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Design and Analysis of 2D Photonic Biosensor with ML for Respiratory Virus Detection

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Title Design and Analysis of 2D Photonic Biosensor with ML for Respiratory Virus Detection
 
Creator S, Vishalatchi
Murugan, Kalpana
R, Nagaraj
N, Gayathri H
 
Subject Naïve Bayes
Sensor
Virus
2D PhC
Hexagonal ring resonator
Sensitivity
Quality factor
Respiratory virus
 
Description 614-621
In this study, we have designed and integrated a novel photonic biosensor with a Machine Learning approach for the
detection of five common respiratory viruses. The biosensor has been developed using a two-dimensional hexagonal
photonic crystal defect structure, which has been designed through the use of Finite Difference Time Domain (FDTD) and
Plane Wave Expansion (PWE) techniques to monitor wavelength shifts during virus detection. The analytes have been
efficiently captured within the sensor's pores to optimize performance. The uniqueness of our sensor has been demonstrated
through enhanced sensitivity (584nm/RIU) and a remarkable quality factor (9734). We have employed the naïve Bayes
classifier Machine Learning algorithm to achieve accurate virus detection, leveraging parameters that have been extracted
from the sensor design. Our integrated sensor and classifier have provided robust classification of virus types, outperforming
existing methods, and yielding highly accurate results. Furthermore, to enhance user accessibility, we have developed a
graphical user interface for intuitive result interpretation.
 
Date 2023-11-21T04:52:55Z
2023-11-21T04:52:55Z
2023-11
 
Type Article
 
Identifier 0971-4588 (Print); 0975-1017 (Online)
http://nopr.niscpr.res.in/handle/123456789/62914
https://doi.org/10.56042/ijems.v30i4.2520
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source IJEMS Vol.30(4) [August 2023]