Predicting Covid-19 Cases using CNN Model

Paul Menounga Mbilong, Asmae El Kassiri, Fatima-Zahra Belouadha, El Bhiri Brahim

Abstract

The prediction of COVD-19 confirmed cases is a complex time-series problem. In the literature, Long Short Time Memory (LSTM) has proven its efficiency to resolve issues related to the time series problems. However, Convolution neural network (CNN) did not been widely used in this aim and is considered as more suitable for imaging processing. Therefore, in this paper, we use it to predict COVID-19 cases and compared it with LSTM in the context of Morocco during the period of confinement. The obtained results which we present and discuss in this article are very promising.

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Paper Citation


in Harvard Style

Menounga Mbilong P., Kassiri A., Belouadha F. and Brahim E. (2020). Predicting Covid-19 Cases using CNN Model.In Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH, ISBN 978-989-758-514-2, pages 217-223. DOI: 10.5220/0010466102170223


in Bibtex Style

@conference{icath20,
author={Paul Menounga Mbilong and Asmae El Kassiri and Fatima-Zahra Belouadha and El Bhiri Brahim},
title={Predicting Covid-19 Cases using CNN Model},
booktitle={Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH,},
year={2020},
pages={217-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010466102170223},
isbn={978-989-758-514-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH,
TI - Predicting Covid-19 Cases using CNN Model
SN - 978-989-758-514-2
AU - Menounga Mbilong P.
AU - Kassiri A.
AU - Belouadha F.
AU - Brahim E.
PY - 2020
SP - 217
EP - 223
DO - 10.5220/0010466102170223