Artificial Intelligence and Machine Learning for EDGE Computing

Erscheinungsjahr 2022
ISBN: 978-0-12-824054-0
Verlag: William Andrew Publishing

Buch, Englisch, 516 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

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.

Zielgruppe

<p>Computer scientists and researchers in applied informatics, Artificial Intelligence, data science, Cloud computing, networking, and information technology. </p>

Autoren/Hrsg.

Pandey, Rajiv

Khatri, Sunil Kumar

Verma, Parul

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

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

Über Autor(innen)

Verma, Parul
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).

Khatri, Sunil Kumar
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.

Pandey, Rajiv
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.