Название: Machine Learning and IoT for Intelligent Systems and Smart Applications
Автор: Madhumathy P., M. Vinoth Kumar, R. Umamaheswari
Издательство: CRC Press
Год: 2022
Страниц: 243
Язык: английский
Формат: pdf (true)
Размер: 23.9 MB

The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of Machine Learning and IoT with pertinent applications.　It further discusses challenges and future directions in the Machine Learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects.

The book presents an overview of the different algorithms by focusing on the advantages, disadvantages and applications of each algorithm in the field of Machine learning and IOT. The book provides machine learning (ML) techniques to address both intelligence and configurability to various IoT devices. The book also reports the challenges and the future directions in the IoT and machine learning. This book comes with an energy-efficient cross layer model and energy-related routing metric combination to prolong the lifetime of low power IoT networks. This book deals with Machine Learning which is subset of AI that uses computational statistics to find a mathematical model describing Input and Output Data. Machine Learning techniques have been successfully involved in a various applications including assistance in medical diagnosis and analyzing disease based on clinical and laboratory symptoms with appropriate data to give more efficient result for diagnosing disease.

Though these new skills are prodigious, they result in numerous challenges including resource constraints of IoT devices, poor interoperability, heterogeneity of IoT system and several privacy and security vulnerabilities. They also expose severe IoT security challenges. Further, traditional security approaches against the most prominent attacks are insufficient. Therefore, enabling the IoT devices to learn and adapt to various threats dynamically and addressing them proactively need immediate attention. In this regard, machine learning (ML) techniques are employed to address both intelligence and reconfigurability to various IoT devices.

Features:

- Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications.
- Discusses supervised and unsupervised machine learning for IoT data and devices.
- Presents an overview of the different algorithms related to Machine learning and IoT.
- Covers practical case studies on industrial and smart home automation.
- Includes implementation of AI from case studies in personal and industrial IoT.

This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering.








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