Advanced Machine Learning with Evolutionary and Metaheuristic Techniques (Computational Intelligence Methods and Applications) - Tapa blanda

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Dr. Jayaraman Valadi  is a Distinguished Professor of Computer Science at FLAME University, Pune, India. He earned his Doctorate degree in Chemistry from Pune University. His research encompasses diverse areas, focusing on modeling and simulations in chemical and biochemical engineering, as well as process modeling, control, and optimization. Over the past decade, he has dedicated his efforts to exploring applications of Machine Learning and Artificial intelligence across various domains. He has dozens of publications in various reputed international journals. Beginning his journey in 1976, Dr. Valadi was associated with the Council of Industrial and Scientific Research (CSIR) in India, where he worked for 33 years and retired as a Deputy Director in 2009. After that, he was a CSIR Emeritus Scientist at the Center for Development of Advanced Computing, Pune till January 2013 & thereafter as a visiting faculty at Shiv Nadar University, Greater Noida, India until May 2023.

 

Dr. Krishna Pratap Singh is an Associate Professor in the Department of Information Technology at the Indian Institute of Information Technology Allahabad (IIITA), India, where he also heads the Machine Learning and Optimization (MLO) Lab. Dr. Singh earned his Ph.D. in Optimization (2009) from IIT Roorkee, and has over 15 years of research and academic experience. He is a member of the Sakura Science Club, Japan, Senior member IEEE and ACM Member. Currently, his research group is working on Transfer Learning for low resources data and towards developing a model in a Federated learning setting.

 

Dr. Muneendra Ojha is an Assistant Professor in the Department of Information Technology at the Indian Institute of Information Technology Allahabad (IIITA), India, and leading the Artificial Intelligence and Multiagent Systems (AIMS) lab. Dr. Ojha earned his Ph.D. from IIITA and MS from the University of Missouri-Columbia, USA.Dr. Ojha has more than 19 years of academic and industry experience. His research interests include multi-objective optimization, evolutionary algorithms, semantic web, natural language processing, deep reinforcement learning, and multi-agent systems.

 

Dr. Patrick Siarry received the PhD degree from the University Paris 6, in 1986 and the Doctorate of Sciences(Habilitation) from the University of Paris 11, in 1994. He was first involved in the development of analog and digital models of nuclear power plants at  Electricité de France (EDF. Since 1995 he is a full Professor of automatics and informatics. His main research interests are the adaptation of new stochastic global optimization heuristics to various situations (multi objective mixed discrete-continuous variables, continuous variables, dynamic,etc.) and their application to various engineering fields. He is also interested in the fitting of process models to experimental data and thelearning of fuzzy rule bases and neural networks. P.Siarry is a senior member  IEEE,  an appointed member of the Technical Committee on Soft Computing of the IEEE systems, Man and Cybernetics (SMC) Society and an appointed member of the Technical Committee on Optimal Control (TC 2.4) of IFAC.

De la contraportada

This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning.