Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3179 2018 2018 Computer Teacher Department, Faculty of Specific Education, Mansoura University, Egypt W. W. Computer Teacher Department, Faculty of Specific Education, Mansoura University, Egypt A. F. Elgamal Computer Teacher Department, Faculty of Specific Education, Mansoura University, Egypt W. K. Elsaid 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. 2025 2025 238 252 10.54216/FPA.170118 https://www.americaspg.com/articleinfo/3/show/3179