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Prediction of Covid-19 Cases for Malaysia, Egypt, and USA using Deep Learning Models

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dc.citedby0
dc.contributor.authorHasan R.A.en_US
dc.contributor.authorJamaluddin J.E.en_US
dc.contributor.authorid58487876600en_US
dc.contributor.authorid37080724200en_US
dc.date.accessioned2024-10-14T03:20:38Z
dc.date.available2024-10-14T03:20:38Z
dc.date.issued2023
dc.description.abstractForecasting in pandemics and disasters is one of the means that contribute to reducing the damage of this pandemic, and the Corona virus is reportedly the most dangerous pandemic that the entire world is suffering from. As a result, we aim to use a deep learning algorithm to predict confirmed and new cases of Covid-19 in our study. This paper identifies the most essential deep learning techniques. Long short-term memory (LSTM) and gated recurrent unit (GRU) were shown to forecast verified Covid-19 fatalities in Malaysia, Egypt, and the U.S. using time series data from 1 January 2021 to 14 May 2022. The first section of this study examines a comparison of prediction models, while the second section examines how prediction and performance analysis may be enhanced using mean absolute error (MAE), mean absolute error percentage (MAPE), and root mean squared error (RMSE) Metrics. On the basis of the regression curves of two two-layer models, the data were split into training sets of 80% and test sets of 20%. The conclusion is that the outputs of the training model and the original data greatly converged. The findings of the study indicated that, for predicting Covid-19 cases, the GRU model in the three nations is superior than the LSTM model. �Copyright Hasan.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11113/mjfas.v19n3.2992
dc.identifier.epage428
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85164772690
dc.identifier.spage417
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85164772690&doi=10.11113%2fmjfas.v19n3.2992&partnerID=40&md5=f27a04f1303d224247761af19f4e6c2c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34556
dc.identifier.volume19
dc.pagecount11
dc.publisherPenerbit UTM Pressen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleMalaysian Journal of Fundamental and Applied Sciences
dc.subjectCovid-19
dc.subjectDeep Learning
dc.subjectGRU
dc.subjectLSTM
dc.subjectMalaysia
dc.subjectPrediction
dc.titlePrediction of Covid-19 Cases for Malaysia, Egypt, and USA using Deep Learning Modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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