Cardiff Metropolitan University
journal.pone.0274538.pdf (2.15 MB)

Robust and efficient COVID-19 detection techniques: A machine learning approach

Download (2.15 MB)
journal contribution
posted on 2022-09-27, 15:20 authored by Md. Mahadi Hasan, Saba Binte Murtaz, Muhammad Usama Islam, Muhammad Jafar Sadeq, M Jasim UddinM Jasim Uddin

 The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus. 


Published in



Public Library of Science


  • VoR (Version of Record)


Hasan, M.M., Murtaz, S.B., Islam, M.U., Sadeq, M.J. and Uddin, J. (2022) 'Robust and efficient COVID-19 detection techniques: A machine learning approach', Plos one, 17(9), p.e0274538.

Electronic ISSN


Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Jasim Udin

Copyright Holder

  • © The Authors


  • en

Usage metrics

    School of Technologies Research - Journal Articles