Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough

Gunjan, Vinit Kumar (Editor)/ Zurada, Jacek M. (Editor)

ISBN 10: 3030966364 / ISBN 13: 9783030966362

Editore: Springer Nature, 2023

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569 pages. 9.25x6.10x1.16 inches. In Stock.

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Dati bibliografici

• Titolo: Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough
• Editore: Springer Nature
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 3030966364
• ISBN 13: 9783030966362
• Rilegatura: Paperback
• Condizione: Brand New
• Peso: 0,79 chilogrammi

Descrizione articolo

Riassunto

This book provides a systematic and comprehensive overview of AI and machine learning which have got the ability to identify patterns in large and complex data sets. A remarkable success has been experienced in the last decade by emulating the brain computer interface. It presents the cognitive science methods and technologies that have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focuses on audiences interested in machine learning, cognitive and neuro-inspired computational systems, their theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions on applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming.

Informazioni sull?autore

Vinit Kumar Gunjan is Associate Professor in Department of Computer Science & Engineering and Dean of Academic affairs at CMR Institute of Technology Hyderabad (Affiliated to Jawaharlal Nehru Technological University, Hyderabad). He is an active researcher; he has published research papers in IEEE, Elsevier and Springer conferences, authored several books and edited volumes of Springer series, most of which are indexed in SCOPUS database. He was awarded with the prestigious Early Career Research Award in the year 2016 by Science Engineering Research Board, Department of Science & Technology, Government of India. He is Senior Member of IEEE,  active Volunteer of IEEE Hyderabad Section, and 2021 Additional Secretary; he is 2021 Vice Chairman—IEEE Computational Intelligence Society; he is volunteered in the capacity of Treasurer, Secretary and Chairman of IEEE Young Professionals Affinity Group and IEEE Computer Society. He was involved as organizer in many technical and non-technical workshops, seminars and conferences of IEEE and Springer. During the tenure, he had an honour of working with top leaders of IEEE and was awarded with outstanding IEEE Young Professional Award in 2017 by IEEE Hyderabad Section.

Jacek M. Zurada is Professor of Electrical and Computer Engineering and Director of the Computational Intelligence Laboratory at the University of Louisville, Kentucky, 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, including the pioneering text Introduction to artificial neural systems, co-edited the volumes computational intelligence: imitating life, knowledge-based neurocomputing, and co-edited twenty volumes in Springer Lecture Notes on Computer Science. 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.