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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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10479512/ |
| _version_ | 1797221992573698048 |
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| author | Yahaya Idris Abubakar Alice Othmani Patrick Siarry Aznul Qalid Md Sabri |
| author_facet | Yahaya Idris Abubakar Alice Othmani Patrick Siarry Aznul Qalid Md Sabri |
| author_sort | Yahaya Idris Abubakar |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-24T13:14:15Z |
| format | Article |
| id | doaj.art-b5b38111d02f4052ac389d50e1bddef2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T13:14:15Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b5b38111d02f4052ac389d50e1bddef22024-04-04T23:00:45ZengIEEEIEEE Access2169-35362024-01-0112470914710910.1109/ACCESS.2024.338214010479512A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep LearningYahaya Idris Abubakar0https://orcid.org/0009-0000-5057-0261Alice Othmani1https://orcid.org/0000-0002-3442-0578Patrick Siarry2https://orcid.org/0000-0002-5722-4115Aznul Qalid Md Sabri3https://orcid.org/0000-0002-4758-5400Laboratoire Images, Signaux et Systémes Intelligents (LiSSi)–EA 3956, Université Paris-Est Créteil (UPEC), Créteil Cedex, FranceLaboratoire Images, Signaux et Systémes Intelligents (LiSSi)–EA 3956, Université Paris-Est Créteil (UPEC), Créteil Cedex, FranceLaboratoire Images, Signaux et Systémes Intelligents (LiSSi)–EA 3956, Université Paris-Est Créteil (UPEC), Créteil Cedex, FranceFaculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaRare 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.https://ieeexplore.ieee.org/document/10479512/Artificial intelligencedeep learningdetectionmachine learningrare event detection |
| spellingShingle | Yahaya Idris Abubakar Alice Othmani Patrick Siarry Aznul Qalid Md Sabri A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning IEEE Access Artificial intelligence deep learning detection machine learning rare event detection |
| title | 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_short | 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 |
| topic | Artificial intelligence deep learning detection machine learning rare event detection |
| url | https://ieeexplore.ieee.org/document/10479512/ |
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