Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer<strong> </strong>
An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient (r) values. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task..
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Preprint| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Preprints.org - (2020) vom: 26. Okt. Zur Gesamtaufnahme - year:2020 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ardabili, Sina [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.20944/preprints202010.0519.v1 |
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| PPN (Katalog-ID): |
preprintsorg019218796 |
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| 245 | 1 | 0 | |a Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer<strong> </strong> |
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| 520 | |a An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient (r) values. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task. | ||
| 700 | 1 | |a Mosavi, Amir |e verfasserin |4 aut | |
| 700 | 1 | |a Band, Shahab S. |e verfasserin |4 aut | |
| 700 | 1 | |a Varkonyi-Koczy, Annamaria R. |e verfasserin |4 aut | |
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