Computational Intelligence in Machine Learning (eBook, PDF)

Proceedings of the 2nd International Conference ICCIML 2022
Redaktion: Gunjan, Vinit Kumar; Singh, Sri Niwas; Zurada, Jacek M.; Kumar, Amit

• Format: PDF

Produktbeschreibung

This volumes comprises select proceedings of the International Conference on Computational Intelligence in Machine Learning (ICCIML 2022). The contents cover latest research trends and developments in the areas of machine learning, smart cities, IoT, Artificial Intelligence, cyber physical systems, cybernetics, data science, neural network, cognition, among others. It also addresses the comprehensive nature of computational intelligence, AI, ML and DL to emphasize its character in modelling, identification, optimization, prediction, forecasting, and control of future intelligent systems. This volume will be a useful guide to those working as researchers in academia and industry by presenting in-depth fundamental research contributions from a methodological/application perspective in understanding Artificial intelligence and machine learning approaches and their capabilities in solving diverse range of problems in industries and its real-world applications.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 707
• Erscheinungstermin: 20. Februar 2024
• Englisch
• ISBN-13: 9789819979547
• Artikelnr.: 70014131

Autorenporträt

Vinit Kumar Gunjan is an Associate Professor in the Department of Computer Science & Engineering at CMR Institute of Technology India (affiliated with Jawaharlal Nehru Technological University, Hyderabad). Dr. Gunjan is an active researcher and published research papers with high-quality conferences authored several books and edited volumes. He was awarded the prestigious Early Career Research Award in 2016 by the Science Engineering Research Board, Department of Science & Technology, Government of India. He has been involved in several technical and non-technical workshops, seminars, and conferences. During his tenure, worked with top leaders of IEEE and was awarded the best IEEE Young Professional award in 2017 by IEEE Hyderabad Section.  Amit Kumar is a DNA forensics professional, entrepreneur, engineer, bioinformatician, and an IEEE volunteer. In 2005, he founded the first private DNA testing Company Bio Axis DNAResearch Centre (P.) Ltd in Hyderabad, India, with a US collaborator. He has vast experience in training 1000+ crime investigating officers and helped 750+ criminal and non-criminal cases to reach justice by offering analytical services in his laboratory. His group also works extensively on genetic predisposition risk studies of cancers and has been helping many cancer patients since 2012 to fight and win the battle against cancer. He was a member of the IEEE Strategy Development and Environmental Assessment Committee (SDEA) of IEEE MGA. He has driven several conferences, conference leadership programs, entrepreneurship development workshops, innovation, and internship-related events. Currently, he is Managing Director of BioAxis DNA Research Centre (P) Ltd and IEEE MGA Nominations and Appointments committee member.  Jacek M. Zurada is a Professor of Electrical and Computer Engineering and Director of the Computational Intelligence Laboratory at the University of Louisville, USA, where he served as Department Chair and Distinguished University Scholar. He received his M.S. and Ph.D. degrees (with distinction) in electrical engineering from the Technical University of Gdansk, Poland. He has published over 420 journal and conference papers in neural networks, deep learning, computational intelligence, data mining, image processing, and VLSI circuits. He has authored or co-authored three books. In addition to his pioneering neural networks textbook, his most recognized achievements include an extension of complex-valued neurons to associative memories and perception networks; sensitivity concepts applied to multilayer neural networks; application of networks to clustering, biomedical image classification, and drug dosing; blind sources separation; and rule extraction as a tool for prediction of protein secondary structure.  S. N. Singh obtained his M. Tech. and Ph.D. in Electrical Engineeringfrom the Indian Institute of Technology (IIT) Kanpur, in 1989 and 1995, respectively. Presently, Prof. Singh is Director, Atal Bihari Bajpayee- Indian Institute of Information Technology and Management Gwalior (MP), India (on leave from Professor (HAG), Department of Electrical Engineering, Indian Institute of Technology Kanpur, India). His research interests include power system restructuring, FACTS, power system optimization & control, security analysis, wind power, etc. Prof Singh has published more than 500 papers in international/national journals/conferences and supervised 40 Ph.D. (8 Ph.D. under progress). He has also written 30 book chapters, 8 edited books, and 2 textbooks. Prof. Singh has completed three dozen technical projects in India and abroad.

Inhaltsangabe

Machine Learning-based Project Resource Allocation Fitment Analysis System - (ML-PRAFS).- Electric Theft Detection using Un-supervised Machine Learning Based Matrix Profile and K Means Clustering Technique.- Placement Analysis - A New Approach to Ease the Recruitment Process.- Continuous Assessment Analyzer using Django.- Fuzzy Logic in Battery Energy Storage System (Bess).- Fault Classification of Cooling Fans using a CNN-based Approach.- Violence Recognition using Convolutional Neural Networks.- Automated Grading of Citrus Suhuiensis Fruit using Deep Learning Method.- The Future of Car Automation Field with Smart Driverless Technologies.- Diagnosis and Medicine Prediction for Covid-19 using Machine Learning Approach.- Automated Guided Vehicle Robot Localization with Sensor Fusion.- Implementation of Industrial Automation Water Distribution System Utilizing PLC: A Laboratory Set-up.- Control of Thin McKibben Muscles in an Antagonistic Pair Configuration.- Defect Severity Classification of Complex Composites using CWT and CNN.- Detection of Mobile Phone Usage While Driving using Computer Vision and Deep Learning.- Industry Revolution 4.0 Knowledge Assessment in Malaysia.