Artificial intelligence assisted photonic bio sensing for rapid bacterial diseases
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Rajeswari Periyasamy
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Smitha Sasi
Abstract
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.
Acknowledgments
Authors sincerely appreciate the support and guidance received during this work. Authors extend gratitude to mentors, colleagues, and collaborators for their valuable insights and encouragement. Special thanks to Dayananda Sagar College of engineering, Bengaluru, India and Sandip Institute of Technology. & Research Centre Nashik, Maharashtra, India, for providing the necessary resources and facilities.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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© 2025 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Atomic, Molecular & Optical Physics
- Artificial intelligence assisted photonic bio sensing for rapid bacterial diseases
- A compact analytical solution of the Dicke superradiance master equation via residue calculus
- Chemical Physics
- Extremal linear triangular chains with respect to the Kirchhoff index
- Hydrodynamics & Plasma Physics
- Heat and mass transfer enhancement for MHD nanofluid coupled to elastic interface
- Effect of Combined Kappa–Cairns distributed electrons on ion-acoustic solitary structures in electron–ion dusty plasma
- The solitary periodic soliton and formation of shock with phase shift in multi-component unmagnetized dusty plasmas
- Study of propagation dynamics of intense Laguerre–Gaussian laser beam in preformed plasma channel created by ignitor–heater method
- Solid State Physics & Materials Science
- Effects of substrate bias on the properties of TiCrN films deposited on 316L by RF magnetron sputtering
- Generalized Debye scattering formula for strained amorphous materials
Articles in the same Issue
- Frontmatter
- Atomic, Molecular & Optical Physics
- Artificial intelligence assisted photonic bio sensing for rapid bacterial diseases
- A compact analytical solution of the Dicke superradiance master equation via residue calculus
- Chemical Physics
- Extremal linear triangular chains with respect to the Kirchhoff index
- Hydrodynamics & Plasma Physics
- Heat and mass transfer enhancement for MHD nanofluid coupled to elastic interface
- Effect of Combined Kappa–Cairns distributed electrons on ion-acoustic solitary structures in electron–ion dusty plasma
- The solitary periodic soliton and formation of shock with phase shift in multi-component unmagnetized dusty plasmas
- Study of propagation dynamics of intense Laguerre–Gaussian laser beam in preformed plasma channel created by ignitor–heater method
- Solid State Physics & Materials Science
- Effects of substrate bias on the properties of TiCrN films deposited on 316L by RF magnetron sputtering
- Generalized Debye scattering formula for strained amorphous materials
