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Advancing COVID-19 diagnostics with explainable AI techniques from IoT-driven gene expression data Cover

Advancing COVID-19 diagnostics with explainable AI techniques from IoT-driven gene expression data

Control and Cybernetics
Volume 53 (2024): Issue 4 (December 2024)
By: P. Santosh Kumar Patra and  Biswajit Tripathy  
Open Access
|Aug 2025
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DOI: https://doi.org/10.2478/candc-2024-0023 | Journal eISSN: 2720-4278 | Journal ISSN: 0324-8569
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Language: English
Page range: 569 - 618
Submitted on: Oct 1, 2024
Accepted on: Apr 1, 2025
Published on: Aug 26, 2025
Published by: Systems Research Institute Polish Academy of Sciences
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
explainable AI,
COVID-19 classification,
gene expression,
IoT San Francisco dataset,
feature engineering,
convolutional liquid neural network,
particle swarm optimization
Related subjects:
Computer sciences,
Computer sciences, other,
Engineering,
Electrical engineering,
Fundamentals of electrical engineering,
Mechanical engineering,
Fundamentals of mechanical engineering,
Mathematics,
General mathematics

© 2025 P. Santosh Kumar Patra, Biswajit Tripathy, published by Systems Research Institute Polish Academy of Sciences
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 53 (2024): Issue 4 (December 2024)
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