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Coot–Lion optimized deep learning algorithm for COVID-19 point mutation rate prediction using genome sequences

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  • Praveen Gugulothu
  • Raju Bhukya

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.

Suggested Citation

  • Praveen Gugulothu & Raju Bhukya, 2024. "Coot–Lion optimized deep learning algorithm for COVID-19 point mutation rate prediction using genome sequences," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 27(11), pages 1410-1429, August.
    • Handle: RePEc:taf:gcmbxx:v:27:y:2024:i:11:p:1410-1429
    DOI: 10.1080/10255842.2023.2244109
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