Volume 17 • Issue 1 • PP: 238-252 • 2025
Proposing a Mobile Application for Educational Institutions' Support during Epidemic Crises
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This study proposes an intelligent system designed to detect and manage epidemic outbreaks within institutional settings by leveraging a fusion of advanced AI technologies. The system operates through five key stages: symptom-based diagnostic testing, AI-powered cough detection, analysis of X-ray and CT scan images using Convolutional Neural Networks (CNN), evaluation of vital signs, and the geolocation of COVID-19 patients using GPS. Cough detection is enhanced by integrating Short-Time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCC). Trained on an extensive dataset comprising over 5,856 CT scans, 7135 X-ray images, and over 30,000 crowdsourced cough recordings, the system demonstrates a high accuracy rate of 95% in identifying potential epidemic cases. This fusion of techniques offers a robust solution for early detection and rapid intervention, significantly mitigating the risk of widespread transmission within high-density environments.
Keywords
References
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