Deep learning for Covid-19 forecasting: State-of-the-art review.
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Deep learning for Covid-19 forecasting: State-of-the-art review.
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| Автор |
Kamalov, F.
Rajab, K. Cherukuri, A. K. Elnagar, A. Safaraliev, M. |
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| Тематика |
CNN
COVID-19 DEEP LEARNING FORECASTING GNN LSTM MLP SURVEY FORECASTING LEARNING SYSTEMS LONG SHORT-TERM MEMORY 'CURRENT COVID-19 DEEP LEARNING GNN GOOGLE SCHOLAR LEARNING METHODS LSTM MACHINE LEARNING METHODS MLP STATE-OF-THE ART REVIEWS ARTICLE CONVOLUTIONAL NEURAL NETWORK CORONAVIRUS DISEASE 2019 DEEP LEARNING FORECASTING GRADIENT BOOSTING GRAPH NEURAL NETWORK HUMAN K NEAREST NEIGHBOR LONG SHORT TERM MEMORY NETWORK MATHEMATICAL MODEL MULTILAYER PERCEPTRON RANDOM FOREST RECURRENT NEURAL NETWORK SUPPORT VECTOR MACHINE SUSCEPTIBLE EXPOSED INFECTIOUS RECOVERED MODEL TAXONOMY SURVEYS |
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| Описание |
The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. © 2022 Elsevier B.V.
Dunarea de Jos” University of Galati Umm Al-Qura Univer-sity Umm Al-Qura University, UQU, (22UQU4300274DSR01) Deanship of Scientific Research, King Saud University Funding text 1: Deanship of Scientific Research at Umm Al-Qura University supported this work by Grant Code: (22UQU4300274DSR01). Funding text 2: Conceptualization, H.O.T., H.M.H.Z., A.M.A.M., G.A. and S.A.M.I. methodology, F.T.A. and H.O.T. software, D.S.B., A.H.A., H.M.H.Z. and A.E. validation, S.A.M.I., A.M.A.M., D.S.B., D.E.A., W.E., Y.S.R. and A.E. formal analysis, H.M.H.Z., and F.T.A. investigation, H.O.T., W.E., and G.A. resources, F.T.A. and D.S.B. data curation, S.A.M.I., A.H.A. and A.E. writing—original draft preparation, Y.S.R., D.E.A., H.O.T., D.E.A., F.T.A. and A.E. writing—review and editing, H.M.H.Z., S.I, A.M.A.M., A.H.A. and A.E. visualization, W.E. and A.E. supervision, H.M.H.Z., W.E., Y.S.R. and D.S.B. project administration, H.O.T., A.E., Y.S.R. and S.A.M.I. funding acquisition A.E. (The APC was funded by “Dunarea de Jos” University of Galati, Romania). All authors have read and agreed to the published version of the manuscript. Funding text 3: The authors would like to thank the Deanship of Scientific Research at the Umm Al-Qura Univer-sity for supporting this work by grant code (22UQU4300274DSR01). The APC was covered by “Dunarea de Jos” University of Galati, Romania. |
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| Дата |
2024-04-22T15:53:44Z
2024-04-22T15:53:44Z 2022 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) Published version (info:eu-repo/semantics/publishedVersion) |
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| Идентификатор |
Kamalov, F, Rajab, K, Cherukuri, AK, Elnagar, A & Safaraliev, M 2022, 'Deep learning for Covid-19 forecasting: State-of-the-art review', Neurocomputing, Том. 511, стр. 142-154. https://doi.org/10.1016/j.neucom.2022.09.005
Kamalov, F., Rajab, K., Cherukuri, A. K., Elnagar, A., & Safaraliev, M. (2022). Deep learning for Covid-19 forecasting: State-of-the-art review. Neurocomputing, 511, 142-154. https://doi.org/10.1016/j.neucom.2022.09.005 0925-2312 Final All Open Access; Green Open Access https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454152 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454152 http://elar.urfu.ru/handle/10995/132485 10.1016/j.neucom.2022.09.005 85138086201 871948700012 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by |
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| Формат |
application/pdf
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| Издатель |
Elsevier B.V.
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| Источник |
Neurocomputing
Neurocomputing |
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