This book presents a collection of recent work that applies new and sophisticated computational techniques to manage uncertainty. This book expands the focus to more contemporary approaches, including fuzzy logic, artificial intelligence (AI), machine learning, and multi-criteria decision-making, as well as other methods to solve problems. The chapters of this book describe various applications in critical areas of various disciplines such as the design and optimization of sustainable infrastructure, management of e-waste recycling networks, improvements in cyber security, and social media and toxic content classification. The shift from the opaque black-box models to transparent systems that explain the justification and the logic of the predictions is a key factor for model trust. This book is essential for those who seek computational intelligence for rational decision-making under uncertainty.
Novel algorithms for power loss reduction.- From Point Cloud to BIM:
Reconstruction for As-built Modeling of Steel Structural Frames.- Development
of smart mobility for passenger transportation in green cities based on an
intelligent transport network using aerial rope systems.- A Novel Score
Function-Based Group Decision-Making Framework for Selecting Solar
Photovoltaic Module Recycling Technologies in Quartic Fuzzy Environment.-
Floor Level Classification in Multistoried Educational Building Using
Interpretable Machine Learning: A Case Study in Dhaka, Bangladesh.- A Novel
Similarity Measure Based Approach to Solve Triangular Fuzzy Multi- Objective
Linear Programming Problem.- Assessment of the Mechanical Properties of Fibre
Reinforced Soil Using Artificial Intelligence: A State-of-the-Art Review.- An
XAI based Topic Sensitive Classification of Social Media Fandom Wars using
Semantic Embeddings and Deep Transformer Models.- A Complex Linear
Diophantine Fuzzy Based MCGDM Approach for Optimal Site Selection of E-Waste
Recycling and Up-cycling Centres in the North-East Region of India.-
XSS-Shield: Multi-View Canonicalization, Dual-Branch Deep Learning, and a
Public Dataset for Obfuscation-Resilient Cross-Site Scripting Detection.
Dr. Aydin Azizi holds a Ph.D. in mechanical engineeringmechatronics. He currently serves as Senior Lecturer and Academic Partnership Liaison Manager at Oxford Brookes University. His current research focuses on investigating and developing novel techniques to model, control, and optimize complex systems, with expertise in control and automation, AI, and simulation techniques.
Dr. Danial Jahed Armaghani is an internationally recognized researcher and one of the most highly cited scientists globally in tunneling, geomechanics, and AI-driven predictive modeling. His research has advanced theory-guided machine learning and real-time TBM performance forecasting, establishing him as a leading expert driving innovation in mechanized tunneling and intelligent underground construction.
Dr. Mirrashid applies computational intelligence methods to problems in structural and earthquake engineering, with an emphasis on reducing the environmental footprint of built infrastructure. In her capacity as Research Consultant at Abu Dhabi University, she has devised machine learning approaches that advance predictive modeling of structural response, guide optimization of low-carbon construction materials, and inform rigorous assessments of infrastructure safety.
