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    A review of mothflame optimization algorithm: analysis and applications
    (Elsevier, 2024) Abualigah, Laith; Al-Abadi, Laheeb; Ikotun, Abiodun M.; AL-Saqqar, Faisal; Izci, Davut; Zhang, Peiying; Sumari, Putra
    The moth flame optimization (MFO) algorithm, which is inspired by nature, is studied in this study. This optimizer takes its main inspiration from the directional navigation approach used by moths in nature. Moths fly at a steady angle to the moon at night. This is a particularly efficient method for traveling long distances in a straight line. Insects, on the other hand, are trapped in a useless/deadly spiral path around artificial lights. This chapter provides a review of published papers that used the MFO method. © 2024 Elsevier Inc. All rights reserved.
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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.