dc.creatorSoto, Ismael
dc.creatorZamorano-Illanes, Raul
dc.creatorBecerra, Raimundo
dc.creatorPalacios Játiva, Pablo
dc.creatorAzurdia-Meza, Cesar A.
dc.creatorAlavia, Wilson
dc.creatorGarcía, Verónica
dc.creatorIjaz, Muhammad
dc.creatorZabala-Blanco, David
dc.date2023-03-21T20:05:12Z
dc.date2023-03-21T20:05:12Z
dc.date2023
dc.date.accessioned2024-05-02T20:30:43Z
dc.date.available2024-05-02T20:30:43Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/4530
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9274773
dc.descriptionThis 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.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceSensors, 23(3), 1533
dc.subjectCOVID-19
dc.subjectCSK
dc.subjectQAM
dc.subjectVLC
dc.subjectBER
dc.titleA new COVID-19 detection method based on CSK/QAM visible light communication and machine learning
dc.typeArticle


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