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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 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.