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    Deep Reinforcement Learning: Bridging Learning and Control in Intelligent Systems
    (CRC Press, 2025) Izci, Davut; Thanh, Hung Vo; Zitar, Raed Abu; Al-Okbi, Nada Khalil; Liu, Zhe; Şahin, Canan Batur; Abualigah, Laith
    Deep Reinforcement Learning (DRL) has gained popularity as a new approach in artificial intelligence that successfully integrates representation learning through Deep Learning with decision making in Reinforcement Learning. In this work, we investigate basic concepts of DRL design, structural composition, and scope of usage in intelligent systems. More specifically, several benchmark tasks were assigned to test states and actions of DRL algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO). Empirical evaluations illustrate the potential of DRL in dealing with complex tasks. Sample efficiency, stability and interpretability issues of DRL are also reviewed in the paper. Suggestions for further work are concentrated on algorithm increase of robustness, training time and expense decrease as well as enhancement of implementation efficiency in practical environment. © 2025 Laith Abualigah.
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    Recurrent Neural Networks and its Applications in Time Series Data
    (CRC Press, 2025) Nazza, Muhannad Akram; Al-Okbi, Nada Khalil; Şahin, Canan Batur; Hu, Gang; Sumari, Putra; Snasel, Vaclav; Abualigah, Laith
    Recurrent Neural Networks (RNNs) have been extensively embraced for sequencing analysis and sequential data modelling, especially time series data. This paper delves into the principles and applications of the RNNs concentrically time series analysis. The advantages of time series RNNs, including long short-term memory (LSTM) and Gated recurrent units (GRU), are discussed in detail. Different RNN models have been implemented on both synthetic and real-look time series datasets to assess their performance. The results clearly show RNNs outperformed traditional forecasting and pattern recognition methods. Lastly, the issues related to RNN training, namely, vanishing gradients and overfitting, are briefly addressed, and potential improvements for RNNs are outlined. © 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.