Soft Computing Models for Accurate COVID-19 Prediction: A Comparative Study
DOI:
https://doi.org/10.48001/jowacs.2024.227-11Keywords:
COVID-19, Machine learning, Mathematical model, Predictive models, Regression tree analysis, VirusesAbstract
The purpose of this research is to explore the effectiveness of soft computing models for accurate COVID-19 prediction by conducting a complete comparison investigation. The implementation of soft computing approaches, such as neural networks, fuzzy logic, and genetic algorithms, is something that we are investigating as a response to the worldwide requirement for accurate forecasting in the continuing epidemic. The study of the relevant literature draws attention to the shortcomings of traditional modelling techniques, so laying the groundwork for the implications and possibilities of soft computing in the field of illness prediction. The selection of a wide variety of soft computing models, the diligent collecting of data, the use of preprocessing methods, and the establishment of a methodical framework for comparative analysis are all components of our approach. The findings and comparison analysis shed light on the unique advantages and disadvantages of each model, providing insights into the overall performance of the models as well as the variables that influence the accuracy of their predictions. According to the results of this research, significant insights have been contributed to the ever-changing environment of COVID-19 prediction, which has ramifications for the process of making informed decisions in the field of public health.
Downloads
References
Adhyapok, S., & Sarma, H. K. D. (2020, March). Review on QoS aware routing protocols for multi-channel wireless sensor network. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 503-509). IEEE.
https://doi.org/10.1109/ICIMIA48430.2020.9074932.
Bharany, S., Sharma, S., Alsharabi, N., Tag Eldin, E., & Ghamry, N. A. (2023). Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization. Frontiers in Marine Science, 10, 1117787.
https://doi.org/10.3389/fmars.2023.1117787.
Chen, J., Li, K., Tang, Z., Bilal, K., Yu, S., Weng, C., & Li, K. (2016). A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Transactions on Parallel and Distributed Systems, 28(4), 919-933. https://doi.org/10.1109/TPDS.2016.2603511.
Dattatray, M. T., & Amrit, M. P. (2014). A study of fault management algorithm and recover the faulty node using the FNR algorithms for wireless sensor network. International Journal of Engineering Research and General Science, 2(6), 590-595.
http://www.ijergs.org/files/documents/A-STUDY-77.pdf
Dutta, A., Peng, G. C. A., & Choudhary, A. (2013). Risks in enterprise cloud computing: The perspective of IT experts. Journal of Computer Information Systems, 53(4), 39-48.
https://doi.org/10.1080/08874417.2013.11645649.
Eastham, J. A., Auffenberg, G. B., Barocas, D. A., Chou, R., Crispino, T., Davis, J. W., ... & Wei Boorjian, S. A. (2022). Clinically localized prostate cancer: AUA/ASTRO guideline, part I: introduction, risk assessment, staging, and risk-based management. The Journal of Urology, 208(1), 10-18.
https://doi.org/10.1097/JU.0000000000002757.
Jiang, T., Fang, H., & Wang, H. (2018). Blockchain-based internet of vehicles: Distributed network architecture and performance analysis. IEEE Internet of Things Journal, 6(3), 4640-4649.
https://doi.org/10.1109/JIOT.2018.2874398.
Levidow, L., Zaccaria, D., Maia, R., Vivas, E., Todorovic, M., & Scardigno, A. (2014). Improving water-efficient irrigation: Prospects and difficulties of innovative practices. Agricultural Water Management, 146, 84-94. https://doi.org/10.1016/j.agwat.2014.07.012.
Mittal, N., Singh, U., & Sohi, B. S. (2017). A stable energy efficient clustering protocol for wireless sensor networks. Wireless Networks, 23, 1809-1821.
https://doi.org/10.1007/s11276-016-1255-6.
Ozhan, O., & Yagin, F. H. (2022). Machine learning approach for classification of prostate cancer based on clinical biomarkers. The Journal of Cognitive Systems, 7(2), 17-20.
https://doi.org/10.52876/jcs.1221425.
Sarki, R., Ahmed, K., Wang, H., & Zhang, Y. (2020). Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Information Science and Systems, 8(1), 32.
https://doi.org/10.1007/s13755-020-00125-5.
Takale, D. D., Sharma, D. Y. K., & SN, P. (2019). A review on data centric routing for wireless sensor network. Journal of Emerging Technologies and Innovative Research (JETIR), 6(1).
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4416491.
Takale, D. G., Gunjal, S. D., Khan, V. N., Raj, A., & Guja, S. N. (2022). Road accident prediction model using data mining techniques. NeuroQuantology, 20(16), 2904.
Downloads
