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

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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

Authors

P. Santosh Kumar Patra

psantoshkumarpatra1@gmail.com

Biju Patnaik University of Technology, Department of Computer Science and Engineering, India

Biswajit Tripathy

biswajit69@gmail.com

Einstein College of Computer Application and Management, Master of Computer Applications, India

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,

Electrical engineering,

Fundamentals of electrical engineering,

Mechanical engineering,

Fundamentals of mechanical engineering,

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.

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