Masoomeh Mirrashid – författare

This book demonstrates innovative approaches in the application of computational intelligence. The initial chapters discuss the application of AI to analyze physical systems, in particular contemporary neural networks for rapid and accurate seismic analysis of critical infrastructure. It also discusses advanced metaheuristic and hybrid AI approaches that afford robust, non-invasive evaluations of geological and rock-mass properties, replacing the expensive and time-consuming traditional methods. The subsequent chapters expand the scope to include strategic and operational intelligence dimensions, where the use of generative AI aims at enhancing the security and functionality of IoT ecosystems, and where the seamless interplay of Natural Language Processing and IoT technology enables more human-centred control of systems. It also describes Edge AI, elaborating the on-device processing capabilities that enable fast, secure, real-time decision-making.

The introduction of AI and ML technologies has brought new changes to the engineering profession. This book presents the theory and practical solutions backed with real results. It also explains in detail the uses of Machine Learning methods, Gene Expression Programming, Extreme Gradient Boosting, and Deep Neural Networks in predicting parameters that are critical in understanding soil behavior, foundation settlements, material behavior, resource consumption, and beyond. One focal point is the shift from opaque models to transparent, accountable AI. The integration of AI methods is elucidated to clarify decisions made by predictive models and instill trust in the predictive systems. Additionally, the book addresses the issue of sustainability by demonstrating how AI can refine the utilization of industrial by-products such as fly ash and marble slurry in the construction sector and improve the efficiency of public transportation systems.

This book presents smart optimization techniques and adaptive intelligent control systems which stimulate innovations in engineering and computational science. This book captures these innovations and describes multiple applications of various advanced algorithms. The book encompasses a diverse range of applications and illustrates the value of these computational techniques; notable applications include the attitude control of CubeSats, the performance of building components during seismic events, the efficiency of tunnel-boring machines, and forecasting of environmental and structural phenomena. A major theme presented within the book is the development of advanced intelligent systems, including self-optimizing modules, as reflected in research advances on Digital Twins and Omniverse technologies, where real-time supervision and control are achieved through virtual replicas of physical entities and spatial environments. The incorporation of artificial intelligence (AI) provides essential simplifications of otherwise opaque models, yielding interpretable insights that facilitate process automation. By connecting rigorous theoretical work with real-world problems, the book serves as a guide for researchers, engineers, and students, which offers a comprehensive picture of advanced developments in the area of adaptive optimized systems, alongside the theory and practice necessary to innovate within systems engineering.

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.