A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease

Nastiti, FE; Musa, S; Yafi, E

A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease

Nastiti, FE Musa, S Yafi, E

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Publisher:: Institute of Advanced Engineering and Science
Publication Type:: Journal Article
Citation:: IAES International Journal of Artificial Intelligence, 2024, 13, (2), pp. 2034-2046
Issue Date:: 2024-06-01

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Field	Value	Language
dc.contributor.author	Nastiti, FE
dc.contributor.author	Musa, S
dc.contributor.author	Yafi, E https://orcid.org/0000-0002-3144-0784
dc.date.accessioned	2025-03-04T23:46:49Z
dc.date.available	2025-03-04T23:46:49Z
dc.date.issued	2024-06-01
dc.identifier.citation	IAES International Journal of Artificial Intelligence, 2024, 13, (2), pp. 2034-2046
dc.identifier.issn	2089-4872
dc.identifier.issn	2252-8938
dc.identifier.uri	http://hdl.handle.net/10453/185505
dc.description.abstract	In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in Indonesia. In initiating LSTM initial weights with weaknesses, using the hybrid optimization algorithm helps overcome these problems and improve model performance. The results of this study suggest that the LSTM-PSO-GWO model can be utilized as an effective and reliable predictive tool to gauge the COVID-19 virus’s spread in Indonesia.
dc.language	en
dc.publisher	Institute of Advanced Engineering and Science
dc.relation.ispartof	IAES International Journal of Artificial Intelligence
dc.relation.isbasedon	10.11591/ijai.v13.i2.pp2036-2048
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease
dc.type	Journal Article
utslib.citation.volume	13
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/
dc.date.updated	2025-03-04T23:46:47Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	2

Abstract:

In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in Indonesia. In initiating LSTM initial weights with weaknesses, using the hybrid optimization algorithm helps overcome these problems and improve model performance. The results of this study suggest that the LSTM-PSO-GWO model can be utilized as an effective and reliable predictive tool to gauge the COVID-19 virus’s spread in Indonesia.

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http://hdl.handle.net/10453/185505