2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 1 , PP: 238-252, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Esraa M El-mohdy 1 , A. F. Elgamal 2 , W. K. Elsaid 3 *
Doi: https://doi.org/10.54216/FPA.170118
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.
Institutions , Epidemics, CNN , MF-STFT , Crisis , CT, X-Ray
[1] Dhama, K., Khan, S., et al. (2022). Coronavirus Disease 2019–COVID 19. Clinical Microbiology Reviews, 33(4), e00028-20.
[2] Jamison, D. T., Gelband, H., Horton, S., et al. (2017). Disease Control Priorities: Improving Health and Reducing Poverty (3rd Ed.). Washington, DC: The International Bank for Reconstruction and Development.
[3] Eftekhari, A., Alipour, M., et al. (2021). A Comprehensive Review of Detection Methods for SARS-CoV-2. Microorganisms, 9(2), 23.
[4] ALQARALEH, S. (2021). Efficient Turkish text classification approach for crisis management systems. GU Journal of Science, 34(3), 718-731.
[5] ÖZTÜRK, D. G., & ÖZBALCI, A. A. (2021). Crisis management during the COVID-19 pandemic. ResearchGate.
[6] Park, Y.-E. (2021). Developing a COVID-19 crisis management strategy using news media and social media in big data analytics. Social Science Computer Review.
[7] Javed, M. L., & Niazi, H. K. (2015). Crisis preparedness and response for schools: An analytical study of Punjab, Pakistan. Journal of Education and Practice, 6(22), 40-47.
[8] Gomez-Gonzalez, E., Gomez, E., Marquez-Rivas, J., et al. (2020). Artificial intelligence in medicine and healthcare: A review and classification of current and near-future applications and their ethical and social impact.
[9] Tokel, A., & Ozkan, T., et al. (2017). Crisis management skills of school administrators in terms of school improvement: Scale development. EURASIA Journal of Mathematics, Science and Technology Education, 13(10), 7573-7579.
[10] Sharma, S., Kumari, B., Ali, A., Yadav, R. K., Sharma, A. K., Sharma, K. K., Hajela, K., & Singh, G. K. (2022). Mobile technology: A tool for healthcare and a boon in pandemic. Journal of Family Medicine and Primary Care, 11(1), 37-43.
[11] Jain, G., Mittal, D., Thakur, D., & Mittal, M. K. (2020). A deep learning approach to detect COVID-19 coronavirus with X-ray images. Biocybernetics and Biomedical Engineering, 40(4), 1391-1405.
[12] Bader, M., Shahin, I., & Hassan, A. (2020). Studying the similarity of COVID-19 sounds based on correlation analysis of MFCC. Proceedings of the IEEE Conference on Control Conference and Intelligent Systems (CCCI).
[13] Eftekhari, S. M., Etemadi, M., & Hosseini, S. M. (2021). The approach of a new model of earthquake crisis management in the classification of vital arteries. Health in Emergencies and Disasters Quarterly, 7(1), 33-38.
[14] Giełczyk, A., Marciniak, A., Tarczewska, M., & Lutowski, Z. (2022). Pre-processing methods in chest X-ray image classification. PLoS ONE, 17(4), e0265949.
[15] Costa, D. G., Peixoto, J. P. J., Jesus, T. C., Portugal, P., Vasques, F., Rangel, E., & Peixoto, M. (2022). A survey of emergencies management systems in smart cities. IEEE Access.
[16] Khalifa, M., Al Baz, M., & Mutta, A. K. (2022). Impact of applying artificial intelligence on human resources crisis management: An analytical study on COVID-19. Information Sciences Letters, 11(1), 269-276.
[17] Parker, M., & Alfaro, P. (2022). Education during the COVID-19 pandemic: Inclusion and psychosocial support (Studies and Perspectives series-ECLAC Subregional Headquarters for the Caribbean, No. 104, LC/TS.2021/211-LC/CAR/TS.2021/6). Economic Commission for Latin America and the Caribbean (ECLAC).
[18] Tiwari, S., & Jain, A. (2022). A lightweight capsule network architecture for detection of COVID-19 from lung CT scans. International Journal of Imaging Systems and Technology, 32(3), 419-434.
[19] Fan, X., Feng, X., Dong, Y., & Hou, H. (2022). COVID-19 CT image recognition algorithm based on transformer and CNN. Displays, 72, 102150.
[20] Bravo, P., Martinez-Pereira, A., Fernández-González, L., et al. (2023). What is needed to effectively communicate risk during a health crisis? A qualitative study with international experts based on the COVID-19 pandemic. BMJ Open, 13, e067531.
[21] Raimondi, S., Gandini, S., Rubio Quintanares, G. H., et al. (2023). European Cohorts of Patients and Schools to Advance Response to Epidemics (EuCARE): A cluster randomized interventional and observational study protocol to investigate the relationship between schools and SARS-CoV-2 infection. BMC Infectious Diseases.
[22] Xu, Y., Lam, H.-K., Jia, G., Jiang, J., Liao, J., & Bao, X. (2023). Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation. Computers in Biology and Medicine, 152, 106417.
[23] Furriel, B. C. R. S., Oliveira, B. D., Prôa, R., Paiva, J. Q., Loureiro, R. M., Calixto, W. P., Reis, M. R. C., & Giavina-Bianchi, M. (2024). Artificial intelligence for skin cancer detection and classification for clinical environment: A systematic review. Frontiers in Medicine, 10, 1305954.
[24] Yu, N., Li, W., Kang, Q., Xiong, Z., Wang, S., Lin, X., Liu, Y., Xiao, J., Liu, H., Deng, D., et al. (2020). Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study. Lancet Infectious Diseases, 20(5), 559-564.
[25] Abdelaziz, Ahmed. N., & Alia. (2022). Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion. Fusion: Practice and Applications, 8(2), 08-15.
[26] Alam, N.-A., Ahsan, M., Based, M. A., Haider, J., & Kowalski, M. (2021). COVID-19 detection from chest X-ray images using feature fusion and deep learning. Sensors, 21(4), 1480.
[27] NUCLEOTYPE. (2020). Vital signs - (Heart rate, blood pressure, respiratory rate, oxygen saturation, and temperature).
[28] Wang, S., Ding, S., & Xiong, L. (2020). A new system for surveillance and digital contact tracing for COVID-19: spatiotemporal reporting over network and GPS. JMIR mHealth and uHealth, 8(6), e19457.
[29] Wang, S., Ding, S., & Xiong, L. (2020). A new system for surveillance and digital contact tracing for COVID-19: spatiotemporal reporting over network and GPS. JMIR mHealth and uHealth, 8(6), e19457.
[30] Sevi, M., & Aydin, İ. (2020, October). COVID-19 detection using deep learning methods. In 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) (pp. 1-6). IEEE.
[31] Townsend, J. T. (1971). Theoretical analysis of an alphabetic confusion matrix. Perception & Psychophysics, 9, 40-50.
