Academic Press Artificial Intelligence and Machine Learning for EDGE Computing

Product Description Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms.Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Review Examines the methods and applications of Artificial Intelligence and Machine Learning as applied to Edge computing About the Author Dr. Rajiv Pandey is a Senior Member of IEEE and a Faculty member at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus India. He possesses a diverse background experience of over 35 years, including 15 years of industry experience and 20 years of academic experience. His research interests include Blockchain and crypto currencies, Information Security, Semantic Web Provenance, Cloud, Big Data, and Data Analytics. He has published more than 40 research papers indexed in Scopus and he has been a Session Chair and Technical Committee member for various IEEE and Elsevier conferences. He has been on technical committees of various government and private universities. He is intellectually involved in supervising Doctorate Research Scholars and Post graduate Students. He is also an active contributor in professional bodies such as IEEE, IET and Lucknow Management Association. He is also a member of Machine Intelligence Research Labs. He has contributed book chapters to Handbook of Research on Big Data Storage and Visualization and Exploring Enterprise Service Bus in the ServiceOriented Architecture Paradigm, both by IGI Global and is currently co-editor of a forthcoming book from Springer titled Semantic IOT: Theory and Applications - Interoperability, Provenance and Beyond, part of the Springer book series Studies in Computational Intelligence.Dr. Sunil Kumar Khatri is a Professor at Amity University Tashkent, Uzbekistan, and has been conferred with an Honorary Visiting Professorship by the University of Technology, Sydney, Australia. He is a Fellow of IETE, Senior Life Member of CSI, IEEE, IASCSIT, and Member of IAENG. Dr. Khatri is Editor of International Journal of Systems Assurance, Engineering and Management, Springer Verlag, and he is on the Editorial Board of several international journals. He has published ten guest edited special issues of international journals, and eleven patents filed. His areas of research are Artificial Intelligence, Software Reliability and Testing, and Data Analytics. He is the co-Edtior of Strategic System Assurance and Business Analytics, forthcoming in 2020 from Springer, and co-Author of A Sum-of-Product Based Multiplication Approach for FIR Filters and DFT from Lambert Academic Publishing.Dr. Neeraj Kumar Singh is an Associate Professor in Computer Science at Ecole Nationale Superieure d’Electrotechnique, d’Electronique, d’Informatique, d’Hydraulique, et des Telecommunications, Toulouse, France and member of the ACADIE team at Institute de Recherche Informatique de Toulouse. Before joining ENSEEIHT, Dr. Singh worked as a research fellow and team leader at the Centre for Software Certification (McSCert), McMaster University, Canada. He worked as a research associate in the Department of Computer Science at University of York,