Artificial Intelligence and Machine Learning for EDGE Computing

Dr. Rajiv Pandey is a Faculty member at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India. He possesses a diverse background experience of around 35 years to include 15 years in industry and 20 years of academic research and instruction. His research interests include blockchain and crypto currencies, information security, semantic web provenance, Cloud computing, Big Data, and Data Analytics. Dr. Pandey is a Senior Member of IEEE and has been a session chair and technical committee member for various IEEE conferences. He has been on the technical committees of various government and private universities, and is the editor of Quantum Computing: A Shift from Bits to Qubits from Springer, Data Modelling and Analytics for the Internet of Medical Things from CRC Press/Taylor & Francis, and Artificial Intelligence and Machine Learning for Edge Computing from AP/Elsevier.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 of Computer Science at INPT-ENSEEIHT and member of the ACADIE team at IRIT. Before joining INPT, 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 Ph.D. 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.” Dr. Singh is the author/editor of Quantum Computing: A Shift from Bits to Qubits and Using Event-B for Critical Device Software Systems from Springer, Essential Computer Science: A Programmer’s Guide to Foundational Concepts and Industrial System Engineering for Drones from APress, and System on Chip Interfaces for Low Power Design from Morgan Kaufmann/Elsevier. 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).

• Part 1: AI and Machine Learning1. Artificial Intelligence2. Machine Learning3. Regression Analysis4. Bayesian Statistics5. Learning Theory6. Supervised Learning7. Unsupervised Learning8. Reinforcement Learning9. Instance Based Learning and Feature Engineering Part 2: Data Science and Predictive Analysis10. Introduction to Data Science and Analysis 11. Linear Algebra, Statistics, Probability, Hypothesis and Inference, Gradient Descent 12. Predictive AnalysisPart 3: Edge Computing13. Distributed Computing - Cloud to fog to Edge 14. Edge Computing15. Integrating AI with Edge Computing 16. Machine learning integration with Edge Computing 17. Applying AI/Ml at the edge