Artificial Intelligence and Machine Learning for EDGE Computing
- 12h 18m
- Neeraj Kumar Singh, Parul Verma, Rajiv Pandey, Sunil Kumar Khatri
- Elsevier Science and Technology Books, Inc.
- 2022
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
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).
In this Book
-
Supervised Learning
-
Supervised Learning: From Theory to Applications
-
Unsupervised Learning
-
Regression Analysis
-
The Integrity of Machine Learning Algorithms against Software Defect Prediction
-
Learning in Sequential Decision-Making under Uncertainty
-
Geospatial Crime Analysis and Forecasting with Machine Learning Techniques
-
Trust Discovery and Information Retrieval using Artificial Intelligence Tools from Multiple Conflicting Sources of Web Cloud Computing and E-Commerce Users
-
Reliable Diabetes Mellitus Forecasting using Artificial Neural Network Multilayer Perceptron
-
A Study of Deep Learning Approach for the Classification of Electroencephalogram (EEG) Brain Signals
-
Integrating AI in E-Procurement of Hospitality Industry in the UAE
-
Application of Artificial Intelligence and Machine Learning in Blockchain Technology
-
Implementing Convolutional Neural Network Model for Prediction in Medical Imaging
-
Fuzzy-Machine Learning Models for the Prediction of Fire Outbreaks: A Comparative Analysis
-
Vehicle Telematics: An Internet of Things and Big Data Approach
-
Evaluate Learner Level Assessment in Intelligent E-Learning Systems using Probabilistic Network Model
-
Ensemble Method for Multiclassification of COVID-19 Virus using Spatial and Frequency Domain Features over X-Ray Images
-
Chronological Text Similarity with Pretrained Embedding and Edit Distance
-
Neural Hybrid Recommendation based on GMF and Hybrid MLP
-
A Real-Time Performance Monitoring Model for Processing of IoT and Big Data using Machine Learning
-
COVID-19 Prediction from Chest X-Ray Images using Deep Convolutional Neural Network
-
Hybrid Deep Learning Neuro-Fuzzy Networks for Industrial Parameters Estimation
-
An Intelligent Framework to Assess Core Competency using the Level Prediction Model (LPM)
-
Edge Computing: A Soul to Internet of Things (IoT) Data
-
5G: The Next-Generation Technology for Edge Communication
-
Challenges and Opportunities in Edge Computing Architecture using Machine Learning Approaches
-
State of the Art for Edge Security in Software-Defined Networks
-
Moving to the Cloud, Fog, and Edge Computing Paradigms: Convergences and Future Research Direction
-
A Comparative Study on IoT-Aided Smart Grids using Blockchain Platform
-
AI Cardiologist at the Edge: A Use Case of a Dew Computing Heart Monitoring Solution
