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Computational Intelligence in Sustainable Reliability Engineering

Book Hero Magic crafted this summary to help describe this book. While it's new and still learning, it may not be perfect - your feedback is welcome! Summary
In Computational Intelligence in Sustainable Reliability Engineering, the book explores innovative computational techniques aimed at enhancing the reliability and sustainability of engineering systems. It delves into the fusion of artificial intelligence, machine learning, and reliability engineering practices, focusing on real-world applications that improve system performance and durability. This resource offers insights into optimising engineering processes while emphasising environmentally friendly practices.
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Format: Hardback
$37199
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

This book may appeal to you if you are interested in how advanced computational techniques can be applied to enhance reliability engineering with a focus on sustainability. You will find the exploration of innovative methods in artificial intelligence and their practical applications beneficial, particularly if you're engaged in fields demanding efficient and sustainable engineering solutions.

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Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! Description

Computational Intelligence in Sustainable Reliability Engineering is a comprehensive guide on how to apply computational intelligence techniques for the optimisation 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 optimisation and solves the problems to help the manufacturing industries.

Some special features of this book include a comprehensive guide for utilising 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 optimisation on reliability and maintainability theory. The book also includes dedicated case studies of real-life applications related to industrial optimisations.

Audience

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

Series: Smart and Sustainable Intelligent Systems

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  • Computational Intelligence in Sustainable Reliability Engineering
    Computational Intelligence in Sustainable Reliability Engineering is a comprehensive guide on how to apply computational intelligence techniques for the optimisation 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...
    Hardback
    $37199
    Available
    Ships in 3-4 weeks

Book Details

INFORMATION

ISBN: 9781119865018

Publisher: John Wiley & Sons Inc

Format: Hardback

Date Published: 23 March 2023

Country: United States

Imprint: Wiley-Scrivener

Contributors:

  • Edited by Prasenjit Chatterjee
  • Edited by Ashish Kumar
  • Edited by Deepak Sinwar
  • Edited by Bui Thanh Hung
  • Edited by S. C. Malik
  • Edited by S. R. Gadde
  • Edited by Prasenjit Chatterjee

Audience: Professional and scholarly

DIMENSIONS

Weight: 730g

Pages: 352

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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