## 4 Results  
### Experimental Evaluation of Trained DNN Models  
The prediction accuracy of the DNN model training and test data was analyzed. Figure 6 illustrates the coefficient of determination and MAPE for the training and test data, according to the numbers of hidden layers and hidden layer nodes of the DNN model. As the number of hidden layer nodes increased, the coefficient of determination for the training and test data gradually increased, without regard to the number of hidden layers, while the MAPE gradually decreased. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Input Parameters**} & \multicolumn{8}{c}{**Date**} \\ \cline{2-10}  & **11/1** & **11/2** & **11/5** & **11/6** & **11/7** & **11/8** & **11/9** & **11/15** & **11/16** & **11/17** \\ \hline Daily working time (min) & 630 & 360 & 791 & 557 & 426 & 910 & 446 & 461 & 404 & 431 \\ Probability of occurring & 0 & 0 & 0.3 & 0 & 0 & 0 & 0 & 0 & 0 & 0.33 \\ Loading 1 & 1 & 1 & 0.7 & 1 & 1 & 1 & 1 & 1 & 1 & 0.67 \\
\begin{tabular}{c} loading 2 \\ \end{tabular} & 1 & 1 & 0.7 & 1 & 1 & 1 & 1 & 1 & 1 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Description of input node setting of optimal DNN model for predicting ore production and crusher utilization over 10 working days. Figure 6: _Cont._  
When the number of hidden layer nodes was set to 20 or more, the coefficient of determination for all of the DNN model training and test data was found to be 0.99, while the MAPE was less than 5%. When the number of hidden layers of the DNN model was set to five and the number of hidden layer nodes was set to 40; the coefficient of determination for the test data was the highest at approximately 0.99; and the MAPE was the lowest at approximately 2.80% (see Table 6). Moreover, the coefficient of determination for the training data in this case was approximately 0.99, while the MAPE was approximately 2.45%. Therefore, the DNN model trained with five hidden layers and 40 hidden layer nodes was selected as the optimal model for the ore production prediction. Figure 7 illustrates the results of using the optimal DNN model for predicting the ore production by predicting the training data and test data, and then analyzing the correlation between the observed and predicted values. The coefficient of determination for the training and test data was found to be greater than 0.99, and the optimal DNN model could accurately predict both the training and test data. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multicolumn{2}{c}{**Statistics**} \\ \cline{2-3}  & **Coefficient of Determination (R\({}^{2}\))** & **MAPE (\%)** \\ \hline Training data & 0.99 & 2.45 \\ Test data & 0.99 & 2.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results of coefficient of determination and MAPE of DNN model with five hidden layers and 40 hidden layer nodes for ore production prediction. Figure 6: Results of accuracy analysis of DNN models for predicting ore production with different numbers of hidden layers and hidden layer nodes: coefficient of determination for (**a**) training data and (**b**) test data, and MAPE for (**c**) training data and (**d**) test data. The coefficient of determination and MAPE for the training and test data of the DNN model trained to predict the crusher utilization were analyzed. Figure 8 illustrates the changes in the DNN model coefficient of determination and MAPE for the training and test data with various configurations of the hidden layer structure. As the number of hidden layer nodes increased, the prediction accuracy for the training and test data increased, and the error decreased, without regard to the number of hidden layers. However, when the number of hidden layers was set to 6, the coefficient of determination decreased, and the MAPE increased further when the number of hidden layer nodes increased to a maximum of 40 compared to when the number of hidden layer nodes was set to 30.