نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه علوم کامپیوتر، دانشکده مهندسی صنایع و کامپیوتر، دانشگاه صنعتی بیرجند، بیرجند، ایران.

2 گروه ریاضی کاربردی، دانشکده علوم ریاضی، دانشگاه مازندران، مازندران، ایران.

چکیده

بیماری کرونا در حال حاضر بحران جهانی سلامت و بزرگترین چالشی است که بشر از زمان جنگ جهانی دوم تاکنون تجربه کرده است. با توجه به همه­گیری این بیماری، پیش­بینی تعداد موارد مبتلا و مرگ­ومیر ناشی از آن برای درک بهتر شرایط فعلی و تهیه برنامه کوتاه­مدت توسط مدیران، بسیار ارزشمند است. بر این اساس، در این مقاله یک مدل شبکه عصبی-فازی برای پیش­بینی تعداد موارد مبتلا و مرگ­ومیر ناشی از این بیماری در کشورهایی که بیشتر با این بیماری درگیر هستند پیشنهاد شده است. عملکرد شبکه عصبی-فازی پیشنهادی با شبکه­های عصبی پیش­بینی سری زمانی و همچنین شبکه­های عصبی توابع پایه­ای شعاعی مقایسه شده است. مدل پیشنهادی قادر است تعداد موارد مبتلا و مرگ­ومیر ناشی از بیماری را برای یک دوره ۱۵ روز آینده با نرخ خطای کمتر پیش­بینی کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Forecasting of the number of cases and deaths due to corona disease using neuro-fuzzy networks

نویسندگان [English]

  • Malihe Niksirat 1
  • Seyed Hadi Nasseri 2

1 Assistant Professor of Department of Computer Science, Birjand University of Technology, Birjand, Iran.

2 Department of Mathematics, Faculty of Mathematical Sciences, University of Mazandaran, Babolsar, Iran.

چکیده [English]

Corona is currently the world's health crisis and the biggest challenge humans have experienced since World War II. Given the epidemic of the disease, it is invaluable to forecasting the number of cases and the resulting deaths to better understand the current situation and provide a short-term plan by managers. Accordingly, in this paper, a neuro-fuzzy network model is proposed to forecast the number of cases and deaths in countries that are most affected by this disease. The performance of the proposed neuro-fuzzy network has been compared with time series forecasting neural network as well as radial basic functions neural networks. The proposed model is able to predict the number of cases and deaths from the disease for a period of the next 15 days at a lower error rate.

کلیدواژه‌ها [English]

  • corona
  • Neuro-fuzzy network
  • time series
  • Forecasting
  • approximation function
  • Classification
Al-Qaness, M. A., Ewees, A. A., Fan, H., & Abd El Aziz, M. (2020). Optimization method for forecasting confirmed cases of COVID-19 in China. Journal of clinical medicine, 9(3), 1-15.
Cichocki, A. & Unbehauen R. (1993). Neural networks for optimization and signal processing. Wiley.
de Campos Souza, P. V. (2020). Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature. Applied soft computing, 92. https://doi.org/10.1016/j.asoc.2020.106275
Du, Y., Cai, Y., Chen, M., Xu, W., Yuan, H., & Li, T. (2014). A novel divide-and-conquer model for CPI prediction using ARIMA, gray model and BPNN. Procedia computer science, 31, 842-851.
Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, solitons & fractals, 134. https://doi.org/10.1016/j.chaos.2020.109761
Han, M., Zhong, K., Qiu, T., & Han, B. (2018). In terval Type-2 fuzzy neural networks for chaotic time series prediction: a concise overview. IEEE transactions on cybernetics, 49, 2720 – 2731.
Hasan, N. (2020). A methodological approach for predicting COVID-19 epidemic using EEMD-ANN hybrid model. Internet of things, 11. https://doi.org/10.1016/j.iot.2020.100228
Khosravi, A., Koury, R. N. N., Machado, L., & Pabon, J. J. G. (2018). Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Poultry science, 92, 1138-1142.
Meng, E., Huang, S., Huang, Q., Fang, W., Wu, L., & Wang, L. (2019). A robust method for non-stationary streamflow prediction based on improved EMD-SVM model. Journal of hydrology, 568, 462-478.
Mizumoto, K., Kagaya, K., & Chowell, G. (2020). Effect of a wet market on coronavirus disease (COVID-19) transmission dynamics in China, 2019–2020. International journal of infectious diseases, 97, 96-101.
Shim, E., Tariq, A., Choi, W., Lee, Y., & Chowell, G. (2020). Transmission potential and severity of COVID-19 in South Korea. International journal of infectious diseases, 93, 339-344.
Singh, A. K., Singh, A., Shaikh, A., Singh, R., & Misra, A. (2020). Chloroquine and hydroxychloroquine in the treatment of COVID-19 with or without diabetes: A systematic search and a narrative review with a special reference to India and other developing countries. Diabetes & metabolic syndrome: clinical research & reviews, 14(3), 241-246.
Škrjanc, I., Iglesias, J. A., Sanchis, A., Leite, D., Lughofer, E., & Gomide, F. (2019). Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: a survey. Information sciences, 490, 344-368.
Tan, Q. F., Lei, X. H., Wang, X., Wang, H., Wen, X., Ji, Y., & Kang, A. Q. (2018). An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach. Journal of hydrology, 567, 767-780.
Tang, J., Liu, F., Zhang, W., Ke, R., & Zou, Y. (2018). Lane-changes prediction based on adaptive fuzzy neural network. Expert systems with applications, 91, 452-463.
Tang, J., Liu, F., Zou, Y., Zhang, W. & Wang, Y. (2017). An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE transactions on intelligent transportation systems, 18, 2340-2350.
Tosepu, R., Gunawan, J., Effendy, D. S., Lestari, H., Bahar, H., & Asfian, P. (2020). Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of the total environment, 725. https://doi.org/10.1016/j.scitotenv.2020.138436
Tseng, F.-M., & Hu, Y.-C. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert systems with applications, 37, 1846–1853.
Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020). Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of things, 11. https://doi.org/10.1016/j.iot.2020.100222
Xiao, Q. (2017). Time series prediction using bayesian filtering model and fuzzy neural networks. Optik, 140, 104-113.