Artificial Intelligence and Machine Learning for EDGE Computing

1st Edition - April 26, 2022
Editors: Rajiv Pandey, Sunil Kumar Khatri, Neeraj Kumar Singh, Parul Verma
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 4 0 5 4 - 0
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 4 0 5 5 - 7

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 alo… Read more

Artificial Intelligence and Machine Learning for EDGE Computing

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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.

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

Rajiv Pandey

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.

Affiliations and expertise

Amity Institute of Information Technology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, India

Sunil Kumar 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 Kumar Singh

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.

Affiliations and expertise

INPT-ENSEEIHT/IRIT, 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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Artificial Intelligence and Machine Learning for EDGE Computing

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Rajiv Pandey

Sunil Kumar Khatri

Neeraj Kumar Singh

Parul Verma