Artificial Intelligence and Machine Learning for EDGE Computing 1st Edition – Ebook Instant Download/Delivery ISBN(s): 9780128240540,0128240547,9780128240557, 0128240555
Product details:
- ISBN 10:0128240555
- ISBN 13:9780128240557
- Author: Rajiv Pandey, Sunil Kumar Khatri, Neeraj Kumar Singh, Parul Verma
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.
Table contents:
Chapter 1: Supervised learning
Chapter 2: Supervised learning: From theory to applications
Chapter 3: Unsupervised learning
Chapter 4: Regression analysis
Chapter 5: The integrity of machine learning algorithms against software defect prediction
Chapter 6: Learning in sequential decision-making under uncertainty
Chapter 7: Geospatial crime analysis and forecasting with machine learning techniques
Chapter 8: Trust discovery and information retrieval using artificial intelligence tools from multiple conflicting sources of web cloud computing and e-commerce users
Chapter 9: Reliable diabetes mellitus forecasting using artificial neural network multilayer perceptron
Chapter 10: A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals
Chapter 11: Integrating AI in e-procurement of hospitality industry in the UAE
Chapter 12: Application of artificial intelligence and machine learning in blockchain technology
Chapter 13: Implementing convolutional neural network model for prediction in medical imaging
Chapter 14: Fuzzy-machine learning models for the prediction of fire outbreaks: A comparative analysis
Chapter 15: Vehicle telematics: An Internet of Things and Big Data approach
Chapter 16: Evaluate learner level assessment in intelligent e-learning systems using probabilistic network model
Chapter 17: Ensemble method for multiclassification of COVID-19 virus using spatial and frequency domain features over X-ray images
Chapter 18: Chronological text similarity with pretrained embedding and edit distance
Chapter 19: Neural hybrid recommendation based on GMF and hybrid MLP
Chapter 20: A real-time performance monitoring model for processing of IoT and big data using machine learning
Chapter 21: COVID-19 prediction from chest X-ray images using deep convolutional neural network
Chapter 22: Hybrid deep learning neuro-fuzzy networks for industrial parameters estimation
Chapter 23: An intelligent framework to assess core competency using the level prediction model (LPM)
Chapter 24: Edge computing: A soul to Internet of things (IoT) data
Chapter 25: 5G: The next-generation technology for edge communication
Chapter 26: Challenges and opportunities in edge computing architecture using machine learning approaches
Chapter 27: State of the art for edge security in software-defined networks
Chapter 28: Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction
Chapter 29: A comparative study on IoT-aided smart grids using blockchain platform
Chapter 30: AI cardiologist at the edge: A use case of a dew computing heart monitoring solution
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