Artificial intelligence and machine learning for EDGE computing

"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 page 4.

Part I. AI and machine learning. 1. Supervised learning -- 2. Supervised learning : from theory to applications -- 3. Unsupervised learning -- 4. Regression analysis -- 5. The integrity of machine learning algorithms against software defect prediction -- 6. Learning in sequential decision-making under uncertainty -- 7. Geospatial crime analysis and forecasting with machine learning techniques -- 8. Trust discovery and information retrieval using artificial intelligence tools from multiple conflicting sources of web cloud computing and e-commerce users -- 9. Reliable diabetes mellitus forecasting using artificial neural network multilayer perceptron -- 10. A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals -- 11. Integrating AI in e-procurement of hospitality industry in the UAE -- 12. Application of artificial intelligence and machine learning in blockchain technology. -- Part II. Data science and predictive analysis. 13. Implementing convolutional neural network model for prediction in medical imaging -- 14. Fuzzy-machine learning models for the prediction of fire outbreaks : a comparative analysis -- 15. Vehicle telematics : an I…