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.
1. Artificial Intelligence and Machine Learning for EDGE Computing - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. Preface - p. Page vii 8. List of Illustrations - p. Page xii 9. List of Tables - p. Page xiii 10. Chapter 1 : Supervised learning - p. Page xiii 11. Chapter 2 : Supervised learning: From theory to applications - p. Page 19 12. Chapter 3 : Unsupervised learning - p. Page 32 13. Chapter 4 : Regression analysis - p. Page 51 14. Chapter 5 : The integrity of machine learning algorithms against software defect prediction - p. Page 62 15. Chapter 6 : Learning in sequential decision-making under uncertainty - p. Page 68 16. Chapter 7 : Geospatial crime analysis and forecasting with machine learning techniques - p. Page 85 17. Chapter 8 : Trust discovery and information retrieval using artificial intelligence tools from multiple conflicting sources of web cloud computing and e-commerce users - p. Page 97 18. Chapter 9 : Reliable diabetes mellitus forecasting using artificial neural network multilayer perceptron - p. Page 118 19. Chapter 10 : A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals - p. Page 131 20. Chapter 11 : Integrating AI in e-procurement of hospitality industry in the UAE - p. Page 142 21. Chapter 12 : Application of artificial intelligence and machine learning in blockchain technology - p. Page 165 22. Chapter 13 : Implementing convolutional neural network model for prediction in medical imaging - p. Page 184 23. Chapter 14 : Fuzzy-machine learning models for the prediction of fire outbreaks: A comparative analysis - p. Page 205 24. Chapter 15 : Vehicle telematics: An Internet of Things and Big Data approach - p. Page 231 25. Chapter 16 : Evaluate learner level assessment in intelligent e-learning systems using probabilistic network model - p. Page 252 26. Chapter 17 : Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images - p. Page 265 27. Chapter 18 : Chronological text similarity with pretrained embedding and edit distance - p. Page 273 28. Chapter 19 : Neural hybrid recommendation based on GMF and hybrid MLP - p. Page 284 29. Chapter 20 : A real-time performance monitoring model for processing of IoT and big data using machine learning - p. Page 293 30. Chapter 21 : COVID-19 prediction from chest X-ray images using deep convolutional neural network - p. Page 293 31. Chapter 22 : Hybrid deep learning neuro-fuzzy networks for industrial parameters estimation - p. Page 323 32. Chapter 23 : An intelligent framework to assess core competency using the level prediction model (LPM) - p. Page 340 33. Chapter 24 : Edge computing: A soul to Internet of things (IoT) data - p. Page 351 34. Chapter 25 : 5G: The next-generation technology for edge communication - p. Page 371 35. Chapter 26 : Challenges and opportunities in edge computing architecture using machine learning approaches - p. Page 389 36. Chapter 27 : State of the art for edge security in software-defined networks - p. Page 407 37. Chapter 28 : Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction - p. Page 422 38. Chapter 29 : A comparative study on IoT-aided smart grids using blockchain platform - p. Page 441 39. Chapter 30 : AI cardiologist at the edge: A use case of a dew computing heart monitoring solution - p. Page 464 40. Index - p. Page 476
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.
1. Artificial Intelligence and Machine Learning for EDGE Computing - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. Preface - p. Page vii 8. List of Illustrations - p. Page xii 9. List of Tables - p. Page xiii 10. Chapter 1 : Supervised learning - p. Page xiii 11. Chapter 2 : Supervised learning: From theory to applications - p. Page 19 12. Chapter 3 : Unsupervised learning - p. Page 32 13. Chapter 4 : Regression analysis - p. Page 51 14. Chapter 5 : The integrity of machine learning algorithms against software defect prediction - p. Page 62 15. Chapter 6 : Learning in sequential decision-making under uncertainty - p. Page 68 16. Chapter 7 : Geospatial crime analysis and forecasting with machine learning techniques - p. Page 85 17. Chapter 8 : Trust discovery and information retrieval using artificial intelligence tools from multiple conflicting sources of web cloud computing and e-commerce users - p. Page 97 18. Chapter 9 : Reliable diabetes mellitus forecasting using artificial neural network multilayer perceptron - p. Page 118 19. Chapter 10 : A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals - p. Page 131 20. Chapter 11 : Integrating AI in e-procurement of hospitality industry in the UAE - p. Page 142 21. Chapter 12 : Application of artificial intelligence and machine learning in blockchain technology - p. Page 165 22. Chapter 13 : Implementing convolutional neural network model for prediction in medical imaging - p. Page 184 23. Chapter 14 : Fuzzy-machine learning models for the prediction of fire outbreaks: A comparative analysis - p. Page 205 24. Chapter 15 : Vehicle telematics: An Internet of Things and Big Data approach - p. Page 231 25. Chapter 16 : Evaluate learner level assessment in intelligent e-learning systems using probabilistic network model - p. Page 252 26. Chapter 17 : Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images - p. Page 265 27. Chapter 18 : Chronological text similarity with pretrained embedding and edit distance - p. Page 273 28. Chapter 19 : Neural hybrid recommendation based on GMF and hybrid MLP - p. Page 284 29. Chapter 20 : A real-time performance monitoring model for processing of IoT and big data using machine learning - p. Page 293 30. Chapter 21 : COVID-19 prediction from chest X-ray images using deep convolutional neural network - p. Page 293 31. Chapter 22 : Hybrid deep learning neuro-fuzzy networks for industrial parameters estimation - p. Page 323 32. Chapter 23 : An intelligent framework to assess core competency using the level prediction model (LPM) - p. Page 340 33. Chapter 24 : Edge computing: A soul to Internet of things (IoT) data - p. Page 351 34. Chapter 25 : 5G: The next-generation technology for edge communication - p. Page 371 35. Chapter 26 : Challenges and opportunities in edge computing architecture using machine learning approaches - p. Page 389 36. Chapter 27 : State of the art for edge security in software-defined networks - p. Page 407 37. Chapter 28 : Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction - p. Page 422 38. Chapter 29 : A comparative study on IoT-aided smart grids using blockchain platform - p. Page 441 39. Chapter 30 : AI cardiologist at the edge: A use case of a dew computing heart monitoring solution - p. Page 464 40. Index - p. Page 476
