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.
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.
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
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).
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