## 5 Model Training
### Effects of Augmentation Methods  
Data augmentation methodologies enhance the performance of Arabic dialect classification models by mitigating class imbalances. ADASYN and SMOTE markedly enhance the Matthews correlation coefficient (MCC), a reliable statistic for imbalanced categorization. RandomOverSampler also enhances the Matthews correlation coefficient, albeit to a lesser  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Methods** & **Model** & **Training Accuracy** & **Training Loss** & **Test Accuracy** & **Test Loss** & **Misclassification** & **Matthews** & **Intersection** & **Dice** \\  & & **Accuracy** & **Loss** & **Accuracy** & **Loss** & **Rate** & **Correlation** & **over the** & **Similarity** \\  & & & & & & & & & **Union** & **Coefficient** \\ \hline No augmentation & ANN & 0.9322 & 0.1648 & 0.9415 & 0.1446 & 0.06502 & 0.3570 & 0.9322 & 0.9649 \\ No augmentation & 1D CNN & 0.9322 & 0.16448 & 0.9415 & 0.1446 & 0.070197 & 0.331 & 0.9285 & 0.9629 \\ No augmentation & 2D CNN & 0.9175 & 0.24438 & 0.94307 & 0.1719 & 0.073892 & 0.0877 & 0.9259 & 0.9615 \\ ADASYN & ANN & 0.8500 & 0.3000 & 0.8200 & 0.3300 & 0.15748 & 0.6964 & 0.7494 & 0.8568 \\ ADASYN & 1D CNN & 0.8800 & 0.2500 & 0.8600 & 0.2800 & 0.192924 & 0.6289 & 0.7018 & 0.8247 \\ ADASYN & 2D CNN & 0.9000 & 0.2000 & 0.8800 & 0.2300 & 0.152118 & 0.7070 & 0.7446 & 0.8536 \\ RandomOverSampler & ANN & 0.9447 & 0.1457 & 0.9554 & 0.1503 & 0.05665 & 0.4544 & 0.9421 & 0.9702 \\ RandomOverSampler & 1D CNN & 0.9366 & 0.1475 & 0.9477 & 0.1550 & 0.060345 & 0.3999 & 0.9386 & 0.9683 \\ RandomOverSampler & 2D CNN & 0.943 & 0.1419 & 0.9554 & 0.1441 & 0.061576 & 0.4034 & 0.9372 & 0.9676 \\ SMOTE & ANN & 0.8200 & 0.3677 & 0.8322 & 0.3703 & 0.175576 & 0.6575 & 0.7190 & 0.8365 \\ SMOTE & 1D CNN & 0.8187 & 0.4003 & 0.8264 & 0.3841 & 0.060345 & 0.3999 & 0.222 & 0.3636 \\ SMOTE & 2D CNN & 0.8909 & 0.2570 & 0.8781 & 0.2867 & 0.124833 & 0.7551 & 0.7875 & 0.8811 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model accuracy and loss.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Augmentation Method** & **Model** & **Verpose** & **Patience** & **Batch Size** & **Min\_Lr** & **Epochs** & **Validation\_Split** \\ \hline No augmentation & ANN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ No augmentation & 1D CNN & 0 & - & 32 & - & 50 & 0.2 \\ No augmentation & 2D CNN & 0 & - & 32 & - & 50 & 0.2 \\ ADASYN & ANN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ ADASYN & 1D CNN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ ADASYN & 2D CNN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ ADASYN & 2D CNN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ RandomOverSampler & ANN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ RandomOverSampler & 1D CNN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ RandomOverSampler & 2D CNN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ SMOTE & ANN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ SMOTE & 1D CNN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ SMOTE & 2D CNN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ SMOTE & 2D CNN & 1 & 10 & 128 & 0.000001 & 30 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters.  
extent. SMOTE produces efficient synthetic samples, allowing 2D CNN models to acquire discriminative features for dialect classification.