A brief review and scientometric analysis on ensemble learning methods for handling COVID-19
Numerous efforts and research have been conducted worldwide to combat the coronavirus disease 2019 (COVID-19) pandemic. In this regard, some researchers have focused on deep and machine-learning approaches to discover more about this disease. There have been many articles on using ensemble learning...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024027257 |
| _version_ | 1797267628259016704 |
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| author | Mohammad Javad Shayegan |
| author_facet | Mohammad Javad Shayegan |
| author_sort | Mohammad Javad Shayegan |
| collection | DOAJ |
| description | Numerous efforts and research have been conducted worldwide to combat the coronavirus disease 2019 (COVID-19) pandemic. In this regard, some researchers have focused on deep and machine-learning approaches to discover more about this disease. There have been many articles on using ensemble learning methods for COVID-19 detection. Still, there seems to be no scientometric analysis or a brief review of these researches. Hence, a combined method of scientometric analysis and brief review was used to study the published articles that employed an ensemble learning approach to detect COVID-19. This research used both methods to overcome their limitations, leading to enhanced and reliable outcomes. The related articles were retrieved from the Scopus database. Then a two-step procedure was employed. A concise review of the collected articles was conducted. Then they underwent scientometric and bibliometric analyses. The findings revealed that convolutional neural network (CNN) is the mostly employed algorithm, while support vector machine (SVM), random forest, Resnet, DenseNet, and visual geometry group (VGG) were also frequently used. Additionally, China has had a significant presence in the numerous top-ranking categories of this field of research. Both study phases yielded valuable results and rankings. |
| first_indexed | 2024-03-07T22:54:38Z |
| format | Article |
| id | doaj.art-c1fc87b423c948ee91e46779a9cfbaf3 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:19:36Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-c1fc87b423c948ee91e46779a9cfbaf32024-03-09T09:28:52ZengElsevierHeliyon2405-84402024-02-01104e26694A brief review and scientometric analysis on ensemble learning methods for handling COVID-19Mohammad Javad Shayegan0Department of Computer Engineering, University of Science and Culture, Tehran, IranNumerous efforts and research have been conducted worldwide to combat the coronavirus disease 2019 (COVID-19) pandemic. In this regard, some researchers have focused on deep and machine-learning approaches to discover more about this disease. There have been many articles on using ensemble learning methods for COVID-19 detection. Still, there seems to be no scientometric analysis or a brief review of these researches. Hence, a combined method of scientometric analysis and brief review was used to study the published articles that employed an ensemble learning approach to detect COVID-19. This research used both methods to overcome their limitations, leading to enhanced and reliable outcomes. The related articles were retrieved from the Scopus database. Then a two-step procedure was employed. A concise review of the collected articles was conducted. Then they underwent scientometric and bibliometric analyses. The findings revealed that convolutional neural network (CNN) is the mostly employed algorithm, while support vector machine (SVM), random forest, Resnet, DenseNet, and visual geometry group (VGG) were also frequently used. Additionally, China has had a significant presence in the numerous top-ranking categories of this field of research. Both study phases yielded valuable results and rankings.http://www.sciencedirect.com/science/article/pii/S2405844024027257Ensemble learningEnsemble methodDeep learningConvolutional neural networkCOVID-19 |
| spellingShingle | Mohammad Javad Shayegan A brief review and scientometric analysis on ensemble learning methods for handling COVID-19 Heliyon Ensemble learning Ensemble method Deep learning Convolutional neural network COVID-19 |
| title | A brief review and scientometric analysis on ensemble learning methods for handling COVID-19 |
| title_full | A brief review and scientometric analysis on ensemble learning methods for handling COVID-19 |
| title_fullStr | A brief review and scientometric analysis on ensemble learning methods for handling COVID-19 |
| title_full_unstemmed | A brief review and scientometric analysis on ensemble learning methods for handling COVID-19 |
| title_short | A brief review and scientometric analysis on ensemble learning methods for handling COVID-19 |
| title_sort | brief review and scientometric analysis on ensemble learning methods for handling covid 19 |
| topic | Ensemble learning Ensemble method Deep learning Convolutional neural network COVID-19 |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024027257 |
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