Artificial Intelligence and Data Analytics for Innovative Applications in Engineering, Sustainability and Technology

This book explores the forefront of Artificial Intelligence (AI), data analytics, and sustainable engineering, presenting in-depth research and practical applications across various domains. It is designed to provide valuable insights for professionals, researchers, and students eager to understand these cutting-edge advancements. The book begins by applying AI to the formal verification of System-on-Chip (SoC) components, showcasing methods that ensure efficient and reliable design processes. It discusses innovative techniques for optimizing the formulation of unfired clay bricks with Chamaerops Humilis fibers. It evaluates the mechanical and thermal properties of compact clay blocks stabilized with wood ash or traditional pottery waste. A detailed bibliometric analysis highlights research progress and trends in renewable energy and sustainable development, offering a clear view of future directions in these vital fields. The book also reviews advancements in traction motors and power electronic converters for electric vehicle drivetrains, providing insights into their efficiency and performance. The mathematical intricacies of the Topological Degree Method for coupled systems of $\psi$-fractional semilinear differential equations with non-local conditions are examined. The book delves into the impact of AI and machine learning on intelligent banking, anomaly detection in IoT networks, and enhancing model portability using Moodle users' traces. Case studies on the profitability of green hydrogen production powered by wind generators and the adoption of public e-services in Moroccan urban agencies illustrate real-world applications and offer practical insights into implementing these technologies. Additionally, the integration of AI in supply chain management within the automotive industry and the future of multi-touch attribution modeling for marketing strategies are explored. The book addresses big data architecture for managing water resources and mapping water quality variability. It includes practical applications such as a hybrid technique for anomaly detection in IoT networks and the smart OSC-Mac CT mode in wireless sensor networks based on WI-LEM technology.