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- W4318482012 abstract "This article proposes a novel method for detecting coronavirus disease 2019 (COVID-19) in an underground channel using visible light communication (VLC) and machine learning (ML). We present mathematical models of COVID-19 Deoxyribose Nucleic Acid (DNA) gene transfer in regular square constellations using a CSK/QAM-based VLC system. ML algorithms are used to classify the bands present in each electrophoresis sample according to whether the band corresponds to a positive, negative, or ladder sample during the search for the optimal model. Complexity studies reveal that the square constellation N=22i×22i,(i=3) yields a greater profit. Performance studies indicate that, for BER = 10-3, there are gains of -10 [dB], -3 [dB], 3 [dB], and 5 [dB] for N=22i×22i,(i=0,1,2,3), respectively. Based on a total of 630 COVID-19 samples, the best model is shown to be XGBoots, which demonstrated an accuracy of 96.03%, greater than that of the other models, and a recall of 99% for positive values." @default.
- W4318482012 created "2023-01-30" @default.
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- W4318482012 date "2023-01-30" @default.
- W4318482012 modified "2023-09-25" @default.
- W4318482012 title "A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning" @default.
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- W4318482012 doi "https://doi.org/10.3390/s23031533" @default.
- W4318482012 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36772574" @default.
- W4318482012 hasPublicationYear "2023" @default.
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