Artificial Intelligence and Machine Learning for EDGE Computing

by Rajiv Pandey (Editor), Sunil Kumar Khatri (Editor), Neeraj Kumar Singh (Editor), Parul Verma (Editor)

Overview

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.

• Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing
• Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers
• Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints

Product Details

ISBN-13:	9780128240557
Publisher:	Elsevier Science
Publication date:	04/26/2022
Sold by:	Barnes & Noble
Format:	eBook
Pages:	516
File size:	100 MB
Note:	This product may take a few minutes to download.

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, comprising 15 years of industry experience and 20 years of academic experience.
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, UK. He also worked as a research scientist at the INRIA Nancy Grand Est Centre, France, where he has received his PhD in computer science. He leads his research in the area of theory and practice of rigorous software engineering and formal methods to design and implement safe, secure and dependable critical systems. He is an active participant in the “Pacemaker Grand Challenge.” He is the author of Using Event-B for Critical Device Software Systems, published by Springer. He has been involved in many scientific activities, such as PC chair, PC member, and external referee for journals and ANR projects.

He is also involved in several research projects on formal methods and system engineering as project leader and as scientific coordinator.
Dr. Parul Verma is working as a Faculty member at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow, India. Her research interests are Natural Language Processing, Web Mining, Deep Mining, Semantic Web, Edge Computing and IoT. She has published and presented almost 30 papers in Scopus and other indexed National and International Journals and Conferences. She has been actively involved in research being as a supervisor to Research Scholars and Post Graduate students. She is also a member of many International and National bodies like ACM (Association for Computing Machinery), IAENG (International Association of Engineers), IACSIT (International Association of Computer Science and Information Technology), Internet Society and CSI (Computer Society of India).

Table of Contents

Part 1: AI and Machine Learning 1. Artificial Intelligence 2. Machine Learning 3. Regression Analysis 4. Bayesian Statistics 5. Learning Theory 6. Supervised Learning 7. Unsupervised Learning 8. Reinforcement Learning 9. Instance Based Learning and Feature Engineering

Part 2: Data Science and Predictive Analysis 10. Introduction to Data Science and Analysis 11. Linear Algebra, Statistics, Probability, Hypothesis and Inference, Gradient Descent 12. Predictive Analysis

Part 3: Edge Computing 13. Distributed Computing - Cloud to fog to Edge 14. Edge Computing 15. Integrating AI with Edge Computing 16. Machine learning integration with Edge Computing 17. Applying AI/Ml at the edge

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From the Publisher

Examines the methods and applications of Artificial Intelligence and Machine Learning as applied to Edge computing