Artificial Intelligence and Machine Learning for EDGE Computing

Artificial Intelligence and Machine Learning for EDGE Computing

by Rajiv Pandey, Sunil Kumar Khatri, Neeraj Kumar Singh, Parul Verma
Released April 2022
Publisher(s): Academic Press
ISBN: 9780128240557

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book 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.

  • 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

Table of contents

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

Product information

  • Title: Artificial Intelligence and Machine Learning for EDGE Computing
  • Author(s): Rajiv Pandey, Sunil Kumar Khatri, Neeraj Kumar Singh, Parul Verma
  • Release date: April 2022
  • Publisher(s): Academic Press
  • ISBN: 9780128240557