A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-01, Vol.23 (3), p.1533
Main Authors: Soto, Ismael, Zamorano-Illanes, Raul, Becerra, Raimundo, Palacios Játiva, Pablo, Azurdia-Meza, Cesar A, Alavia, Wilson, García, Verónica, Ijaz, Muhammad, Zabala-Blanco, David
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Art techniques
Artificial intelligence
BER
Communication
Communication channels
Coronaviruses
COVID-19
COVID-19 - diagnosis
CSK
Disease transmission
Dust
Electrophoresis
Epidemics
Health aspects
Hospitals
Humans
Light
Machine Learning
Methods
Mining
Nucleic acids
Optical communication
Pandemics
Particle size
Pathogens
Public health
QAM
Severe acute respiratory syndrome coronavirus 2
Signal processing
Vaccines
VLC
Wireless communications
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23031533