A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning.
BER
COVID-19
CSK
QAM
VLC
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
30 Jan 2023
30 Jan 2023
Historique:
received:
24
10
2022
revised:
23
01
2023
accepted:
26
01
2023
entrez:
11
2
2023
pubmed:
12
2
2023
medline:
15
2
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36772574
pii: s23031533
doi: 10.3390/s23031533
pmc: PMC10288941
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : FONDEF
ID : Nr. ID21I10191
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