EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 374 (2026) EPJ Web Conf., 374 (2026) 02005 References
Open Access
Issue
EPJ Web Conf.
Volume 374, 2026
1st International Conference on Electronic, Optical Devices and Intelligent Systems (ICEODIS 2026)
Article Number 02005
Number of page(s) 8
Section Artificial Intelligence and Data Science
DOI https://doi.org/10.1051/epjconf/202637402005
Published online 24 June 2026
  1. Lakshmanarao, A., Babu, M. R., & Kiran, T. (2021). An efficient covid19 epidemic analysis and prediction model using machine learning algorithms. International Journal of Online & Biomedical Engineering, 17(11), 176-184. [Google Scholar]
  2. Van, N. T., Irum, S., Abbas, A. F., Sikandar, H., & Khan, N. (2022). Online learning-two side arguments related to mental health. International Journal of Online & Biomedical Engineering, 18(9), 131-143. [Google Scholar]
  3. Ali, L. R., Jebur, S. A., Jahefer, M. M., & Shaker, B. N. (2022). Employing transfer learn ing for diagnosing COVID-19 disease. International Journal of Online & Biomedical Engineering, 18(15), 31-42. [Google Scholar]
  4. Yanagihara, N., Von Leden, H., & Werner-Kukuk, E. (1966). The physical parameters of cough: the larynx in a normal single cough. Acta oto-laryngologica, 61(1-6), 495-510. [Google Scholar]
  5. Dixon, P. C., Dubeau, S., Roy, J. F., & Fournier, P. A. (2025). Automatic cough detection via a multi-sensor smart garment using machine learning. Computers in Biology and Medicine, 191, 110192. [Google Scholar]
  6. Kobayashi, T., Goto, D., Sakaue, Y., Okada, S., & Shiozawa, N. (2025). Cough Detection System using Multi-Sensor Smart Clothing and the Mahalanobis-Taguchi System. Advanced Biomedical Engineering, 14, 317-326. [Google Scholar]
  7. Albini, S., Orlandic, L., Dan, J., Thevenot, J., Teijeiro, T., Constantinescu, D. A., & Atienza, D. (2025). Cough-E: A multimodal, privacypreserving cough detection algorithm for the edge. IEEE Journal of Biomedical and Health Informatics. [Google Scholar]
  8. Paul, A., Srivastava, A., Verma, N., Iqbal, A., Maity, S. K., & Das, H. S. (2025). Whooping Cough Prediction using Edge Artificial Intelligence for Real-Time Health Monitoring. ES General, 9, 1649. [Google Scholar]
  9. Garrido, L. F., Rodrigues, G. S., Costa, L. B., Kurtz, D. J., & Daros, R. R. (2025). Validation of a Swine Cough Monitoring System Under Field Conditions. AgriEngineering, 7(5), 140. [Google Scholar]
  10. Barkani, F., Hamidi, M., Zealouk, O., & Satori, H. (2023). Speech Recognition Algorithms-Based Cough Recognition System. International Journal of Online & Biomedical Engineering, 19(12). [Google Scholar]
  11. Hamidi, M., Zealouk, O., Satori, H., Laaidi, N., &Salek, A. (2023). COVID-19 assessment using HMM cough recognition system. International Journal of Information Technology, 15(1), 193201. [Google Scholar]
  12. https://www.health.harvard.edu/stayinghealthy/that-nagging-cough [Google Scholar]
  13. Purwono, P., Maarif, A., Rahmaniar, W., Fathurrahman, H. I. K., Frisky, A. Z. K., & ulHaq, Q. M. (2023). Understanding of convolutional neural network (cnn): A review. International Journal of Robotics and Control Systems, 2(4), 739-748. [Google Scholar]
  14. Ibrahim, A. B., Seddiq, Y. M., Meftah, A. H., Alghamdi, M., Selouani, S. A., Qamhan, M. A., … & Alshebeili, S. A. (2020). Optimizing arabic speech distinctive phonetic features and phoneme recognition using genetic algorithm. IEEE Access, 8, 200395-200411. [Google Scholar]
  15. Qi, J., Wang, D., Xu, J., & Tejedor, J. (2013, August). Bottleneck features based on gammatone frequency cepstral coefficients. In Interspeech (pp. 1751-1755). [Google Scholar]
  16. Lesnichaia, M., Mikhailava, V., Bogach, N., Lezhenin, Y., Blake, J., & Pyshkin, E. (2022, September). Classification of Accented English Using CNN Model Trained on Amplitude Mel-Spectrograms. In Interspeech (pp. 3669-3673). [Google Scholar]
  17. Peng, P., Jiang, K., You, M., Xie, J., Zhou, H., Xu, W., … & Xu, Y. (2023). Design of an efficient CNN-based cough detection system on lightweight FPGA. IEEE Transactions on Biomedical Circuits and Systems, 17(1), 116-128. [Google Scholar]
  18. Mariappan, R. (2023, January). Early detection of covid using spectral analysis of cough and deep convolutional neural network. In International Conference on Distributed Computing and Intelligent Technology (pp. 197-207). Cham: Springer Nature Switzerland. [Google Scholar]
  19. Bensid, K., Lati, A., Benlamoudi, A., Ghouar, B. E., & Senoussi, M. L. (2023). Efficient Covid-19 disease diagnosis based on cough signal processing and supervised machine learning. Diagnostyka, 24. [Google Scholar]
  20. Jyothi, N. M., & Madhusudhanan, S. (2023). Cough audio signal-based clinical emergency classification of corona variant infected patients using multiclass SVM. In Intelligent Systems and Applications: Select Proceedings of ICISA 2022 (pp. 333-350). Singapore: Springer Nature Singapore. [Google Scholar]
  21. Tawfik, M., Nimbhore, S., Al-Zidi, N. M., Ahmed, Z. A., & Almadani, A. M. (2022, January). Multifeatures extraction for automating COVID-19 detection from cough sound using deep neural networks. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 944-950). IEEE. [Google Scholar]
  22. Jayadi, A., Prasetio, B. H., Akbar, S. R., Widasari, E. R., & Syauqy, D. (2022, November). Embedded flu detection system based cough sound using mfcc and knn algorithm. In 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA) (pp. 1-5). IEEE. [Google Scholar]
  23. Drugman, T., Urbain, J., Bauwens, N., Chessini, R., Aubriot, A. S., Lebecque, P., & Dutoit, T. (2020). Audio and contact microphones for cough detection. arXiv preprint arXiv:2005.05313. [Google Scholar]
  24. Bansal, V., Pahwa, G., & Kannan, N. (2020, October). Cough Classification for COVID-19 based on audio mfcc features using Convolutional Neural Networks. In 2020 IEEE international conference on computing, power and communication technologies (GUCON) (pp. 604608). IEEE. [Google Scholar]
  25. Barkani, F., Hamidi, M., Zealouk, O., & Satori, H. (2023). Speech Recognition Algorithms-B ased Cough Recognition System. International Journal of Online & Biomedical Engineering, 19(12). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.