Artificial Intelligence and Machine Learning for EDGE Computing 1st Edition

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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