Artificial Intelligence and Machine Learning for Safety-Critical Systems - A Comprehensive Guide

Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide serves as a vital reference for engineers and system designers seeking to integrate AI and ML techniques into safety-critical environments. The book is meticulously structured into nine sections, each focusing on core applications and challenges unique to these high-stakes systems. Readers are guided through strategies that optimize resources, minimize failures, and bolster both system and public safety. With its practical approach, the guide aims to bridge the gap between advanced AI solutions and the rigorous demands of safety-critical industries.

The book also delves into diverse domains such as pattern recognition, image processing, edge computing, IoT, encryption, and hardware accelerators. Each application area is explored to reveal the unique hurdles and solutions in deploying ML models in safety-sensitive contexts. Finally, the authors also emphasize the importance of explainable AI, ensuring model outputs are transparent and trustworthy rather than opaque. To further strengthen confidence in these systems, the text discusses legal, certification, and regulatory aspects, equipping readers with the tools necessary to achieve compliance and public trust.

List of contents

Introduction to AI and Machine Learning for Safety-Critical Systems

Section 1: Healthcare
1. Robotics surgery
2. Bio signal processing
3. Medical imaging
4. Medical devices and Life support systems

Section 2: Transportation
5. Autonomous driving
6. Railway transportation
7. Air transportation
8. Roadway transportation

Section 3: Avionics and Space
9. Space systems
10. Rovers for space
11. Satellite communications
12. Radiation related issues

Section 4: Finance
13. Banking systems
14. Business analysis
15. Taxation
16. Loans and Investment
17. Fraud prevention

Section 5: Utility systems
18. Waste-water supply systems
19. Natural gas distribution
20. Power grid distribution
21. Weather systems

Section 6: Manufacturing
22. Heavy Industry
23. Drug manufacturing
24. Electronics manufacturing
25. Food industry
26. Mining industry

Section 7: Telecommunication and Infrastructure
27. Internet of things
28. Sensing technology
29. Distributed communication
30. Communication and controls
31. Radio environment

Section 8: Security and compliance
32. Admin and public services
33. Encryption/decryption
34. Cybersecurity
35. System Monitoring and Intrusion detection system

Section 9: Nuclear systems
36. Nuclear controller and cooling systems
37. Nuclear leak and radiation detections
38. Reactor protection system
39. Nuclear core reactor
40. Management systems for nuclear facility

About the author

Dr. Rajiv Pandey is a Faculty member at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India. He possesses a diverse background experience of around 35 years to include 15 years in industry and 20 years of academic research and instruction. His research interests include blockchain and crypto currencies, information security, semantic web provenance, Cloud computing, Big Data, and Data Analytics. Dr. Pandey is a Senior Member of IEEE and has been a session chair and technical committee member for various IEEE conferences. He has been on the technical committees of various government and private universities, and is the editor of Quantum Computing: A Shift from Bits to Qubits from Springer, Data Modelling and Analytics for the Internet of Medical Things from CRC Press/Taylor & Francis, and Artificial Intelligence and Machine Learning for Edge Computing from AP/Elsevier.
Dr. Kanishka Tyagi is Director of Artificial Intelligence at UHV Technologies, Ft. Wayne, IN, USA, where he leads the development of Machine Learning in diverse R&D projects,
including the sorting of non-recyclable plastics, metal alloys, pathological samples, and the analysis of Roots CT images, funded by the US Department of Energy. Previously, he has worked as a lead machine learning autonomous driving scientist at Aptiv Corporation in Agoura Hills, California. Prior to Aptiv, he worked at Siemens research, interned in ML groups at The MathWorks and Google Research. He has worked as a visiting researcher at Ajou University and Seoul National University. Dr. Tyagi worked as a Research Associate at the Department of Electrical Engineering, Indian Institute of Technology, Kanpur, with Dr. P.K. Kalra. He received his M.S. and Ph.D. degree with Dr. Michael Manry in the Department of Electrical Engineering at the University of Texas at Arlington. His research interests are optimization theory, music and audio processing, neural networks, hardware machine learning, and radar machine learning. He is a co-editor of Quantum Computing: A Shift from Bits to Qubits from Springer. Dr. Tyagi has filed 15 U.S. patents/trade secrets in the course of his research.Dr. Neeraj Kumar Singh is an Associate Professor of Computer Science at INPT-ENSEEIHT and member of the ACADIE team at IRIT. Before joining INPT, Dr. Singh worked as a research fellow and team leader at the Centre for Software Certification (McSCert), McMaster University, Canada. He worked as a research associate in the Department of Computer Science at University of York, UK. He also worked as a research scientist at the INRIA Nancy Grand Est Centre, France, where he has received his Ph.D. in Computer Science. He leads his research in the area of theory and practice of rigorous software engineering and formal methods to design and implement safe, secure, and dependable critical systems. He is an active participant in the “Pacemaker Grand Challenge.” Dr. Singh is the author/editor of Quantum Computing: A Shift from Bits to Qubits and Using Event-B for Critical Device Software Systems from Springer, Essential Computer Science: A Programmer’s Guide to Foundational Concepts and Industrial System Engineering for Drones from APress, and System on Chip Interfaces for Low Power Design from Morgan Kaufmann/Elsevier.
Dr. Nidhi Srivastava is currently working as Assistant Professor at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus India. She has more than 16 years of teaching experience. Dr. Srivastava’s research interests include Human Computer Interaction, Cloud computing, semantic web, and speech recognition. She is a co-editor of Quantum Computing: A Shift from Bits to Qubits and Semantic IoT: Theory and Applications from Springer.

Product details

• Assisted by: Rajiv Pandey (Editor), Singh Neeraj Kumar (Editor), Srivastava Nidhi (Editor), Kanishka Tyagi (Editor)
• Publisher: Elsevier
• Languages: English
• Product format: Paperback / Softback
• Release: 01.05.2026
• EAN: 9780443365973
• ISBN: 978-0-443-36597-3
• Weight: 450 g
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; machine learning, Robotics, Artificial Intelligence, TECHNOLOGY & ENGINEERING / Robotics, COMPUTERS / Programming / General, Artificial Intelligence (AI), Computer programming / software engineering, Expert systems / knowledge-based systems, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Artificial Intelligence / Expert Systems, Computer Programming / Software Development