An Optimal Feature Selection Approach Using IBBO for Histopathological Image Classification . . . . . . . . . . . . . . . . . . . . . . . 31 Mukesh Saraswat, Raju Pal, Roop Singh, Himanshu Mittal, Avinash Pandey, and Jagdish Chand Bansal Accuracy Evaluation of Plant Leaf Disease Detection and Classification Using GLCM and Multiclass SVM Classifier . . . . . . 41 K. Rajiv, N. Rajasekhar, K. Prasanna Lakshmi, D. Srinivasa Rao, and P. Sabitha Reddy A Deep Learning Technique for Automatic Teeth Recognition in Dental Panoramic X-Ray Images Using Modified Palmer Notation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Fahad Parvez Mahdi and Syoji Kobashi Detection of Parkinson’s Disease from Hand-Drawn Images Using Deep Transfer Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Akalpita Das, Himanish Shekhar Das, Arijeet Choudhury, Anupal Neog, and Sourav Mazumdar AnEmpiricalAnalysisofHierarchicalandPartition-BasedClustering Techniques in Optic Disc Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . 85 J. Prakash and B. Vinoth Kumar vii viii Contents Multi-class Support Vector Machine-Based Household Object Recognition System Using Features Supported by Point Cloud Library. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Smita Gour, Pushpa B. Patil, and Basavaraj S. Malapur Initialization of MLP Parameters Using Deep Belief Networks for Cancer Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Barış Dinç, Yasin Kaya, and Serdar Yıldırım An Improved Inception Layer-Based Convolutional Neural Network for Identifying Rice Leaf Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 B. Baranidharan, C. N. S. Vinoth Kumar, and M. Vasim Babu Design and Implementation of Traffic Sign Classifier Using Machine Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Samarth Patel, Pankaj Agarwal, Vijander Singh, and Linesh Raja Designing Controller Parameter of Wind Turbine Emulator Using Artificial Bee Colony Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . 143 Ajay Sharma, Harish Sharma, Ashish Khandelwal, and Nirmala Sharma Text Document Orientation Detection Using Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Shivam Aggarwal, Safal Singh Gaur, and Manju A Deep Learning-Based Segregation of Housing Image Data for Real Estate Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Annu Kumari, Vinod Maan, and Dhiraj Improved Image Super-resolution Using Enhanced Generative Adversarial Network a Comparative Study . . . . . . . . . . . . . . . . . . . . . . 181 B. V. Balaji Prabhu and Omkar Subburao Jois Narasipura Comparative Study of Supervised Machine Learning Algorithms for Healthcare Dataset Using Orange. . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Vaibhav Bhatnagar and Ramesh C. Poonia Maximum Power Point Tracking of Photovoltaic System Using Artificial Neural Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Kusum Lata Agarwal and Shubham Sharma IoT Security: A Survey of Issues, Attacks and Defences . . . . . . . . . . . . 219 Vinesh Kumar Jain and Jyoti Gajrani Detecting the Nuclei in Different Pictures Using Region Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Naiswita Parmar Chaotic Henry Gas Solubility Optimization Algorithm . . . . . . . . . . . . . 247 Nand Kishor Yadav and Mukesh Saraswat Contents ix Electric Load Forecasting Using Fuzzy Knowledge Base System with Improved Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Bhavesh Kumar Chauhan and Praveen Kumar Shukla Deep Learning-Based Framework for Retinal Vasculature Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Shambhavi Shikha Tiwari, Akash Dholaria, Rajat Pandey, Gauri Nigam, Ranjana Agrawal, Rahee Walambe, and Ketan Kotecha EcDEALS:AdaptiveLocalSearchStrategiesinDifferentialEvolution for Escalating Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Harish Sharma, Prashant Sharma, Kavita Sharma, and Rajani Kumari Optimization of Regularization and Early Stopping to Reduce Overfitting in Recognition of Handwritten Characters. . . . . . . . . . . . . . 305 Naman Jain Employing Data Augmentation for Recognition of Hand Gestures Using Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Deepak Kumar, Abdul Aleem, and Manoj Madhava Gore Comparative Design Analysis of Optimized Learning Rate for Convolutional Neural Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Rashmi, Udayan Ghose, and Manushree Gupta Vision-Based Vehicle Detection and Tracking System . . . . . . . . . . . . . . 353 N. Kavitha and D. N. Chandrappa An Optimal Feature Based Automatic Leaf Recognition Model Using Deep Neural Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Aditi Ghosh and Parthajit Roy Super-Resolution of Level-17 Images Using Generative Adversarial Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 B. V. Balaji Prabhu, Nikith P. Salian, B. M. Nikhil, and Omkar Subbaram Jois Narasipura Hand Gesture Recognition System Using IoT and Machine Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 B. N. Ajay, S. Aditya, A. S. Adarsha, N. Deekshitha, and K. Harshitha Improved Video Compression Using Variable Emission Step ConvGRU Based Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Sangeeta and Preeti Gulia Video Surveillance System with Auto Informing Feature. . . . . . . . . . . . 417 Ekta Tyagi, Deeksha, Vikas Sahu, Chandranshu Malhotra, Shubham Poddar, Jayash Verma, and Lokesh Chouhan
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