| Description |
1 online resource |
| Note |
Includes index. |
| Summary |
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. |
| Contents |
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 Internet of Things and Big Data approach -- 16. Evaluate learner level assessment in intelligent e-learning systems using probabilistic network model -- 17. Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images -- 18. Chronological text similarity with pretrained embedding and edit distance -- 19. Neural hybrid recommendation based on GMF and hybrid MLP -- 20. A real-time performance monitoring model for processing of IoT and big data using machine learning -- 21. COVID-19 prediction from chest X-ray images using deep convolutional neural network -- 22. Hybrid deep learning neuro-fuzzy networks for industrial parameters estimation -- 23. An intelligent framework to assess core competency using the level prediction model (LPM). -- Part III. Edge computing. 24. Edge computing : a soul to Internet of things (IoT) data -- 25. 5G : the next-generation technology for edge communication -- 26. Challenges and opportunities in edge computing architecture using machine learning approaches -- 27. State of the art for edge security in software-defined networks -- 28. Moving to the cloud, fog, and edge computing paradigms : convergences and future research direction -- 29. A comparative study on IoT-aided smart grids using blockchain platform -- 30. AI cardiologist at the edge : a use case of a dew computing heart monitoring solution. |
| Subject |
Artificial intelligence.
|
|
Machine learning.
|
|
Edge computing.
|
|
Intelligence artificielle.
|
|
Apprentissage automatique.
|
|
artificial intelligence.
|
|
Artificial intelligence
|
|
Edge computing
|
|
Machine learning
|
|
Informàtica a la perifèria.
|
|
Aprenentatge automàtic.
|
|
Intel·ligència artificial.
|
| Added Author |
Pandey, Rajiv.
|
| Other Form: |
Print version: 0128240547 9780128240540 (OCoLC)1273078449 |
| ISBN |
9780128240557 (electronic bk.) |
|
0128240555 (electronic bk.) |
|
9780128240540 |
|
0128240547 |
| Standard No. |
AU@ 000071686817 |
|
