Artificial Intelligence and Machine Learning for EDGE Computing
eBook - ePub

Artificial Intelligence and Machine Learning for EDGE Computing

  1. 516 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Artificial Intelligence and Machine Learning for EDGE Computing

About this book

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

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Yes, you can access Artificial Intelligence and Machine Learning for EDGE Computing by Rajiv Pandey,Sunil Kumar Khatri,Neeraj Kumar Singh,Parul Verma in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Biotechnology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Front Matter
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Preface
  7. List of Illustrations
  8. List of Tables
  9. Chapter 1 : Supervised learning
  10. Chapter 2 : Supervised learning: From theory to applications
  11. Chapter 3 : Unsupervised learning
  12. Chapter 4 : Regression analysis
  13. Chapter 5 : The integrity of machine learning algorithms against software defect prediction
  14. Chapter 6 : Learning in sequential decision-making under uncertainty
  15. Chapter 7 : Geospatial crime analysis and forecasting with machine learning techniques
  16. Chapter 8 : Trust discovery and information retrieval using artificial intelligence tools from multiple conflicting sources of web cloud computing and e-commerce users
  17. Chapter 9 : Reliable diabetes mellitus forecasting using artificial neural network multilayer perceptron
  18. Chapter 10 : A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals
  19. Chapter 11 : Integrating AI in e-procurement of hospitality industry in the UAE
  20. Chapter 12 : Application of artificial intelligence and machine learning in blockchain technology
  21. Chapter 13 : Implementing convolutional neural network model for prediction in medical imaging
  22. Chapter 14 : Fuzzy-machine learning models for the prediction of fire outbreaks: A comparative analysis
  23. Chapter 15 : Vehicle telematics: An Internet of Things and Big Data approach
  24. Chapter 16 : Evaluate learner level assessment in intelligent e-learning systems using probabilistic network model
  25. Chapter 17 : Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images
  26. Chapter 18 : Chronological text similarity with pretrained embedding and edit distance
  27. Chapter 19 : Neural hybrid recommendation based on GMF and hybrid MLP
  28. Chapter 20 : A real-time performance monitoring model for processing of IoT and big data using machine learning
  29. Chapter 21 : COVID-19 prediction from chest X-ray images using deep convolutional neural network
  30. Chapter 22 : Hybrid deep learning neuro-fuzzy networks for industrial parameters estimation
  31. Chapter 23 : An intelligent framework to assess core competency using the level prediction model (LPM)
  32. Chapter 24 : Edge computing: A soul to Internet of things (IoT) data
  33. Chapter 25 : 5G: The next-generation technology for edge communication
  34. Chapter 26 : Challenges and opportunities in edge computing architecture using machine learning approaches
  35. Chapter 27 : State of the art for edge security in software-defined networks
  36. Chapter 28 : Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction
  37. Chapter 29 : A comparative study on IoT-aided smart grids using blockchain platform
  38. Chapter 30 : AI cardiologist at the edge: A use case of a dew computing heart monitoring solution
  39. Index
  40. A