El-Dosuky Mohamed A, Soliman Mona, Hassanien Aboul Ella
Faculty of Computers and Info Mansoura University Mansoura Egypt.
Faculty of Computer and Artificial intelligence Cairo University Cairo Egypt.
Int J Imaging Syst Technol. 2021 Jun;31(2):472-482. doi: 10.1002/ima.22562. Epub 2021 Feb 24.
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.
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与甲型、乙型和丙型流感。深度网络架构受蟑螂优化算法启发，用于优化深度神经网络的超参数。COVID-19序列来自2019新型冠状病毒资源库，甲型、乙型和丙型流感子数据集来自其他资源库。在训练和测试过程中使用了594个独特的基因组序列，分类模型的总体准确率为99%。
