Artificial Intelligence for Cloud and Edge Computing
- Length: 364 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2022-01-14
- ISBN-10: 3030808203
- ISBN-13: 9783030808204
- Sales Rank: #0 (See Top 100 Books)
This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.
Cover Front Matter An Optimization View to the Design of Edge Computing Infrastructures for IoT Applications AIOps: A Multivocal Literature Review Deep Learning-Based Facial Recognition on Hybrid Architecture for Financial Services Classification of Swine Disease Using K-Nearest Neighbor Algorithm on Cloud-Based Framework Privacy and Trust Models for Cloud-Based EHRs Using Multilevel Cryptography and Artificial Intelligence Utilizing an Agent-Based Auction Protocol for Resource Allocation and Load Balancing in Grid Computing Optimization Model of Smartphone and Smart Watch Based on Multi Level of Elitism (OMSPW-MLE) K-Nearest Neighbour Algorithm for Classification of IoT-Based Edge Computing Device Big Data Analytics of IoT-Based Cloud System Framework: Smart Healthcare Monitoring Systems Genetic Algorithm-Based Pseudo Random Number Generation for Cloud Security Anomaly Detection in IoT Using Machine Learning System Level Knowledge Representation for Edge Intelligence AI-Based Enhanced Time Cost-Effective Cloud Workflow Scheduling AI-JasCon: An Artificial Intelligent Containerization System for Bayesian Fraud Determination in Complex Networks Performance Improvement of Intrusion Detection System for Detecting Attacks on Internet of Things and Edge of Things Back Matter
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