The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
A review on COVID-19 forecasting models
Open accessAbstract
The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Glover, F., & Laguna, M. (1997) · Cited by 5,679
Multi-column deep neural networks for image classification
Ciresan, D., Meier, U., & Schmidhuber, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248110 · Cited by 3,720
Visualizing Bibliometric Networks
van Eck, N. J., & Waltman, L. (2014) · Measuring Scholarly Impact · doi:10.1007/978-3-319-10377-8_13 · Cited by 3,342
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
Blum, C., & Roli, A. (2003) · ACM Computing Surveys · doi:10.1145/937503.937505 · Cited by 3,236
Glover, F., & Laguna, M. (1997) · doi:10.1007/978-1-4615-6089-0 · Cited by 3,001
Taylor, S. J., & Letham, B. (2018) · The American Statistician · doi:10.1080/00031305.2017.1380080 · Cited by 2,737
Cited by
(2022) · doi:10.1098/rsos.220600 · Cited by 12
Metadata
- Authors
- Rahimi, I., Chen, F., & Gandomi, A. H.
- Venue
- Neural Computing and Applications
- Published
- 2021-02-04
- Volume
- 35
- Issue
- 33
- Pages
- 23671-23681
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W3115894432
- Cited by
- 282
- References
- 76
- FWCI
- 21.67
- Type
- journal article
- Language
- en
- Open access
- bronze
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
