Among Coronavirus, as with many other viruses, receptor interactions are an essential determinant of species specificity, virulence, and pathogenesis. The pathogenesis of the COVID‐19 depends on the virus's ability to attach to and enter into a suitable human host cell. This paper presents a cockroach optimized deep neural network to detect COVID‐19 and differentiate between COVID‐19 and influenza types A, B, and C. The deep network architecture is inspired using a cockroach optimization algorithm to optimize the deep neural network hyper‐parameters. COVID‐19 sequences are obtained from repository 2019 Novel Coronavirus Resource, and influenza A, B, and C sub‐dataset are obtained from other repositories. Five hundred ninety‐four unique genomes sequences are used in the training and testing process with 99% overall accuracy for the classification model.

中文翻译：

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COVID-19 与流感病毒：蟑螂优化的深度神经网络分类方法

在冠状病毒中，与许多其他病毒一样，受体相互作用是物种特异性、毒力和发病机制的重要决定因素。COVID-19 的发病机制取决于病毒附着并进入合适的人类宿主细胞的能力。本文提出了一种蟑螂优化的深度神经网络，用于检测 COVID-19 并区分 COVID-19 和流感 A、B 和 C 型。深度网络架构的灵感来自使用蟑螂优化算法来优化深度神经网络超参数. COVID-19 序列来自存储库 2019 Novel Coronavirus Resource，流感 A、B 和 C 子数据集来自其他存储库。