Yazar "Zhang, Peiying" seçeneğine göre listele

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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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    Convolutional Neural Networks: The Power Behind Image Recognition
    (CRC Press, 2025) Almomani, Mohammad H.; Hanandeh, Essam; Şahin, Canan Batur; Zhang, Peiying; Zuong, Zuzho; Nasayreh, Ahmad; Abualigah, Laith
    A number of artificial intelligence advancements have emerged that allow computerizes the same critical thinking tasks that were solely more suited to humans such as image recognition of objects etc. One such advancement that is the focus of the paper is the Convolutional Neural Networks. This paper details the structural design, how they perform the tasks assigned and how they function as a contribution to the image recognition area. Additionally, we show through a case study of three CNN models how they can be evaluated in relation to one set of common data effectively and efficiently. The data shows that there was improvement in processing time and accuracy which emphasises the role of CNN in the detailed domains. Some future trends in the CNN development and its usages are presented. © 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.