Author: Elâ€dosuky, Mohamed A.; Soliman, Mona; Hassanien, Aboul Ella
Title: COVIDâ€19 vs influenza viruses: A cockroach optimized deep neural network classification approach Cord-id: l14gf2en Document date: 2021_2_24
ID: l14gf2en
Snippet: Among Coronavirus, as with many other viruses, receptor interactions are an essential determinant of species specificity, virulence, and pathogenesis. The pathogenesis of the COVIDâ€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 COVIDâ€19 and differentiate between COVIDâ€19 and influenza types A, B, and C. The deep network architecture is inspired using a cockroach optimization a
Document: Among Coronavirus, as with many other viruses, receptor interactions are an essential determinant of species specificity, virulence, and pathogenesis. The pathogenesis of the COVIDâ€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 COVIDâ€19 and differentiate between COVIDâ€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 hyperâ€parameters. COVIDâ€19 sequences are obtained from repository 2019 Novel Coronavirus Resource, and influenza A, B, and C subâ€dataset are obtained from other repositories. Five hundred ninetyâ€four unique genomes sequences are used in the training and testing process with 99% overall accuracy for the classification model.
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