Wadii Boulila – författare

Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.Examines the potential of edge computing and deep transfer learning, offering in-depth insights into how edge intelligence can be leveraged to enhance IoT and IIoT securityEmphasizes the development of lightweight and resource-efficient models suitable for deployment on edge devices, ensuring that security measures can be effectively implemented without imposing undue computational burden or network overheadPresents practical examples, case studies, and implementation guidelines that demonstrate how advanced deep transfer learning techniques can be applied to address real-world security challenges in IoT and IIoT deployments

This book contains contemporary research that outlines and addresses security, privacy challenges and decision-making in IoT environments. The authors provide a variety of subjects related to the following Keywords: IoT, security, AI, deep learning, federated learning, intrusion detection systems, and distributed computing paradigms. This book also offers a collection of the most up-to-date research, providing a complete overview of security and privacy-preserving in IoT environments. It introduces new approaches based on machine learning that tackles security challenges and  provides the field with new research material that’s not covered in the primary literature. The Internet of Things (IoT) refers to a network of tiny devices linked to the Internet or other communication networks. IoT is gaining popularity, because it opens up new possibilities for developing many modern applications. This would include smart cities, smart agriculture, innovative healthcare services and more. The worldwide IoT market surpassed $100 billion in sales for the first time in 2017, and forecasts show that this number might reach $1.6 trillion by 2025. However, as IoT devices grow more widespread, threats, privacy and security concerns are growing. The massive volume of data exchanged highlights significant challenges to preserving individual privacy and securing shared data. Therefore, securing the IoT environment becomes difficult for research and industry stakeholders.Researchers, graduate students and educators in the fields of computer science, cybersecurity, distributed systems and artificial intelligence will want to purchase this book. It will also be a valuable companion for users and developers interested in decision-making and security risk management in IoT environments.

This book contains contemporary research that outlines and addresses security, privacy challenges and decision-making in IoT environments.

From revolutionizing healthcare diagnostics and advancing natural language processing for low-resource languages to enhancing software development and promoting environmental sustainability, this book explores the cutting-edge advancements and practical applications of generative AI and large language models (LLMs).

This book presents a groundbreaking compilation of cutting-edge research at the forefront of innovation, where artificial intelligence, machine learning, and advanced technologies converge to address real-world challenges with realistic solutions. This comprehensive book bridges theory and practice, offering transformative insights across diverse domains—from healthcare and cybersecurity to smart cities and language processing.Dive into pioneering advancements in Technology such as Arabic text-to-gloss machine translation, transformer-based image captioning, and GAN-enhanced privacy policies for constrained environments. Learn how deep learning revolutionizes cancer detection in medical imaging, optimizes traffic flow prediction, and refines speaker diarization in forensic audio. Immerse into ethical AI discussions, UAV navigation in GPS-denied terrains, and blockchain-secured voting systems, all while uncovering novel approaches to IoT security, cloud computing, and industrial efficiency.With contributions from leading researchers and practitioners, this book addresses critical topics like AI-driven sentiment analysis for business intelligence, self-supervised graph neural networks, and ethical implications of emerging technologies. Each chapter combines rigorous technical depth with actionable applications, making it an indispensable resource for researchers, engineers, and students eager to harness AI’s potential in tackling modern global challenges.

Artificial Intelligence is transitioning from passive predictive models to Agentic AI—systems capable of autonomous reasoning, goal-directed behavior, and dynamic interaction with complex environments. This book provides a comprehensive examination of this paradigm, integrating conceptual foundations with architectural frameworks and diverse real-world applications. It clarifies key definitions of agency, critically analyzes agent capabilities and limitations, and details the core mechanics of autonomous reasoning that distinguish agentic systems from traditional automation.The book surveys emerging methodologies, including the evolution from Retrieval-Augmented Generation (RAG) to Agentic RAG, multimodal curiosity mechanisms in language models, and hybrid LLM–optimizer approaches for complex scheduling and orchestration. It further bridges theory and practice through domain-specific chapters addressing deployment in healthcare—emphasizing explainable interpretation for EEG-based classification—enterprise BPM and workflow-centric systems, and cyber-physical infrastructures. Technical contributions also examine tool-based multi-agent engineering workflows, such as automated CAD-to-mesh transformation. Concluding with a critical assessment of challenges and opportunities, the volume offers a rigorous reference for researchers and practitioners advancing autonomous intelligent systems.