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    Soft Computing and Artificial Intelligence in System Safety and Reliability Analysis

    Recent Advances and Perspectives

    AvHarish Garg,Fatemeh Afsharnia

    Häftad, Engelska, 2027

    Del i serien Advances in Reliability Science

    2 057 kr

    Kommande

    Beskrivning

    Soft Computing and Artificial Intelligence in System Safety and Reliability Analysis: Recent Advances and Perspectives provides comprehensive and easy-to-understand information in three main fields of safety and reliability including “AI in reliability and safety science”, “soft computing Applications in safety analysis” and “Operation and Maintenance”. The book's content thoroughly and succinctly addresses a variety of safety and dependability-related topics. The book's contents will be easily understood by everyone, even those with little prior knowledge of the subject, due to its straightforward writing style. Every topic addressed in the book is complemented by illustrations, which aid with conceptual understanding. Using this book, all audiences will learn about different types of recent theoretical and methodological advances in ensemble AI-driven metaheuristic optimization algorithms and their applications in all aspects of safety and reliability, different types of soft computing, as well as hybridization of metaheuristics with other soft computing and artificial intelligence tools, and would be able to perform operations like designing, identifying, choosing the best AI-driven algorithms for the safety and reliability analysis, and maintenance optimization. The main advantage of this book is that all the required and recent knowledge of safety and reliability is provided in one place without the need to read several books and papers which have been written professionally and are therefore hard to understand especially for those who are interested in increasing the knowledge on safety and reliability but have no prior background in this field. Moreover, this book informs the reader about each aspect of heuristic and/or metaheuristic techniques and the applications thereof in the context of the most recent advances in science and engineering and to provide insight into possible future developments in the field of safety and reliability.

    • Comprehensive information on soft computing and artificial intelligence in different aspects of safety, reliability, and maintenance
    • Introductory knowledge on safety, reliability, and maintenance, then applications of soft computing, and artificial intelligence in safety, and reliability, and finally maintenance optimization
    • All the required information on safety, reliability, and maintenance in one place in a simple, comprehensive, and compact way

    Produktinformation

    • Utgivningsdatum:2027-03-01
    • Mått:152 x 229 x undefined mm
    • Vikt:450 g
    • Format:Häftad
    • Språk:Engelska
    • Serie:Advances in Reliability Science
    • Antal sidor:300
    • Förlag:Elsevier Science
    • ISBN:9780443439902

    Utforska kategorier

    • Maskinteknik och material inom Naturvetenskap och teknik
    • Elektronik och kommunikationer inom Naturvetenskap och teknik
    • Artificiell intelligens inom Data och IT

    Mer om författaren

    Dr. Garg is Associate Professor of Mathematics at Thapar Institute of Engineering and Technology, Patiala, Punjab, India. He is the recipient of the Obada-Prize 2022 – Young Distinguished Researchers. He is also the recipient of the Top-Cited paper by an India-based author (2015 – 2019) from Elsevier Publisher. He also serves as an advisory board member of the Universal Scientific Education and Research Network (USERN).Dr. Garg's research interests include computational intelligence, multi-criteria decision making, evolutionary algorithms, reliability analysis, expert systems, and decision support systems, computing with words, and soft computing. He has authored more than 400 papers published in refereed international journals. He has also authored seven book chapters. He has also edited 8 books from Elsevier, Springer, and other publishers. Dr. Garg also serves on editorial boards of several leading international journals, this includes the Founding Editor-in-Chief of the Journal of Computational and Cognitive Engineering. He is also the Associate Editor of IEEE Transaction of Fuzzy Systems, Soft Computing, Alexandria Engineering Journal, Journal of Intelligent & Fuzzy Systems, Complex and Intelligent Systems, Journal of Industrial & Management Optimization, and CAAI Transactions on Intelligence Technology. Fatemeh Afsharnia completed her Ph.D. in Mechanization Engineering from Agricultural Sciences and Natural Resources University of Khuzestan in 2021 and postdoctoral studies from Ferdowsi University of Mashhad. She was also a visiting scholar at the Condition Monitoring Center at the Sharif University of Technology in 2019. She was awarded the "Top Student Award” in 2010, and the “Best Research Student of University Award” in 2019. Dr. Afsharnia’s fields of study are Reliability engineering and analysis, Maintenance management, Maintenance planning, Regression modelling, Reliability theory, Risk assessment and analysis, Safety management and engineering, Probabilistic risk analysis, Maintenance optimization, Condition-based maintenance, etc. She is a member of the Iranian Maintenance Association (IRMA) and has published over 30 publications in several reputed journals and books. Moreover, she has been serving as a reviewer of the Journal of Industrial Information Integration, Computers & Industrial Engineering, ISA Transactions, Measurement, Journal of Food Engineering, and Computers and Electronics in Agriculture Journals published by Elsevier. Mohammad Yazdi completed a dual Ph.D. program at the Centre for Risk, Integrity, and Safety Engineering (C-RISE), at the Memorial University of Newfoundland, Canada, and Macquarie University in Australia. Prior to graduate school, Mohammad took on multifaceted roles such as a Firefighter, Safety Officer, WHS/OHS Advisor, and Auditor across various industrial and energy sectors. His research interests and professional background converge at the nexus of system safety, risk assessment, resilience, process integrity, and asset management, especially concerning renewable and non-renewable energy infrastructure. With an impressive track record of leading large-scale energy projects and technology-rich initiatives, Mohammad offers invaluable support to asset operators, developers, and maintainers Dr. Amit Kumar is working as an Assistant Professor in the department of Applied Sciences, Symbiosis Institute of Technology, Pune campus, Symbiosis International (Deemed University), Maharashtra, India. He has done his Bachelors and Master’s form Chaudhary Charan Singh University, Meerut, India, in 2006 and 2009 respectively. In 2016, he received his Doctorate in Applied mathematics (Reliability theory). He published 65+ research papers in various esteemed (Scopus, WOS etc.) international journals. He is a reviewer/editorial board member in many international journals. His fields of research are, Reliability theory, Fuzzy Reliability, Operations Research, and System Engineering.

    Innehållsförteckning

    • 1. Quantitative Advances in Reliability Science: Exploring Soft Computing and AI Applications2. Comparison of statistical and AI techniques for preventive maintenance optimization based on Bayesian network reliability3. Assessing the Impact of Climate Change on RAMS in Critical Infrastructure Systems4. Resilience Engineering: Enhancing Asset Integrity through System Dynamics and Risk Analysis5. Sensitivity and reliability analysis of a complex real-time system6. Assessment of direct and indirect risk on structures based on probabilistic methods under the effect of one or more critical events7. Finding a Reliable Path for Search and Rescue Problems at Sea, an Application of Water Propagation Modelling8. Reliability of a Complex Sales Network Using Path Tracing Method9. The Future of Reliability and Safety Science in the Era of Artificial Intelligence10. Explainable Artificial Intelligence (XAI) applications in condition monitoring of industrial equipment
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