Introduction

1. Introduction

The aim of this editorial Book Title: Reliability Modelling, System Engineering and Management through Computational Intelligence is to publish well-written original research studies and articles that describe the latest research and developments in the area of reliability modeling, system engineering, and related management. The book gripped on a comprehensive range of mathematics applied in system reliability engineering, which shall provide a collaborative platform among several peers that spans not only an organization but also other organizations to disseminate the research to the practical environment. The use of computational intelligence and theoretical innovations & case studies on reliability assurance with special reference to software (s) shall be discussed in the chapters.

2. Topics to be covered (Not Limited to)

Part 1: Foundations

1. Introduction to Reliability Engineering

This chapter provides a comprehensive overview of reliability engineering principles, including key concepts, metrics (e.g., MTBF, MTTF), and common reliability assessment methods. It discusses the importance of reliability in various industries and the challenges associated with ensuring system reliability.

2. Principles of Systems Engineering

This chapter delves into the core concepts and methodologies of systems engineering, emphasizing a holistic approach to system design, development, and lifecycle management. It encompasses critical components, including system requirements analysis, design processes, integration and testing, and system lifecycle management.

3. Fundamentals of Computational Intelligence

In this chapter, we will go over the basics of computational intelligence, which includes AI, ML, DL, evolutionary algorithms, fuzzy logic, and other related approaches. It investigates these methods' possibilities to solve difficult problems in management and engineering.

Part 2: Applications

4. Machine Learning for Reliability Modelling and Forecasting

The chapter highlights the use of computational intelligence for modelling and forecasting reliability.It examines machine learning techniques for system failure prediction, PHM systems, and maintenance improvements.

5. Designing and Optimising Systems Using Computational Intelligence Methods

Artificial intelligence can improve system design reliability. The paper discusses test planning, evolutionary algorithms to enhance designs, and AI-powered tools to explore and assess designs.

6. Risk Evaluation and Mitigation Utilising AI-Enhanced Instruments

This chapter discusses how AI can manage and quantify risk in complex systems. It discusses AI-powered risk management systems, fault tree analysis, Bayesian networks, and identifying and reducing risks.

7. Maintenance and Reliability Centered Maintenance (RCM) Strategies

AI can improve repair plans, as discussed in this chapter. AI is used to improve reliability-centered maintenance (RCM), condition-based maintenance (CBM), and predictive maintenance systems (PMS).

Part 3: Case Studies

8. Real-world Applications in Different Industries

This section presents real-world examples from several industries (including energy, aerospace, and automotive) that illustrate how AI technologies have enhanced system reliability and management.

Part 4: Future Trends and Emerging Technologies

9. Machine Learning for IIoT and Cyber-Physical Infrastructure

AI and computational intelligence in cyber-physical systems and the Industrial Internet of Things can improve network reliability, efficiency, and resilience. This chapter examines integration.

10. The Latest Developments in Reliability Engineering (such as Digital Twins and Safety Systems Driven by AI)

This chapter addresses developing trends and technologies in dependability engineering including the use of digital twins for system modelling and simulation, the creation of AI-powered safety systems, and ethical issues related to the growing dependence on AI in important systems.

· Fault Tolerance in Software systems

· Modeling and Analysis of complex Software Systems

· Software Reliability Optimization through Soft Computing Techniques

· Software failure analysis using advanced investigative techniques

· Software Reliability and Testing

· Software Vulnerability Analysis.

· Software Patch Management

· Software and Web Engineering

· System Safety and Performance

· Stochastic Process &Software Engineering

3. Submission Procedure

Researchers, Industrial Professional and practicianars are invited to submit their chapter Proposal/Abstract for inclusion in the upcoming book R-SEMCI. Abstract should be 200-300 words outlining the chapters scope, Objectives and Key contributions. upon acceptance the chapters will be published in a Springer Nature. The work may be published in the book series "Engineering Cyber- Systems and Critical Infrastructure".

Authors must ensure their work is original and plagiarisma free and submission should be made via E-Mail and queries can be directed to E-Mail id provided.

Please provide the following points in your proposals / abstracts:

1) Title of the contribution

2) Name of author, co-authors, institution, email-address

3) Content/Abstract of the proposed chapter

5. Publisher

This edited book is scheduled to be published by Springer Group. This publication is anticipated to be released in 2026.

6. Important Dates

The following time line is anticipated:

Abstract Submission (200-300 Words): April 30, 2025

Abstract Acceptance Notification: May 10, 2025

Full Chapter Submission: September 30, 2025

Full Chapter Revision Submission: October 20, 2025

Chapter Submission to Springer: November 30, 2025

7. Inquiries

All the queries can be sent to rmsem.book@gmail.com

8. Editors

Dr. Mukesh Mishra, Galgotias University, India.

Dr. Prashant Johri, Galgotias University, India.

Dr. Adarsh Anand, University of Delhi, India.