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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1016/j.eswa.2022.116611
|2 doi
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|a pubmed24n1528.xml
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Verma, Hanuman
|e verfasserin
|4 aut
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| 245 |
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|a Temporal deep learning architecture for prediction of COVID-19 cases in India
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 09.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Elsevier Ltd. All rights reserved.
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|a To combat the recent coronavirus disease 2019 (COVID-19), academician and clinician are in search of new approaches to predict the COVID-19 outbreak dynamic trends that may slow down or stop the pandemic. Epidemiological models like Susceptible-Infected-Recovered (SIR) and its variants are helpful to understand the dynamics trend of pandemic that may be used in decision making to optimize possible controls from the infectious disease. But these epidemiological models based on mathematical assumptions may not predict the real pandemic situation. Recently the new machine learning approaches are being used to understand the dynamic trend of COVID-19 spread. In this paper, we designed the recurrent and convolutional neural network models: vanilla LSTM, stacked LSTM, ED_LSTM, BiLSTM, CNN, and hybrid CNN+LSTM model to capture the complex trend of COVID-19 outbreak and perform the forecasting of COVID-19 daily confirmed cases of 7, 14, 21 days for India and its four most affected states (Maharashtra, Kerala, Karnataka, and Tamil Nadu). The root mean square error (RMSE) and mean absolute percentage error (MAPE) evaluation metric are computed on the testing data to demonstrate the relative performance of these models. The results show that the stacked LSTM and hybrid CNN+LSTM models perform best relative to other models
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|a Journal Article
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|a CNN
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|a COVID-19
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| 650 |
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|a Deep learning
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| 650 |
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|a LSTM
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| 700 |
1 |
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|a Mandal, Saurav
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gupta, Akshansh
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Expert systems with applications
|d 1999
|g 195(2022) vom: 01. Juni, Seite 116611
|w (DE-627)NLM098196782
|x 0957-4174
|7 nnns
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| 773 |
1 |
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|g volume:195
|g year:2022
|g day:01
|g month:06
|g pages:116611
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| 856 |
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|u http://dx.doi.org/10.1016/j.eswa.2022.116611
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