Selected article for: "correlation coefficient and fit model"
Author: Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson
Title: COVID-19 Outbreak Prediction with Machine Learning
  • Document date: 2020_4_22
    • ID: nu0pn2q8_70
    Snippet: In the USA, the MLP with 8 and 12 neurons, respectively, for scenarios 1 and 2, provided higher accuracy (the smallest RMSE and the largest correlation coefficient values) than the ANFIS model. By considering the average values of the RMSE and correlation coefficient values, it can be concluded that scenario 1 is more suitable than scenario 2, and MLP is more suitable than ANFIS for outbreak prediction. Figures 12 to 16 present the model fits for.....
    Document: In the USA, the MLP with 8 and 12 neurons, respectively, for scenarios 1 and 2, provided higher accuracy (the smallest RMSE and the largest correlation coefficient values) than the ANFIS model. By considering the average values of the RMSE and correlation coefficient values, it can be concluded that scenario 1 is more suitable than scenario 2, and MLP is more suitable than ANFIS for outbreak prediction. Figures 12 to 16 present the model fits for Italy, China, Iran, Germany, and the USA, respectively. By comparing Figure 12 to 16 with Figures 7 to 11 , it can be concluded that the MLP and the logistic model fitted by GWO provided a better fit than the other models. In addition, the ML methods provided better performance compared with other models.

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