AI and IoT in Engineering, Life Science, and Healthcare

AI and IoT in Engineering, Life Science, and Healthcare presents an integrated, application-focused exploration of how intelligent algorithms and connected sensing systems are jointly designed, deployed, and evaluated to solve real-world challenges across engineering workflows, biological research, and clinical practice.

AI and IoT in Engineering, Life Science, and Healthcare presents an integrated, application-focused exploration of how intelligent algorithms and connected sensing systems are jointly designed, deployed, and evaluated to solve real-world challenges across engineering workflows, biological research, and clinical practice. Readers will gain a clear, practice-oriented understanding of how to build end-to-end solutions—from data acquisition with IoT devices to model development, deployment, and validation—supported by concise explanations, case studies, and reproducible workflows. The book explains how to select appropriate sensors, design data pipelines, apply learning models, and assess performance using relevant metrics, while addressing interoperability, scalability, and reliability. It also covers implementation considerations such as edge vs. cloud processing, system integration, and lifecycle management. By combining conceptual clarity with step-by-step examples and applied scenarios, the book enables readers to move from theory to working prototypes and informed decision-making. This book is intended for senior undergraduate and postgraduate students in engineering, biomedical sciences, and health informatics, as well as researchers, practitioners, and professionals seeking to apply connected intelligent systems in applied settings. It is also suitable for educators and industry engineers looking for a structured reference that bridges disciplines and emphasizes practical deployment. Key Features :End-to-end coverage from IoT data acquisition to model deployment and evaluationCross-disciplinary case studies spanning engineering systems, life sciences, and healthcarePractical workflows with implementation guidance for edge and cloud environmentsEmphasis on system integration, scalability, and real-world constraints