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

F. E. Nastiti, Shahrulniza Musa, Eiad Yafi
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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. 
预测新发传染病传播的混合深度学习优化方法
本研究开发了一种新方法,用于预测冠状病毒病(COVID-19)的估计病例数。将长短期记忆(LSTM)神经网络与粒子群优化(PSO)和灰狼优化(GWO)相结合,采用了混合优化算法技术。这项调查利用了印度尼西亚卫生部 2020-2021 年期间的 COVID-19 原始数据。所开发的 LSTM-PSO-GWO 混合优化算法可以提高预测 COVID-19 病毒在印尼传播的性能和准确性。在初始 LSTM 初始权重存在缺陷时,使用混合优化算法有助于克服这些问题,提高模型性能。本研究的结果表明,LSTM-PSO-GWO 模型可作为一种有效、可靠的预测工具,用于评估 COVID-19 病毒在印度尼西亚的传播情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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