Artificial intelligence assisted photonic bio sensing for rapid bacterial diseases.
Combining artificial intelligence (AI) and photonic biosensors is a new method of high-accuracy bacterial detection. In the present work, a decision tree classifier is used, aimed at the classification of bacterial species by taking readings from the wavelength measurements extracted from photonic s...
| Publicado en: | Zeitschrift für Naturforschung Section A: A Journal of Physical Sciences Vol. 80; no. 8; pp. 665 - 672 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
De Gruyter
Aug2025
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=187123268&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 187123268 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 09320784 FL07 jtl: Zeitschrift für Naturforschung Section A: A Journal of Physical Sciences issn: 09320784 maglogo: N pubinfo: dt: Aug2025 vid: 80 iid: 8 pid: 1734 pub: De Gruyter artinfo: ui: 187123268 10.1515/zna-2025-0076 ppf: 665 ppct: 7 formats: tig: atl: Artificial intelligence assisted photonic bio sensing for rapid bacterial diseases. aug: au: Periyasamy, Rajeswari Sasi, Smitha Malagi, Vindhya P. Shivaswamy, Rashmi Chikkaiah, Jayanth Pathak, Ranjeet Kumar affil: Department of Electronics and Telecommunication Engineering, 302613 Dayananda Sagar College of Engineering, Bengaluru, India Department of Artificial Intelligence and Machine Learning, 302613 Dayananda Sagar College of Engineering, Bangalore, India Department of Computer Science and Engineering, (Data Science), Bengaluru, India Department of Electronics and Telecommunication Engineering, Sandip Institute of Technology & Research Centre, Nashik, Maharashtra, India su: Probability density function Artificial intelligence Bacteria classification Bacterial diseases Feature selection sug: subj: Probability density function Artificial intelligence Bacteria classification Bacterial diseases Feature selection keyword: artificial intelligence bacteria Kernel density estimation multi classification photonic crystal ab: Combining artificial intelligence (AI) and photonic biosensors is a new method of high-accuracy bacterial detection. In the present work, a decision tree classifier is used, aimed at the classification of bacterial species by taking readings from the wavelength measurements extracted from photonic sensor simulations performed using Rsoft. The data set is processed through univariate analysis, Kernel density estimation (KDE) and box plot evaluation, and optimized feature selection as well as outlier removal. The classifier is trained with a 70.27 % classification accuracy. Performance evaluation using a confusion matrix highlighted the classification efficiency. The obtained findings show the promise of AI based photonic bio sensing for the bacterial infectious diseases. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
|---|