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
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.