This book covers a wide range of topics related to the integration of Artificial Intelligence, Big Data, IoT, and Blockchain: From Concepts to Applications. It begins by establishing a solid foundation and introducing the concepts and principles of each technology. The subsequent chapters delve into the various applications and use cases, providing readers with real-world examples of how AI, IoT, and Blockchain can be leveraged to address key challenges in Smart Environments. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Data science, Artificial Intelligence, and Smart Environments inspire novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and social science. This book reviews the state of the art of big data analysis, Artificial Intelligence, and Smart Environments. It includes issues that pertain to signal processing, probability models, machine learning, data mining, databases, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, smart city, etc. The papers in this book were the outcome of research conducted in this field of study. The latter makes use of applications and techniques related to data analysis in general and big data and smart cities in particular. The authors hope that this book serves as a valuable resource and guide for readers, empowering them to navigate the intricate landscape of Artificial Intelligence, IoT, and Blockchain in Smart Environments. Let the authors embark on this transformative journey together, as the authors explore the concepts and applications that hold the potential to shape the future of Smart Environments. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers, as well as anyone interested in big data analysis and Artificial Intelligence.
Chapter
1. Iterative method of Newton-Raphson for systems with two
equations.- Chapter
2. Harnessing Reinforcement Learning for Enhanced Solar
Radiation Prediction: State-of-the-Art and Future Directions.- Chapter
3.
LoRaWAN-based Smart Irrigation Systems: A literature review.- Chapter
4. A
Temporal Metadata Management Approach for Data Lakes.- Chapter
5. Artificial
Intelligence in Social Media: From Content Personalization to User
Engagement.- Chapter
6. Designing a Comprehensive Data Security Protection
System for Multi-Dimensional Threats in Cloud Computing.- Chapter
7.
Optimized CNN-BiGRU Intrusion Detection Model with SMOTE Enhancement: Using
Optuna for Automated Hyperparameter Tuning.- Chapter
8. Digitally-Enabled
Labor Market: The Dark Side of Digital Transformation.- Chapter
9. A
Comparative Study on the Adoption of Customer Relationship Management (CRM)
Technologies by Family and Non-Family Small and Medium Enterprises
(SMEs).- Chapter
10. Learning design of lesson plans with LAMS and
CADMOS.- Chapter
11. Systematic literature reviews in political marketing:
behavior, influence, and trust in the era of big data and artificial
intelligence.- Chapter
12. The role of artificial intelligence and collective
intelligence in the evolution of the HR function.- Chapter
13. The Role of
Business Intelligence Systems in an Organization Case of SONACOS,
Morocco.- Chapter
14. Marketing strategies developed by informal production
units.- Chapter
15. Medical Image Compression Using Discrete Wavelet
Transform.- Chapter
16. Perspectives on the integration of artificial
intelligence in the independent hotel sector of the Rabat-Sale-Kenitra
region: An empirical study.- Chapter
17. Modeling Moroccan consumer behavior:
An empirical approach.- Chapter
18. Exploring Consumer Behavior in Emerging
Markets Towards Organic Products Through Local Identity, Patriotism, and
Trust in Origin: Moroccan Case.- Chapter
19. The Influence of Behavioral
Biases on Portfolio Management Decisions: Deviations from the Efficient
Frontier of Modern Portfolio Theory.- Chapter
20. Optimizing Hyperparameters
for Fraud Detection: A Comparative Analysis of Machine Learning Algorithms.-
Etc..
Yousef FARHAOUI is a Professor at Moulay Ismail University, Faculty of sciences and Techniques, Morocco. He is the Chair of IDMS Team and the Director of STI Laboratory. He obtained his Ph.D. degree in Computer Security from Ibn Zohr University of Science. His research interests include learning, AI, e-learning, computer security, big data analytics, and business intelligence. Farhaoui has three books in computer science. He is a coordinator and member of the organizing committee and member of the scientific committee of several international congresses. He is also a member of various international associations. He has authored 15 books and several book chapters with Springer and IGI. He has served as a Reviewer for IEEE, IET, Springer, Inderscience and Elsevier Journals. He is also the Guest Editor of several journals like Wiley, Springer, Inderscience, and others. He has been the General Chair, Session Chair, and Panelist in several conferences. He is Senior Member of IEEE, IET, ACM and EAI.
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