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A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning.
Soto, Ismael; Zamorano-Illanes, Raul; Becerra, Raimundo; Palacios Játiva, Pablo; Azurdia-Meza, Cesar A; Alavia, Wilson; García, Verónica; Ijaz, Muhammad; Zabala-Blanco, David.
Afiliação
  • Soto I; CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile.
  • Zamorano-Illanes R; CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile.
  • Becerra R; Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile.
  • Palacios Játiva P; Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile.
  • Azurdia-Meza CA; Escuela de Informática y Telecomunicaciones, Universidad Diego Portales, Santiago 8370190, Chile.
  • Alavia W; Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile.
  • García V; CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile.
  • Ijaz M; Departamento en Ciencia y Tecnología de los Alimentos, de la Universidad de Santiago de Chile, Santiago 9170124, Chile.
  • Zabala-Blanco D; Manchester Metropolitan University, Manchester M1 5GD, UK.
Sensors (Basel) ; 23(3)2023 Jan 30.
Article em En | MEDLINE | ID: mdl-36772574
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Chile País de publicação: Suíça
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Chile País de publicação: Suíça