Artificial Intelligence and Machine Learning for EDGE Computing

Artificial Intelligence and Machine Learning for EDGE Computing

1st Edition - April 26, 2022

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  • Editors: Rajiv Pandey, Sunil Khatri, Neeraj Singh, Parul Verma
  • eBook ISBN: 9780128240557
  • Paperback ISBN: 9780128240540

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Description

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.

Key Features

  • 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

Readership

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

Table of Contents

  • Cover
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Preface
  • Part I: AI and machine learning
  • Chapter 1: Supervised learning
  • Abstract
  • 1: Introduction
  • 2: Perceptron
  • 3: Linear regression
  • 4: Logistic regression
  • 5: Multilayer perceptron
  • 6: KL divergence
  • 7: Generalized linear models
  • 8: Kernel method
  • 9: Nonlinear SVM classifier
  • 10: Tree ensembles
  • References
  • Chapter 2: Supervised learning: From theory to applications
  • Abstract
  • 1: Introduction
  • 2: What are regression and classification problems?
  • 3: Learning algorithms
  • 4: Evaluation metrics
  • 5: Supervised learning to detect fraudulent credit card transactions
  • 6: Supervised learning for hand writing recognition
  • 7: Conclusion
  • References
  • Chapter 3: Unsupervised learning
  • Abstract
  • 1: Introduction
  • 2: k-means clustering
  • 3: k-means++ clustering
  • 4: Sequential leader clustering
  • 5: EM algorithm
  • 6: Gaussian mixture model
  • 7: Autoencoders
  • 8: Principal component analysis
  • 9: Linear discriminant analysis
  • 10: Independent component analysis
  • References
  • Chapter 4: Regression analysis
  • Abstract
  • 1: Introduction
  • 2: Linear regression
  • 3: Cost functions
  • 4: Gradient descent
  • 5: Polynomial regression
  • 6: Regularization
  • 7: Evaluating a machine learning model
  • References
  • Chapter 5: The integrity of machine learning algorithms against software defect prediction
  • Abstract
  • 1: Introduction
  • 2: Related works
  • 3: Proposed method
  • 4: Experiment
  • 5: Results
  • 6: Threats to validity
  • 7: Conclusions
  • References
  • Chapter 6: Learning in sequential decision-making under uncertainty
  • Abstract
  • Acknowledgments
  • 1: Introduction
  • 2: Multiarmed bandit problem
  • 3: Markov decision process planning problem
  • 4: Reinforcement learning
  • 5: Summary
  • References
  • Chapter 7: Geospatial crime analysis and forecasting with machine learning techniques
  • Abstract
  • 1: Introduction
  • 2: Related work
  • 3: Methodology
  • 4: Results and discussion
  • 5: Conclusions
  • References
  • Chapter 8: Trust discovery and information retrieval using artificial intelligence tools from multiple conflicting sources of web cloud computing and e-commerce users
  • Abstract
  • Acknowledgments
  • 1: Introduction
  • 2: Trusted computing
  • 3: Problem identification
  • 4: Truth content discovery algorithm
  • 5: Trustworthy and scalable service providers algorithm
  • 6: Efficient feature extraction and classification (EFEC) algorithm
  • 7: QUERY retrieval time (QRT)
  • 8: Trust content discovery and trustworthy and scalable service providers algorithm
  • 9: Efficient feature xtraction and classification (EFEC) algorithm and customer review datasets
  • 10: Summary
  • 11: Conclusions
  • 12: Future enhancements
  • References
  • Chapter 9: Reliable diabetes mellitus forecasting using artificial neural network multilayer perceptron
  • Abstract
  • 1: Introduction
  • 2: Related works
  • 3: Methodology
  • 4: Building the diabetic diagnostic criteria
  • 5: Evaluating the diabetes outcomes using classification algorithms
  • 6: Conclusions
  • References
  • Chapter 10: A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals
  • Abstract
  • 1: Introduction
  • 2: Methods
  • 3: Results
  • 4: Discussion
  • 5: Conclusions
  • References
  • Chapter 11: Integrating AI in e-procurement of hospitality industry in the UAE
  • Abstract
  • 1: Introduction
  • 2: Problem statement
  • 3: Authors’ contributions
  • 4: Significance of the study
  • 5: Theoretical framework
  • 6: Research aims and objectives
  • 7: Literature review
  • 8: Major findings
  • 9: Discussions
  • 10: Major gaps in the study
  • 11: Conclusions
  • References
  • Chapter 12: Application of artificial intelligence and machine learning in blockchain technology
  • Abstract
  • Acknowledgment
  • 1: Introduction
  • 2: Applications of artificial intelligence, machine learning, and blockchain technology
  • 3: It takes two to tango: Future of artificial intelligence and machine learning in blockchain technology
  • 4: Edge computing: A potential use case of blockchain
  • 5: Conclusions
  • References
  • Part II: Data science and predictive analysis
  • Chapter 13: Implementing convolutional neural network model for prediction in medical imaging
  • Abstract
  • 1: Introduction
  • 2: Convolutional neural networks
  • 3: Implementing CNN for biomedical imaging and analysis
  • 4: Architecture models for different image type
  • 5: Conclusion
  • 6: Future scope
  • References
  • Chapter 14: Fuzzy-machine learning models for the prediction of fire outbreaks: A comparative analysis
  • Abstract
  • 1: Introduction
  • 2: Related literature
  • 3: Research methodology
  • 4: Machine learning algorithms for fire outbreak prediction
  • 5: Result and discussion
  • 6: Conclusions
  • References
  • Chapter 15: Vehicle telematics: An Internet of Things and Big Data approach
  • Abstract
  • 1: Introduction
  • 2: Big Data
  • 3: Big Data with cloud computing
  • 4: Internet of Things (IoT)
  • 5: Vehicle telematics
  • 6: Case study—Vehicle reaction time prediction
  • 7: Conclusions
  • References
  • Chapter 16: Evaluate learner level assessment in intelligent e-learning systems using probabilistic network model
  • Abstract
  • 1: Introduction
  • 2: Related work
  • 3: Contribution of intelligent e-learning system using BN model
  • 4: Learner assessment model
  • 5: Results and discussions
  • 6: Conclusions and future work
  • References
  • Chapter 17: Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images
  • Abstract
  • 1: Introduction
  • 2: Literature review
  • 3: Proposed methodology
  • 4: Result analysis
  • 5: Discussion and conclusions
  • References
  • Chapter 18: Chronological text similarity with pretrained embedding and edit distance
  • Abstract
  • 1: Introduction
  • 2: Literature review
  • 3: Theoretical background
  • 4: Modeling
  • 5: Experimental settings
  • 6: Results and discussion
  • 7: Conclusions
  • References
  • Chapter 19: Neural hybrid recommendation based on GMF and hybrid MLP
  • Abstract
  • 1: Introduction
  • 2: Theoretical background and related works
  • 3: Neural hybrid recommendation (NHybF)
  • 4: Experiments
  • 5: Conclusions
  • References
  • Chapter 20: A real-time performance monitoring model for processing of IoT and big data using machine learning
  • Abstract
  • 1: Introduction
  • 2: Experimental study
  • 3: Major findings
  • 4: Conclusions
  • References
  • Chapter 21: COVID-19 prediction from chest X-ray images using deep convolutional neural network
  • Abstract
  • 1: Introduction
  • 2: Methodology
  • 3: Results and discussions
  • 4: Conclusions
  • References
  • Further reading
  • Chapter 22: Hybrid deep learning neuro-fuzzy networks for industrial parameters estimation
  • Abstract
  • 1: Introduction
  • 2: Preliminaries
  • 3: Methodology
  • 4: Results and discussion
  • 5: Validation of model
  • 6: Discussions on performance evaluation
  • 7: Conclusions
  • 8: Future scope
  • References
  • Chapter 23: An intelligent framework to assess core competency using the level prediction model (LPM)
  • Abstract
  • 1: Introduction
  • 2: Related work
  • 3: Existing applications
  • 4: Proposed system
  • 5: Experimental
  • 6: Conclusions
  • References
  • Part III: Edge computing
  • Chapter 24: Edge computing: A soul to Internet of things (IoT) data
  • Abstract
  • 1: Introduction
  • 2: Edge computing characteristics
  • 3: New challenges in Internet of technology (IoT): Edge computing
  • 4: Edge computing support to IoT functionality
  • 5: IoT applications: Cloud or edge computing?
  • 6: Benefits and potential of edge computing for IoT
  • 7: Use case: Edge computing in IoT
  • 8: Pertinent open issues which require additional investigations for edge computing
  • 9: Conclusions
  • References
  • Chapter 25: 5G: The next-generation technology for edge communication
  • Abstract
  • 1: Introduction
  • 2: History
  • 3: 5G technology
  • 4: 5G cellular network
  • 5: Components used in 5G technology/network
  • 6: Differences from 4G architecture
  • 7: Security of 5G architecture
  • 8: 5G time period
  • 9: Case study on 5G technology
  • 10: 5G advancement
  • 11: Advantage and disadvantage of 5G technology
  • 12: Challenges
  • 13: Future scope
  • 14: Conclusions
  • References
  • Chapter 26: Challenges and opportunities in edge computing architecture using machine learning approaches
  • Abstract
  • 1: Introduction
  • 2: Overview of edge computing
  • 3: Security and privacy in edge computing
  • 4: Intersection of machine learning and edge using enabling technologies
  • 5: Machine learning and edge bringing AI to IoT
  • 6: OpenVINO toolkit
  • 7: Challenges in machine learning and edge computing integration
  • 8: Conclusions
  • References
  • Chapter 27: State of the art for edge security in software-defined networks
  • Abstract
  • 1: Introduction
  • 2: Hybrid software-defined networks
  • 3: Security challenges in hybrid software-defined networks
  • 4: Solutions for hybrid software-defined networks
  • 5: Learning techniques for hybrid software-defined networks
  • 6: Discussion and implementation
  • 7: Conclusions
  • References
  • Further reading
  • Chapter 28: Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction
  • Abstract
  • 1: Introduction
  • 2: Features and differences between cloud, fog, and edge computing
  • 3: Framework and programming models: Architecture of fog computing
  • 4: Moving cloud to edge computing
  • 5: Case study: Edge computing for intelligent aquaculture
  • 6: Conclusions
  • References
  • Chapter 29: A comparative study on IoT-aided smart grids using blockchain platform
  • Abstract
  • 1: Introduction to smart grid, IoT role, and challenges of smart grid implementations
  • 2: Secure smart grid using blockchain technology
  • 3: Conclusions
  • References
  • Chapter 30: AI cardiologist at the edge: A use case of a dew computing heart monitoring solution
  • Abstract
  • 1: Introduction
  • 2: Related work
  • 3: Architectural approach
  • 4: ECGalert use case
  • 5: Discussion
  • 6: Conclusions
  • References
  • Index

Product details

  • No. of pages: 516
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: April 26, 2022
  • Imprint: Academic Press
  • eBook ISBN: 9780128240557
  • Paperback ISBN: 9780128240540

About the Editors

Rajiv Pandey

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, including 15 years of industry experience and 20 years of academic experience. His research interests include Blockchain and crypto currencies, Information Security, Semantic Web Provenance, Cloud, Big Data, and Data Analytics. He has published more than 40 research papers indexed in Scopus and he has been a Session Chair and Technical Committee member for various IEEE and Elsevier conferences. He has been on technical committees of various government and private universities. He is intellectually involved in supervising Doctorate Research Scholars and Post graduate Students. He is also an active contributor in professional bodies such as IEEE, IET and Lucknow Management Association. He is also a member of Machine Intelligence Research Labs. He has contributed book chapters to Handbook of Research on Big Data Storage and Visualization and Exploring Enterprise Service Bus in the ServiceOriented Architecture Paradigm, both by IGI Global and is currently co-editor of a forthcoming book from Springer titled Semantic IOT: Theory and Applications - Interoperability, Provenance and Beyond, part of the Springer book series Studies in Computational Intelligence.

Affiliations and Expertise

Faculty, Amity University, Lucknow, India

Sunil Khatri

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.

Affiliations and Expertise

Researcher, Artificial Intelligence, Cryptography, Data Warehouse, Data Mining, Network Security

Neeraj Singh

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.

Affiliations and Expertise

Associate Professor, Computer Science, ENSEEIHT, Toulouse, France

Parul Verma

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).

Affiliations and Expertise

Faculty, Amity University, Lucknow, India

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