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    The Evolution of Machine Learning: From Traditional Algorithms to Deep Learning Paradigms
    (CRC Press, 2025) Alomari, Saleh Ali; Abdel-Salam, Mahmoud; Raza, Ali; Şahin, Canan Batur; Zitar, Raed Abu; Zhang, Peiying; Snasel, Vaclav
    There has been a noticeable development in the area of machine learning (ML) over the last few decades, transitioning from conventional algorithm-based systems to neural networks. The goal of this chapter is to portray the evolution of machine learning, highlighting important steps, primary algorithms, and the development of mono-approach neural network modeling. We examine the various methodologies developed in the field, including supervised, unsupervised, and reinforcement learning, and explain how deep learning architectures have transformed image recognition and natural language processing, and autonomous systems. The chapter concludes by addressing existing issues in machine learning, most notably interpretability, bias and the computational complexity, while suggesting directions for future research in this active field. © 2025 Laith Abualigah.
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    The Intersection of AI and the Internet of Things (IoT): Transforming Data into Intelligence
    (CRC Press, 2025) Al-Okbi, Nada Khalil; Khodadadi, Nima; Kumar, Manoj; Şahin, Canan Batur; Khishe, Mohammad; Raza, Ali; Abualigah, Laith
    The application of Artificial Intelligence (AI)-enabled systems is becoming a trend in decision making impacts revenue and efficiency of the companies while adding value to IoT (Internet of Things). This research paper investigates the elements upholding the synergy between IoT and AI, arguing that their integration advances the development of smart systems that optimize decision making, improve operational processes, and improve user experience. Ample attention is paid to several aspects of AI integration practices: techniques used within various IoT hubs, exposed results of their case application, and archived barriers and prospects of this paradigmatic combination. Such an understanding will enable the relevant actors to create data-driven intelligence that is appropriate for them. © 2025 Laith Abualigah.