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Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts

Author

Listed:
  • Mohammed Majid Abdulrazzaq

    (Department of Computer Engineering, Demir Celik Campus, Karabuk University, 78050 Karabuk, Turkey
    Computer Center, University of Anbar, Anbar 31001, Iraq)

  • Nehad T. A. Ramaha

    (Department of Computer Engineering, Demir Celik Campus, Karabuk University, 78050 Karabuk, Turkey)

  • Alaa Ali Hameed

    (Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, 34396 Istanbul, Turkey)

  • Mohammad Salman

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Dong Keon Yon

    (Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University, Seoul 02453, Republic of Korea)

  • Norma Latif Fitriyani

    (Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea)

  • Muhammad Syafrudin

    (Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea)

  • Seung Won Lee

    (Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea)

Abstract

Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review’s numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy.

Suggested Citation

  • Mohammed Majid Abdulrazzaq & Nehad T. A. Ramaha & Alaa Ali Hameed & Mohammad Salman & Dong Keon Yon & Norma Latif Fitriyani & Muhammad Syafrudin & Seung Won Lee, 2024. "Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts," Mathematics, MDPI, vol. 12(5), pages 1-42, March.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:758-:d:1350634
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    References listed on IDEAS

    as
    1. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    2. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    Full references (including those not matched with items on IDEAS)

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