Deep Learning Applications for Cyber-Physical Systems

This book explores advanced innovations in deep learning for cyber-physical systems, focusing on data processing, health informatics, and predictive analytics.

In today's data-driven world, the constant generation of big data from various sectors such as business, engineering, and science poses significant challenges. Deep Learning Applications for Cyber-Physical Systems addresses these challenges by presenting cutting-edge innovations and research focused on methodological and algorithmic solutions to data processing problems. The book emphasizes the importance of extracting meaningful insights from vast amounts of data generated by the Internet of Things (IoT) and cyber-physical systems (CPS). It explores how these insights, often termed 'smart data,' can be utilized to enhance decision-making across various applications, including business and healthcare.

The text delves into the evolving trends in health informatics and computer-aided diagnosis, showcasing deep learning techniques that are reshaping these fields. With a focus on smart medical systems, intrusion detection systems, and predictive analytics, Deep Learning Applications for Cyber-Physical Systems provides a comprehensive overview of the intersection between deep learning and CPS. This makes it an essential resource for those looking to understand the practical applications of these technologies in real-world scenarios.

Targeted at a diverse audience, including computer scientists, engineers, practitioners, researchers, and students, this book serves as a valuable platform for sharing knowledge and fostering collaboration in the realms of machine learning, deep learning, and cyber-physical systems. It is particularly beneficial for anyone interested in leveraging data to drive innovation and improve outcomes in various fields.