Predictive Analytics in System Reliability - Rilegato

Rilegato

ISBN 10: 303105346X ISBN 13: 9783031053467

Casa editrice: Springer Nature, 2022

Riassunto

This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering.

The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.

Su questo libro

Informazioni sull?autore

Dr. Vijay Kumar received his M.Sc. in Applied Mathematics and M.Phil. in Mathematics from Indian Institute of Technology (IIT), Roorkee, India in 1998 and 2000, respectively. He has completed his PhD from the Department of Operational Research, University of Delhi. Currently, he is an Associate Professor in the Department of Mathematics, Amity Institute of Applied Sciences, Amity University Uttar Pradesh, Noida, India.  He is the co-authored of one book and has published more than 55 research papers in the areas of software reliability, mathematical modeling and optimization in international journals and conferences of high repute. His current research interests include software reliability growth modelling, optimal control theory and marketing models in the context of innovation diffusion theory. He has reviewed many papers for Soft Computing(Springer), IJRQSE, IJQRM, IJSAEM and other reputed journals. He has edited special issues of IJAMS, CMES (Taylor & Francis), and RIO journal. He is an editorial board member of IJMEMS. He is a life member of Society for Reliability Engineering, Quality and Operations Management (SREQOM).

Dr. Hoang Pham is Distinguished Professor and Former Chairman (2007-2013) of the Department of Industrial and Systems Engineering at Rutgers University, New Jersey. Before joining Rutgers, he was Senior Engineering Specialist with the Boeing Company and the Idaho National Engineering Laboratory. He has been served as Editor-in-Chief, Editor, Associate Editor, Guest Editor, and Board Member of many journals. He is Author or Coauthor of 7 books and has published over 190 journal articles and edited 15 books including Springer Handbook in Engineering Statistics and Handbook in Reliability Engineering. He has delivered over 40 invited keynote and plenary speeches at many international conferences. His numerous awards include the 2009 IEEE Reliability Society Engineer of the Year Award. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Institute of Industrial Engineers (IIE).

 

 

Dalla quarta di copertina

This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering.

The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 303105346X
• ISBN 13: 9783031053467
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 296
• Redattore: Kumar Vijay, Pham Hoang
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania