Abstract
Recently, COVID-19 pandemic has increasingly affected the lives of the world population. Researchers have investigated various ways including conventional approach about the transmission routes of COVID-19 persons. However, it is difficult to track COVID-19 in real-time at anywhere. The paper has presented a novel approach using Self Organizing Maps with Picture Fuzzy Sets for tracking COVID-19 persons. The proposed clustering model has been grouped records of COVID-19 persons together with these rules in order to find similar features of COVID-19 person in large data sets. To confirm the effectiveness of this model, experimental results show that the proposed model has demonstrated by using SOM integrated with these rules for tracking COVID-19 persons.
Keywords
- Clustering approach
- COVID-19 person
- Identifying COVID-19 person
- Deep learning in COVID-19
- Self-Organizing maps
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Acknowledgements
This work was supported by the University of Economics Ho Chi Minh City (UEH), Vietnam under project CS-2021-51.
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Pham, H.V., Nguyen, Q.H. (2022). The Clustering Approach Using SOM and Picture Fuzzy Sets for Tracking Influenced COVID-19 Persons. In: Dang, N.H.T., Zhang, YD., Tavares, J.M.R.S., Chen, BH. (eds) Artificial Intelligence in Data and Big Data Processing. ICABDE 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 124. Springer, Cham. https://doi.org/10.1007/978-3-030-97610-1_42
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DOI: https://doi.org/10.1007/978-3-030-97610-1_42
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