## 3 Results and Discussion
### Soft Computing Predictive Models
For a better understanding of the ensemble models developed in this study, a diagram is  
Figure 8: Predicted peak uplift resistance values of ANNMLP and ANNRF models. provided in Figure 9, which compares the models with other ML models, such as KNN, SVM, and CHAID models. In addition to using R and MAE criteria for evaluating the models' performance, a series of gain charts were employed to allow a more in-depth performance assessment (Figure 10). Gains are measured as (quantity of hits in quantile/whole quantity of hits) \(\times\) 100%. At this point, it is essential to state that \"hit\" points show the progress of a predictive model to forecast the values more significant than the middle point of the field's range (peak uplift resistance > 78.92 N). In the diagram mentioned above, the blue line in the faultless model with tremendous confidence (where hits = 100% of cases), the oblique red line signifies the at-chance model, and the other three lines in the center depict the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Partition**} & \multicolumn{2}{c}{**R**} & \multicolumn{2}{c}{**MAE**} & \multirow{2}{*}{**Partition**} & \multirow{2}{*}{**Cumulative**} \\ \cline{3-3} \cline{5-8}  & & **Value** & & & & **Rank** & **Rank** & **Ranking** \\ \hline \multirow{2}{*}{STANDARDANNMLP} & Train & 0.977 & 4 & 4.459 & 4 & 8 & \\  & Test & 0.917 & 3 & 6.243 & 4 & 7 & 15 \\ \hline \multirow{2}{*}{ANNMLP-BOOSTED} & Train & 0.999 & 6 & 0.77 & 6 & 12 & \\  & Test & 0.954 & 5 & 3.848 & 6 & 11 & 23 \\ \hline \multirow{2}{*}{ANNMLP-BAGGED} & Train & 0.995 & 5 & 1.739 & 5 & 10 & \\  & Test & 0.967 & 6 & 3.969 & 5 & 11 & 21 \\ \hline \multirow{2}{*}{STANDARDANNRBF} & Train & 0.931 & 3 & 8.281 & 2 & 5 & \\  & Test & 0.918 & 4 & 6.445 & 3 & 7 & 12 \\ \hline \multirow{2}{*}{ANNRBF-BOOSTED} & Train & 0.896 & 2 & 8.186 & 3 & 5 & \\  & Test & 0.835 & 2 & 9.275 & 2 & 4 & 9 \\ \hline \multirow{2}{*}{ANNRFB-BAGGED} & Train & 0.707 & 1 & 15.809 & 1 & 2 & \\  & Test & 0.655 & 1 & 14.185 & 1 & 2 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the applied performance criteria and cumulative ranking of each model in predicting peak uplift resistance values. Figure 9: Comparison between the ANN models developed in this study and other ML models. models applied in this study. In general, the higher-level lines display more reliable models, especially on the chart's left side. To analyze a model applied and the at-chance model, the region between a model and the red line can be utilized. The area mentioned above signifies the superiority of an applied model versus the at-chance model. The region between a model employed and the excellent model illustrates areas of improvement for an applied model. According to the results of gains charts, for the ANNMLP-ensemble models in both testing and training phases, the ANNMLP-BOOSTED models performed better than all the other models developed in this study. Moreover, for ANNRF-ensemble models, all models showed similar performance in the testing phase. On the other hand, for the training phase, the ANNRF-BOOSTED model outperformed all the other models. This study shows that boosting and bagging techniques can improve performance prediction (both the accuracy level and system error) of the MLP model. However, these techniques were not able to improve the performance of the RBF model, which may imply that both ensemble techniques are more suitable for the MLP-ANN model than the RBF-ANN model. The boosting technique showed better effectiveness than the bagging technique when applied to MLP and RBF models. The better performance of the boosting  
Figure 10: Gain charts of standard and ANN-ensemble models. technique may be associated with its ability to mind the weightage of the more eminent accuracy sample and the lower accuracy sample, and that it can at a later stage supply the merged results. Furthermore, the net error is assessed in every learning run, and it operates well with interactions.