A new COVID-19 detection method based on CSK/QAM visible light communication and machine learning
| dc.creator | Soto, Ismael | |
| dc.creator | Zamorano-Illanes, Raul | |
| dc.creator | Becerra, Raimundo | |
| dc.creator | Palacios Játiva, Pablo | |
| dc.creator | Azurdia-Meza, Cesar A. | |
| dc.creator | Alavia, Wilson | |
| dc.creator | García, Verónica | |
| dc.creator | Ijaz, Muhammad | |
| dc.creator | Zabala-Blanco, David | |
| dc.date | 2023-03-21T20:05:12Z | |
| dc.date | 2023-03-21T20:05:12Z | |
| dc.date | 2023 | |
| dc.date.accessioned | 2024-05-02T20:30:43Z | |
| dc.date.available | 2024-05-02T20:30:43Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/4530 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9274773 | |
| dc.description | 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. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | Sensors, 23(3), 1533 | |
| dc.subject | COVID-19 | |
| dc.subject | CSK | |
| dc.subject | QAM | |
| dc.subject | VLC | |
| dc.subject | BER | |
| dc.title | A new COVID-19 detection method based on CSK/QAM visible light communication and machine learning | |
| dc.type | Article |