Open Access
Issue |
ITM Web Conf.
Volume 57, 2023
Fifth International Conference on Advances in Electrical and Computer Technologies 2023 (ICAECT 2023)
|
|
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Article Number | 01004 | |
Number of page(s) | 11 | |
Section | Software Engineering & Information Technology | |
DOI | https://doi.org/10.1051/itmconf/20235701004 | |
Published online | 10 November 2023 |
- Tiwari, S., Chanak, P., & Singh, S. K. (2023). A review of the machine learning algorithms for covid-19 case analysis. IEEE Transactions on Artificial Intelligence, 4(1), 44–59 [CrossRef] [Google Scholar]
- Zhang, Y., Chen, K., Weng, Y., Chen, Z., Zhang, J., & Hubbard, R. (2022). An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US. Expert Systems with Applications, 198(116882), 116882 [CrossRef] [Google Scholar]
- Ahuja C, “Deep learning-based detection of COVID-19 cases on Twitter”(2021). New Gener. Comput, 189–212. [Google Scholar]
- J. Wang, “A machine learning approach for predicting the spread of infectious diseases on social media”, 2020. [Google Scholar]
- Yeasmin, N., Mahbub, N. I., Baowaly, M. K., Singh, B. C., Alom, Z., Aung, Z., & Azim, M. A. (2022). Analysis and prediction of user sentiment on COVID-19 pandemic using tweets. Big Data and Cognitive Computing, 6(2), 65. [CrossRef] [Google Scholar]
- S. S. Hasan, “Twitter-based prediction of Zika virus outbreak using machine learning algorithms”, 2020. [Google Scholar]
- E. G. Althouse, “Real-time prediction of infectious disease outbreaks using social media data and machine learning”, 2019. [Google Scholar]
- Masum, M., Masud, M. A., Adnan, M. I., Shahriar, H., & Kim, S. (2022). Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management. Socio-Economic Planning Sciences, 80(101249), 101249. [CrossRef] [Google Scholar]
- Bani Baker, Q., Shatnawi, F., & Rawashdeh, S. (2022). Forecasting epidemic diseases with Arabic Twitter data and WHO reports using machine learning techniques. Bulletin of Electrical Engineering and Informatics, 11(2), 738–749. [CrossRef] [Google Scholar]
- Santangelo, O. E., Gentile, V., Pizzo, S., Giordano, D., & Cedrone, F. (2023). Machine learning and prediction of infectious diseases: A systematic review. Machine Learning and Knowledge Extraction, 5(1), 175–198. [CrossRef] [Google Scholar]
- Saleem, F., Al-Ghamdi, A. S. A.-M., Alassafi, M. O., & AlGhamdi, S. A. (2022). Machine Learning, Deep Learning, and mathematical models to analyze forecasting and epidemiology of COVID-19: A Systematic Literature Review. International Journal of Environmental Research and Public Health, 19(9), 5099. [CrossRef] [Google Scholar]
- Tiwari, D., Bhati, B. S., Al-Turjman, F., & Nagpal, B. (2022). Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques. Expert Systems, 39(3), e12714. [CrossRef] [Google Scholar]
- Lamsal, R., Harwood, A., & Read, M. R. (2022). Twitter conversations predict the daily confirmed COVID-19 cases. Applied Soft Computing, 129(109603), 109603. [CrossRef] [Google Scholar]
- Narayan, K., Rathore, H., & Znidi, F. (2022). Using epidemic modeling, machine learning and control feedback strategy for policy management of COVID-19. IEEE Access: Practical Innovations, Open Solutions, 10, 98244–98258. [Google Scholar]
- Abdeldayem, O. M., Dabbish, A. M., Habashy, M. M., Mostafa, M. K., Elhefnawy, M., Amin, L., Al-Sakkari, E. G., Ragab, A., & Rene, E. R. (2022). Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook. The Science of the Total Environment, 803(149834), 149834. [CrossRef] [Google Scholar]
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