## 4 Results and Experimental  
### Validation of Each Model According to Various Parameters  
The three models were analyzed in terms of accuracy and loss per epoch using the training and validation sets to indicate how well the model predicted the training or validation data. Figure 8 illustrates the accuracy and loss results of each model per epoch. \begin{table}
\begin{tabular}{c c c c c} \hline
**Code** & **Train Data** & **Test Data** & **Validation Data** & **Total** \\ \hline
08 & 16,000 & 2000 & 2000 & 20,000 \\
32 & 16,000 & 2000 & 2000 & 20,000 \\
12 & 16,000 & 2000 & 2000 & 20,000 \\
13 &  
Accuracy was interpreted as an improvement in prediction performance as the difference between the training and validation data decreased. Conversely, as the training progressed, the curve became a better fit for the training data, and the training accuracy converged to nearly 100%. However, when the loss became divergent, the curve suffered from overfitting. Therefore, the curve should converge after a certain point to avoid problems. In Model 1, when the training accuracy converged to 100% as the epochs progressed, a difference in validation accuracy occurred. When the validation accuracy converged to 87% (more than a 5% difference in accuracy), the training accuracy reached 92%; however, there was no positive effect as the epochs progressed. As the training loss of Model 1 converged to zero, the validation loss gradually increased, which could be interpreted as a sign of overfitting. Further details are presented in Table 9.  
Figure 8: Accuracy and loss of models. Model 1 (**a**) accuracy and (**b**) loss; Model 2 (**c**) accuracy and (**d**) loss; Model 3 (**e**) accuracy and (**f**) loss. In Model 2, the training accuracy reached 99% when the validation accuracy converged to 93% (within a 6-7% accuracy difference), indicating a tendency for accuracy to improve with epochs. The training loss in Model 2 converged to zero, while the validation loss gradually increased, as in Model 1, and then converged at epochs above 18; however, it exhibited a tendency to overfit. Further details are presented in Table 10.  
In Model 3, the training accuracy was 99% when the validation accuracy converged to 95% (within a 4-5% accuracy difference), and the accuracy reached 96% when the epoch was 20. The training loss in Model 3 also converged to zero, similar to the previous models,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Epoch**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Difference**} \\ \cline{2-7}  & **Accuracy** & **Loss** & **Accuracy** & **Loss** & **Accuracy** & **Loss** \\ \hline
1 & 71\% & 1.13 & 88\% & 0.41 & \(-\)17\% & \(-\)0.72 \\
2 & 88\% & 0.39 & 90\% & 0  
and the validation loss converged when the number of epochs was 16 or more. Further details are presented in Table 11.  
With each epoch, the model was increasingly fit to the training data, which was manifested by the training accuracy reaching 100%, or a loss converging to zero. Thus, the model learned the training data perfectly, but it also introduced the possibility of overfitting. Overfitting occurred when the model was well fitted to the training data and became less predictive of new data. However, the validation accuracy and loss converged to a certain value. Thus, the ability of the model to generalize new data was established, as illustrated in Figure 9.  
The performance of the classification models and their tendency to overfit were identified based on the difference between accuracy and loss. The performance of the classification model showed that the accuracy improved with the performance of the classification model, from Models 1, 2, and 3. Similarly, the difference between the training and validation losses eliminated the overfitting problem in Model 3. Therefore, Model 3, proposed in this study, exhibited the best classification performance for both the new and training data.