Artificial Intelligence and Machine Learning for Safety-Critical Systems

A Comprehensive Guide
Erscheinungsjahr 2026
ISBN: 978-0-443-36597-3
Verlag: Elsevier Science

Buch, Englisch, 350 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource capturing the challenges and mitigation strategies involved in designing such systems. Divided into nine sections, the book covers the most important applications of safety-critical systems, helping readers understand how related problems are being solved in different domains/problem settings. The goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety. The authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems. In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models.

Autoren/Hrsg.

Pandey, Rajiv

Tyagi, Kanishka

Singh, Neeraj Kumar

Srivastava, Nidhi

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering

Inhaltsverzeichnis

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

Über Autor(innen)

Srivastava, Nidhi
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.

Tyagi, Kanishka
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.

Pandey, Rajiv
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.