Industry 4.0 is used interchangeably with the fourth industrial revolution and represents a new stage in the organization asnd control of the industrial value chain. Cyber-physical systems form the basis of industry 4.0 (e.g., 'smart machines'). They use modern control systems, have embedded software systems, can be addressed via IoT (the Internet of Things), and may use extensive data analytics and/or articifical inteligence systems to operate autonomously. The aim of this book is to provide detailed insights into the state of art techniques in AI, IoT, Blockchain technology and associated technologies which play a vital role in the implementation of a successful project for upcoming and practicing engineers. Owing to its multidisciplinary nature, Industry 4.0 is not a single topic but a combination of a multitude of technologies from different domains. Keeping this in mind the book includes the following topics: Artificial intelligence Internet of things Blockchain technology Digital manufacturing Robotics Cybersecurity The book will be a comprehensive guide to academicians and engineers who want to align with recent trends of fourth industrial revolution.

Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.