Version 1
: Received: 22 October 2020 / Approved: 26 October 2020 / Online: 26 October 2020 (11:57:14 CET)
How to cite:
Ardabili, S.; Mosavi, A.; Band, S.S.; Varkonyi-Koczy, A.R. Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer. Preprints2020, 2020100519. https://doi.org/10.20944/preprints202010.0519.v1
Ardabili, S.; Mosavi, A.; Band, S.S.; Varkonyi-Koczy, A.R. Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer . Preprints 2020, 2020100519. https://doi.org/10.20944/preprints202010.0519.v1
Ardabili, S.; Mosavi, A.; Band, S.S.; Varkonyi-Koczy, A.R. Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer. Preprints2020, 2020100519. https://doi.org/10.20944/preprints202010.0519.v1
APA Style
Ardabili, S., Mosavi, A., Band, S.S., & Varkonyi-Koczy, A.R. (2020). Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer<strong> </strong>. Preprints. https://doi.org/10.20944/preprints202010.0519.v1
Chicago/Turabian Style
Ardabili, S., Shahab S. Band and Annamaria R. Varkonyi-Koczy. 2020 "Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer<strong> </strong>" Preprints. https://doi.org/10.20944/preprints202010.0519.v1
Abstract
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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
