Epidemic Outbreak Prediction with Ensemble of Deep Learning Models
7th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2022
; : 71-76, 2022.
Article
in English
| Scopus | ID: covidwho-2285321
ABSTRACT
The ability of today's technology has proved it's significance and dire need in the world yet again, with COVID-19 being a global pandemic. Various techniques are being incorporated and researches being conducted everyday in order to mitigate this pandemic. Forecasting of COVID-19 cases is one such task in machine learning which is being researched intensively to develop reliable forecasting models.In the proposed work, we have forecasted the number of COVID-19 confirmed,recovered and death cases globally using time series data with machine learning and deep learning ensemble models. The purpose of this study is to prove that ensemble of several week learners that we have developed can result in a better performing model. Deep learning models always tend to perform better than machine learning and traditional linear models due to their non-linearity. Our study concludes that deep learning ensemble model achieves better performance than the machine learning ensemble (Random forest) and the individual base learners used in ensemble model itself in COVID-19 forecasting. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
7th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2022
Year:
2022
Document Type:
Article
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