Neuware - COMPUTATIONAL INTELLIGENCE IN SUBSTAINABLE RELIABILITY ENGINEERINGThe book is a comprehensive guide on how to apply computational intelligence techniques for the optimization of sustainable materials and reliability engineering.This book focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in reliability engineering, material design, and manufacturing to ensure sustainability. Computational Intelligence in Sustainable Reliability Engineering unveils applications of different models of evolutionary algorithms in the field of optimization and solves the problems to help the manufacturing industries. Some special features of this book include a comprehensive guide for utilizing computational models for reliability engineering, state-of-the-art swarm intelligence methods for solving manufacturing processes and developing sustainable materials, high-quality and innovative research contributions, and a guide for applying computational optimization on reliability and maintainability theory. The book also includes dedicated case studies of real-life applications related to industrial optimizations.AudienceResearchers, industry professionals, and post-graduate students in reliability engineering, manufacturing, materials, and design.

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Bibliographic Details

Title: Computational Intelligence in Sustainable Reliability Engineering
Publisher: Wiley M�r 2023
Publication Date: 2023
Language: English
ISBN 10: 1119865018
ISBN 13: 9781119865018
Binding: Buch
Condition: Neu
Dimensions: 231 millimeters width by 160 millimeters height by 25 millimeters depth
Weight: 558 grams

About this title

Synopsis

COMPUTATIONAL INTELLIGENCE IN SUBSTAINABLE RELIABILITY ENGINEERING

The book is a comprehensive guide on how to apply computational intelligence techniques for the optimization of sustainable materials and reliability engineering.

This book focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in reliability engineering, material design, and manufacturing to ensure sustainability. Computational Intelligence in Sustainable Reliability Engineering unveils applications of different models of evolutionary algorithms in the field of optimization and solves the problems to help the manufacturing industries. Some special features of this book include a comprehensive guide for utilizing computational models for reliability engineering, state-of-the-art swarm intelligence methods for solving manufacturing processes and developing sustainable materials, high-quality and innovative research contributions, and a guide for applying computational optimization on reliability and maintainability theory. The book also includes dedicated case studies of real-life applications related to industrial optimizations.

Audience

Researchers, industry professionals, and post-graduate students in reliability engineering, manufacturing, materials, and design.

About the Author

S. C. Malik, PhD, is a professor of Statistics at Maharshi Dayanand University Rohtak, India. He has published more than 170 research articles in international journals, has participated in about 80 national/international conferences and workshops, as well as authored 3 books.

Deepak Sinwar, PhD, is an assistant professor in the Department of Computer and Communication Engineering, School of Computing & Information Technology at Manipal University Jaipur, Jaipur, Rajasthan, India. His research interests include computational intelligence, data mining, machine learning, reliability theory, computer networks, and pattern recognition.

Ashish Kumar, PhD, is an assistant professor in the Department of Mathematics & Statistics, Manipal University Jaipur, Jaipur. He has published more than 80 research papers in various national/international journals and participated in more than 50 conferences in India and abroad. His area of interest is reliability modeling and analysis, sampling theory, reliability estimation, and data analysis.

Gadde Srinivasa Rao, PhD, is a Professor of Statistics in the Department of Statistics, Dodoma University, Tanzania. He has published more than 140 articles in peer-reviewed journals and participated in more than 70 national and international conferences. His research interests include statistical inference, quality control, and reliability estimation.

Prasenjit Chatterjee, PhD, is the Dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has more than 100 research papers in various international journals and peer-reviewed conferences. He has authored and edited more than 20 books and is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).

Bui Thanh Hung, PhD, is the Director of the Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Vietnam, and received his doctorate from Japan Advanced Institute of Science and Technology (JAIST) in 2013. He has published numerous research articles in international journals and conferences as well as 14 book chapters. His main research interests are natural language processing, machine learning, machine translation, text processing, data analytics, computer vision, and artificial intelligence.

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