Overview
- Speci?es the importance of e?cient theoretical methods in dealing with problems in the context of interdependent networks
- Provides a comprehensive investigation of recently developed algorithms for largescale networks
- Presents basics and mathematical foundations needed to analyze and address the interdependent complex networks
Part of the book series: Advances in Intelligent Systems and Computing (AISC, volume 1123)
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Table of contents (12 chapters)
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Front Matter
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Theoretical Algorithms for Optimization, Learning, and Data Analytics in Interdependent Complex Networks
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Front Matter
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Application of Optimization, Learning and Control in Interdependent Complex Networks
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Front Matter
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Back Matter
Keywords
About this book
This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks.
• Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks• Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks
• Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks
M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.
Editors and Affiliations
About the editor
M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011. His research interests include distributed machine learning and optimization algorithms, distributed intelligence, sensor networks, interdependent networks, and cyberphysical resilience. Application domains include energy systems, healthcare, device-free human sensing, and transportation networks.
Prof. Amini is a life member of IEEE-Eta Kappa Nu (IEEE-HKN), the honor society of IEEE. He organized a panel on distributed learning and novel artificial intelligence algorithms, and their application to healthcare, robotics, energy cybersecurity, distributed sensing, and policy issues in 2019 workshop on artificial intelligence at FIU. He also served as President of Carnegie Mellon University Energy Science and Innovation Club; as technical program committee of several IEEE and ACM conferences; and as the lead editor for a book series on ‘‘Sustainable Interdependent Networks’’ since 2017. He has published more than 80 refereed journal and conference papers, and book chapters. He has co-authored two books, and edited three books on various aspects of optimization and machine learning for interdependent networks. He is the recipient of the best paper award of “IEEE Conference on Computational Science & Computational Intelligence” in 2019, best reviewer award from four IEEE Transactions, the best journal paper award in “Journal of Modern Power Systems and Clean Energy”, and the dean’s honorary award from the President of Sharif University of Technology.Bibliographic Information
Book Title: Optimization, Learning, and Control for Interdependent Complex Networks
Editors: M. Hadi Amini
Series Title: Advances in Intelligent Systems and Computing
DOI: https://doi.org/10.1007/978-3-030-34094-0
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-34093-3Published: 23 February 2020
Softcover ISBN: 978-3-030-34096-4Published: 23 February 2021
eBook ISBN: 978-3-030-34094-0Published: 22 February 2020
Series ISSN: 2194-5357
Series E-ISSN: 2194-5365
Edition Number: 1
Number of Pages: X, 304
Number of Illustrations: 23 b/w illustrations, 67 illustrations in colour
Topics: Communications Engineering, Networks, Power Electronics, Electrical Machines and Networks, Computational Intelligence, Information Systems Applications (incl. Internet)