Tavakolian Alireza, Hajati Farshid, Rezaee Alireza, Fasakhodi Amirhossein Oliaei, Uddin Shahadat
Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, N Kargar, 1439957131, Tehran, Iran.
College of Engineering and Science, Victoria University Sydney, 160 Sussex Street, Sydney, NSW 2000, Australia.
Softw Impacts. 2022 Aug;13:100337. doi: 10.1016/j.simpa.2022.100337. Epub 2022 Jun 22.
COVID-19 and swine-origin influenza A (H1N1) are both pandemics that sparked significant concern worldwide. These two viruses have the same symptoms and occur at a collision timeline. Optimized Parallel Inception (OPI) presents a new strategy to screen the COVID-19 from H1N1 with use of only symptoms. In this paper, the process of preprocessing, screening, and specifying feature importance by OPI and particle swarm optimization is presented. Experimental results indicate 98.88 accuracy for screening COVID-19, H1N1, and Neither COVID-19 Nor H1N1.
新型冠状病毒肺炎（COVID-19）和甲型H1N1猪流感都是引发全球广泛关注的大流行病。这两种病毒症状相同，且在同一时间范围内出现。优化并行卷积神经网络（OPI）提出了一种仅利用症状从H1N1中筛查COVID-19的新策略。本文介绍了通过OPI和粒子群优化进行预处理、筛查以及确定特征重要性的过程。实验结果表明，筛查COVID-19、H1N1以及既非COVID-19也非H1N1的准确率为98.88%。
