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

Mohammed Majid Abdulrazzaq, Nehad T. A. Ramaha, Alaa Ali Hameed, Mohammad Salman, Dong Keon Yon, Norma Latif Fitriyani (), Muhammad Syafrudin () and Seung Won Lee ()
Additional contact information
Mohammed Majid Abdulrazzaq: Department of Computer Engineering, Demir Celik Campus, Karabuk University, 78050 Karabuk, Turkey
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

Mathematics, 2024, vol. 12, issue 5, 1-42

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.

Keywords: deep learning (DL); self-supervised learning (SSL); machine learning (ML); cognition; classification; data annotation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/5/758/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/5/758/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:5:p:758-:d:1350634

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().