Proposing a Mobile Application for Educational Institutions' Support during Epidemic Crises

Esraa M El-mohdy¹, A. F. Elgamal¹ , W.K. Elsaid^1,*

¹Computer Teacher Department, Faculty of Specific Education, Mansoura University, Egypt

Emails: esraaelmohdy@mans.edu.eg; amany_elgamal@mans.edu.eg; prof_wessam@yahoo.com

Abstract

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: Institutions; Epidemics, CNN; MF-STFT; Crisis; CT, X-Ray