A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning

A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning

Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning...

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Main Authors: Abubakar, Yahaya Idris, Othmani, Alice, Siarry, Patrick, Sabri, Aznul Qalid Md
Format: Article
Published: Institute of Electrical and Electronics Engineers 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/45833/
https://doi.org/10.1109/ACCESS.2024.3382140
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Institution: Universiti Malaya
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spelling my.um.eprints.458332024-11-12T07:42:10Z http://eprints.um.edu.my/45833/ A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning Abubakar, Yahaya Idris Othmani, Alice Siarry, Patrick Sabri, Aznul Qalid Md QA75 Electronic computers. Computer science Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field. Institute of Electrical and Electronics Engineers 2024 Article PeerReviewed Abubakar, Yahaya Idris and Othmani, Alice and Siarry, Patrick and Sabri, Aznul Qalid Md (2024) A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning. IEEE Access, 12. pp. 47091-47109. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3382140 <https://doi.org/10.1109/ACCESS.2024.3382140>. https://doi.org/10.1109/ACCESS.2024.3382140 10.1109/ACCESS.2024.3382140
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Abubakar, Yahaya Idris
Othmani, Alice
Siarry, Patrick
Sabri, Aznul Qalid Md
A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning
description Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.
format Article
author Abubakar, Yahaya Idris
Othmani, Alice
Siarry, Patrick
Sabri, Aznul Qalid Md
author_facet Abubakar, Yahaya Idris
Othmani, Alice
Siarry, Patrick
Sabri, Aznul Qalid Md
author_sort Abubakar, Yahaya Idris
title A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning
title_short A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning
title_full A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning
title_fullStr A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning
title_full_unstemmed A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning
title_sort systematic review of rare events detection across modalities using machine learning and deep learning
publisher Institute of Electrical and Electronics Engineers
publishDate 2024
url http://eprints.um.edu.my/45833/
https://doi.org/10.1109/ACCESS.2024.3382140
_version_ 1816130464982761472

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