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A new COVID-19 detection method based on CSK/QAM visible light communication and machine learning

dc.contributor.authorSoto, Ismael
dc.contributor.authorZamorano-Illanes, Raul
dc.contributor.authorBecerra, Raimundo
dc.contributor.authorPalacios Játiva, Pablo
dc.contributor.authorAzurdia-Meza, Cesar A.
dc.contributor.authorAlavia, Wilson
dc.contributor.authorGarcía, Verónica
dc.contributor.authorIjaz, Muhammad
dc.contributor.authorZabala-Blanco, David
dc.date.accessioned2023-03-21T20:05:12Z
dc.date.available2023-03-21T20:05:12Z
dc.date.issued2023
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/4530
dc.description.abstractThis 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.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceSensors, 23(3), 1533es_CL
dc.subjectCOVID-19es_CL
dc.subjectCSKes_CL
dc.subjectQAMes_CL
dc.subjectVLCes_CL
dc.subjectBERes_CL
dc.titleA new COVID-19 detection method based on CSK/QAM visible light communication and machine learninges_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.indexacionIsies_CL
dc.ucm.urimdpi.com/1424-8220/23/3/1533es_CL
dc.ucm.doidoi.org/10.3390/s23031533es_CL


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