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Predicting COVID-19 cases using bidirectional LSTM on multivariate time series
To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short...
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| Publicado no: | Environ Sci Pollut Res Int |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Springer Berlin Heidelberg
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8155803/ https://ncbi.nlm.nih.gov/pubmed/34043172 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s11356-021-14286-7 |
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