Artificial Intelligence and Data Analytics for Innovative Applications in Engineering, Sustainability and Technology
Description
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.
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Persons
Dr. Aniss Moumen is Professor of Computer Science at the National School of Applied Sciences-Kenitra, part of Ibn Tofail University in Kenitra, Morocco. He graduated in 2002 as an Electrical Application Engineer from the University of Sidi Mohammed Ben Abdellah in Fez, Morocco. In 2005, he earned a specialized master's degree in information security and systems from the Higher School of Computer Science, Electronics, and Automation in Paris. He also received a master's in network and Telecommunication from the Chaib Doukkali University in El Jadida in 2004. He completed his Ph.D. in Computer Science, Mathematics, and Applications at Ibn Tofail University in 2016. Dr. MOUMEN teaches engineering students various subjects.
Mohammed Zouiten graduated as Electrical Application Engineer from the University of Sidi Mohammed Ben Abdellah-Fez-Morroco. In 2007, he received his master's degree in computer science from the same University. In 2014, he received his Ph.D. degree in Geographical Information Systems and modeling From USMBA University. In 2021, he received his university accreditation From Abdelmalik Essadi University, Tetouan of Morroco, where he has also collaborated as Researcher in several scientific projects. Over the last five years, he has worked as Professor and Researcher at the Faculty Polydisciplinary of TAZA at the University Sidi Mohammed Ben Abdellah, where he has conducted several statistics and computer science courses and supervised some master theses. He has also produced several publications on floods, forest fires, and natural hazards in general.
Dr. Charaf Hajjaj is Associate Professor at the Higher School of Technology in Essaouira, part of Cadi Ayyad University. He received his Ph.D. in Physics and Engineering from the Faculty of Sciences at Chouaïb Doukkali University, El Jadida, Morocco in March 2019. Dr. Hajjaj also holds a M.Sc. in Telecommunications, Networks, and Industrial Electronics from the same university. Dr. Charaf has several years of experience working at the Green Energy Park research center (UM6P - IRESEN) solar test facilities in Ben-Guerir, Morocco. As part of his Ph.D. research, he conducted experiments testing various PV technologies under semi-arid and harsh climate conditions.
Dr. Brahim El Bhiri is Professor of Telecommunication and embedded systems at the Moroccan School of Engineering Sciences (EMSI: Ecole Marocaine des Sciences de l'Ingenieur). He is Director of R&D&I at the same school and Head of the SMARTiLab Laboratory of Research, Development, and Innovation. He has over 12 years of experience in electronics, wireless sensor networks, data processing, and embedded systems. He has published over thirty papers in international scientific journals and conferences. He is the author of 5 books and 22 book chapters, published by leading international publishers like Springer.
Dr. Mohamed Mustapha Zarrouk holds a Ph.D. in Applied Mathematics from the Faculty of Sciences and Technology Errachidia, Morocco. He is currently Professor of applied mathematics and computer sciences at Euro-Med University, Fez. Dr. Zarrouk does research in numerical solutions of PDEs with applications related to flow and transport in porous media. Its most recent projects include the Preconditioned iterative methods for reactive transport models in porous media and optimized space domain decomposition.
Content
1. Understanding Public e-Service Adoption: A Conceptual Framework for Moroccan Urban Agencies.- 2. A Global Review and Empirical Study on Artificial Intelligence in Industry 4.0 with Insights from Moroccan Students.- 3. Improving Battery Utilization and Lifespan of an Electric Vehicles: Advanced SOC Estimation via Deep Neural Network.- 4. Impact evaluation of school transport program on schooling in rural areas in Morocco.- 5. Detection of Facial Emotion with Deep Learning Models and Contribution of Inception-V3.
