THERMAL SCIENCE

International Scientific Journal

Safar M. Alghamdi

,

Sharaf Obaid Ali

,

Maha A. Aldahlan

,

Gaafar Mohamed Abdalkrim

,

Azhari A. Alhag

USING FUZZY TIME SERIES FORECASTING AND GAUSSIAN MIXTURE MODEL TO CLASSIFY AND PREDICT NEW CASES OF COVID-19 IN SAUDI ARABIA

ABSTRACT
In light of the global events resulting from the spread of the Corona pandemic and viral mutations, there is a need to examine epidemic data in terms of numbers of infected and deaths, different geographical locations, and the dynamics of disease dissemination virus. In the Kingdom of Saudi Arabia (KSA), since the spread of the virus on March 2, 2020, the number of confirmed cases has increased to 599044 cases until January 13, 2022, of which 262 are critical cases, while the number of recovery cases have reached 55035 cases, and deaths are 8901. It is a serious disease, and its spread is difficult to contain. The number of cases has continued to grow rapidly since the first cases appeared. Guess and Buck's model for forecasting time-series data is an important figure that cannot be crossed when predicting fuzzy time-series, although several modifications have been made to the model to improve the accuracy of its results. The Gaussian mixture model and the fuzzy method for modelling new cases in Saudi Arabia were used as machine learning methods to classify and predict new cases of the virus in Saudi Arabia. Foggy time series forecasting. The studied datasets from the World Health Organization from May 15 to August 12, 2020 were used.
KEYWORDS
Gaussian mixture model, fuzzy, machine learning, predict, classify
PAPER SUBMITTED: 2022-08-20
PAPER REVISED: 2022-10-19
PAPER ACCEPTED: 2022-11-05
PUBLISHED ONLINE: 2023-01-21
DOI REFERENCE: https://doi.org/10.2298/TSCI22S1261A
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Special issue 1, PAGES [261 - 270]
REFERENCES
  1. Mohamad, A., et al., Prediction of Fuzzy Sunspot Time Series by Using RBFANN, Tikrit Journal of Pure Science, 22 (2018), 11, pp. 106-112
  2. ***, Coronavirus, W. H. O. (2021), Dashboard, Mar., 15
  3. ***, International Debt Statistics, databank.worldbank.org/source/international-debt-statistics
  4. Elhag, A. A., et al., Artificial Neural Networks and Statistical Models for Optimization Studying COVID-19, Results in Physics, 25 (2021), 104274
  5. Kolo, D. K., Solomon, A. A., A Decision Tree Approach for Predicting Students Academic Performance, Int. J. Education Manag. Eng., 5 (2015), 5, pp. 12-19
  6. Kenekayoro, P., An Exploratory Study on the Use of Machine Learning to Predict Student Academic Performance, International Journal of Knowledge-Based Organizations, 8 (2018), 4, pp. 67-79
  7. Bekele, R., Menzel, W., A Bayesian Approach to Predict Performance of a Student (Bapps): A Case with Ethiopian Students, Algorithms, 22 (2005), 24
  8. Al-Turaiki, I., et al., Empirical Evaluation of Alternative Time-Series Models for Covid-19 Forecasting in Saudi Arabia, International Journal of Environmental Research and Public Health, 18 (2021), 8660
  9. Alballa, N., Al-Turaiki, I., Machine Learning Approaches in COVID-19 Diagnosis, Mortality, and Severity Risk Prediction: A Review, Informatics in Medicine Unlocked, 24 (2021), 100564
  10. Zrieq, R., et al., Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia, Healthcare, 10 (2022), 1874, pp. 1-27
  11. Karnik, N., et al., Applications of Type-2 Fuzzy Logic Systems to Forecasting of Time-Series, Information sciences, 120 (1999) 1, 4, pp. 89-111
  12. Kannan, K., et al., A Comparison of Fuzzy Time Series and ARIMA Model, International Journal of Scientific & Technology Research, 8 (2019), 8, pp. 1872-1876
  13. Chen, S.-M., Forecasting Enrollments Based on Fuzzy Time Series, Fuzzy Sets and Systems, 81 (1996), 3, pp. 311-319
  14. Younis, M. C., Evaluation of Deep Learning Approaches for Identification of Different Corona-Virus Species and Time Series Prediction, Computerized Medical Imaging and Graphics, 90 (2021), 101921
  15. Huarng, K., Effective Lengths of Intervals to Improve Forecasting in Fuzzy Time Series, Fuzzy Sets and Systems., 123 (2001), 3, pp. 87-394
  16. Zhao, H., et al., Adaptive Neuro-Fuzzy Inference System for Generation of Diffuser dot Patterns in Light Guides, Applied Optics, 49 (2010), 14, pp. 2694-2702
  17. Fraley, C., Raftery, A. E., How Many Clusters, Which Clustering Method, Answers Via Model-Based Cluster Analysis, The Computer Journal, 41 (1998), 8, pp. 578-588
  18. Song, Q., Chissom, B. S., Fuzzy Time Series and its Models, Fuzzy Sets and Systems, 54 (1993), 1, pp. 1-9
  19. Sah, M., Degtiarev, K. Y., Forecasting Enrollment Model Based on First-Order Fuzzy Time Series, In World Academy of Science, Engineering and Technology, 1 (2005), pp. 375-378
PDF VERSION [DOWNLOAD]
Using fuzzy time series forecasting and Gaussian mixture model to classify and predict new cases of COVID-19 in Saudi Arabia

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence