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• W4386438869 abstract "In this study, a deep quantum neural network (DQNN) based on the Lion-based Coot algorithm (LBCA-based Deep QNN) is employed to predict COVID-19. Here, the genome sequences are subjected to feature extraction. The fusion of features is performed using the Bray-Curtis distance and the deep belief network (DBN). Lastly, a deep quantum neural network (Deep QNN) is used to predict COVID-19. The LBCA is obtained by integrating Coot algorithm and LOA. The COVID-19 predictions are done with mutation points. The LBCA-based Deep QNN outperformed with testing accuracy of 0.941, true positive rate of 0.931, and false positive rate of 0.869." @default.
• W4386438869 created "2023-09-06" @default.
• W4386438869 creator A5007529224 @default.
• W4386438869 creator A5092755779 @default.
• W4386438869 date "2023-09-05" @default.
• W4386438869 modified "2023-09-27" @default.
• W4386438869 title "Coot–Lion optimized deep learning algorithm for COVID-19 point mutation rate prediction using genome sequences" @default.
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• W4386438869 doi "https://doi.org/10.1080/10255842.2023.2244109" @default.
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• W4386438869 hasPublicationYear "2023" @default.
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