The normalized percentage mean squared error obtained from ANN and RSM models were 0.05 and 0.1%, respectively.
The performance of the fuzzy predicting model has been compared with the response surface (RS) model with respect to mean squared error (MSE), mean percentage error (MPE) and co-efficient of determination (R2).
The accuracy of fetal weight predictions was quantified by calculating the correlation with actual birth weight, the mean squared error, the mean absolute percentage error, and the percentage of birth weights that were predicted accurately to within ±10% and to within ±15% of the actual birth weight.
Quality is usually measured using mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).
Four error indicators, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE), are used for the evaluation and to rank the performance of the hybrid models, which are computed using the following equations.
To optimize the modeling, the results of the algorithms were compared by minimized root mean squared error (RMSE) and the percentage of absolute average deviation (AAD) while the coefficient of determination (R2) was maximized.
Root mean squared error (RMSE) and mean absolute percentage error (MAPE) are employed to evaluate the performances of the models.
The mean squared error (MSE) and mean absolute percentage error (MAPE) are quite low whereas the coefficient of determination (R2) is reasonably high for the developed model.
Flower numbers were estimated from image-based flower cover percentage with root mean squared errors that ranged from 159 to 194 flowers, which was better than the reported results for other studies with a similar objective.
