
A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems
Description
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This book aims at offering a unique collection of ideas and experiences mainly focusing on the main streams and merger of Artificial Intelligence (AI) and the Internet of Things (IoT) for a wide slice of the communication and networking community. In the era when the world is grappling with many unforeseen challenges, scientists and researchers are envisioning smart cyber systems that guarantee sustainable development for a better human life. The main contributors that destined to play a huge role in developing such systems, among others, are AI and IoT. While AI provides intelligence to machines and data by identifying patterns, developing predictions, and detecting anomalies, IoT performs as a nerve system by connecting a huge number of machines and capturing an enormous amount of data. AI-enabled IoT, therefore, redefines the way industries, businesses, and economies function with increased automation and efficiency and reduced human interaction and costs.
This book is an attempt to publish innovative ideas, emerging trends, implementation experience, and use-cases pertaining to the merger of AI and IoT. The primary market of this book is centered around students, researchers, academicians, industrialists, entrepreneurs, and professionals working in electrical/computer engineering, IT, telecom/electronic engineering, and related fields. The secondary market of this book is related to individuals working in the fields such as finance, management, mathematics, physics, environment, mechatronics, and the automation industry.
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Content
IoT for better Mobile Health Applications.- Energy Efficient hybrid IoT System for ambient Living: Smart Geyser.- Analysis of Agriculture Production and Impacts of Climate Change in South Asian Region: A Concern related with Healthcare 4.0 Using ML and Sensors.- Block Chain Application in Automobile Registration: A novel Approach for Sustainable Smart Cities with Industry 4.0.- Nonparametric Test for Change-Point Detection of IoT Time-Series Data.
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