D. Jeya Mala – författare

Building on the knowledge of risks, vulnerabilities, and safety measures associated with cyber-physical systems, this book focuses on adapting artificial intelligence (AI) techniques to smart cyber-physical systems application development. The future is going to see cyber-physical systems in almost every aspect of life, so a book that focuses on shedding light on the design, development, and security aspects of cyber-physical systems in more crucial domains such as defense, healthcare, biomedical, smart city applications, is needed.Integrating AI Techniques into the Design and Development of Smart Cyber-Physical Systems: Defense, Biomedical, Infrastructure, and Transportation offers an introductory exploration of the fundamental theories and concepts of AI and machine learning (ML) that are utilized in the building of dependable cyber-physical systems. It brings the ideas of advanced design and development and empowered security measures to cyber-physical systems. By focusing on the application of AI in cyber-physical systems design as well as security aspects, an improvement in reliability and advancements can be explored. Also included are the latest findings and advancements as well as case studies and illustrative examples on the design and development of smart cyber-physical systems.This resource is highly valuable for those employed in educational institutions, research laboratories, enterprises, and government agencies, as well as for students seeking novel ideas in the realm of smart cyber-physical systems design.

With the increasing use of deep learning systems across various industries, there is a growing need to make their decision-making processes more understandable and transparent. Regulatory requirements now demand clarity, and users and stakeholders want to know how AI systems work. The textbook addresses these needs by providing a detailed guide on integrating Explainable AI (XAI) into the Deep Learning Operations (DLOps) pipeline. By doing so, organizations can implement Continuous Integration (CI) and Continuous Deployment (CD) practices effectively.Explainable AI: Building Trustworthy Deep Learning Systems focuses on how to incorporate XAI models, tools, and techniques to clarify machine learning decisions. It explores applications in fields such as healthcare, defense, human activity recognition, and object identification. The book offers practical advice on embedding XAI tools throughout the lifecycle of deep learning systems, covering topics like Explainability and Interpretability, Deep Learning Operations (DLOps), and Machine Learning Operations (MLOps). It also includes real-world examples, challenges, and solutions.This textbook is ideal for undergraduate and graduate students studying computer science, electronic and communications engineering, and electrical and electronics engineering. It is particularly suited for courses like AI Internals in Cyber-Physical Systems, AI Security Analytics, and Human-Computer Interaction with XAI. Professionals in systems engineering and industrial engineering will also find it valuable.For those adopting the textbook for courses, a solutions manual and PowerPoint slides are available.