## 5 Conclusions  
The conducted study has shown that the proposed Conv2D VAE can generate synthetic samples for augmentation of the initial training dataset based on real-world MoD-based \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Dataset Configuration** \\ \end{tabular} } & \multicolumn{5}{c}{**Prediction Performance Metrics**} \\ \cline{2-6}  & **RMSE** & **MAE** & **Recall** & **Precision** & **F1 Score** \\ \hline \multicolumn{6}{c}{**Initial Training Dataset**} & **0.179** & **0.042** & **0.939** & **0.941** & **0.943** \\ \hline \multicolumn{6}{c}{+12\% synthetic samples for each cluster} & 0.327 & 0.116 & 0.826 & 0.827 & 0.826 \\ \hline \multicolumn{6}{c}{+11\% synthetic samples for each cluster} & 0.288 & 0.060 & 0.908 & 0.913 & 0.910 \\ \hline \multicolumn{6}{c}{+10\% synthetic samples for each cluster} & 0.242 & 0.070 & 0.901 & 0.904 & 0.902 \\ \hline \multicolumn{6}{c}{+9\% synthetic samples for each cluster} & 0.431 & 0.201 & 0.918 & 0.925 & 0.922 \\ \hline \multicolumn{6}{c}{+8\% synthetic samples for each cluster} & 0.328 & 0.130 & 0.796 & 0.799 & 0.797 \\ \hline \multicolumn{6}{c}{+7\% synthetic samples for each cluster} & 0.271 & 0.127 & 0.948 & 0.947 & 0.947 \\ \hline \multicolumn{6}{c}{+5\% synthetic samples for each cluster} & 0.157 & 0.027 & 0.957 & 0.951 & 0.959 \\ \hline \multicolumn{6}{c}{+3\% synthetic samples for each cluster} & 0.157 & 0.024 & 0.961 & 0.965 & 0.963 \\ \hline \multicolumn{6}{c}{+10\% synthetic samples} & 0.196 & 0.049 & 0.931 & 0.932 & 0.932 \\ \hline \multicolumn{6}{c}{+10\% synthetic samples} & 0.234 & 0.064 & 0.905 & 0.909 & 0.907 \\ \hline \multicolumn{6}{c}{+10\% synthetic samples} & 0.441 & 0.208 & 0.686 & 0.688 & 0.687 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification performance using trained ANN models based on 2-dimensional convolution layers. The individual training of each model is performed using different configurations of training datasets augmented by the GAN model. All models are tested with the same validation datasets generated by real-world MoD-based data. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Prediction Accuracy\%**} \\ \hline \multicolumn{2}{c}{**Model Which Generated**} & \multicolumn{2}{c}{**2D- Convolution**} & \multicolumn{2}{c}{**2D-Convolution**} \\ \multicolumn{2}{c}{**Synthetic Samples**} & \multicolumn{2}{c}{**VAE**} & \multicolumn{2}{c}{**GAN**} \\ \hline \multicolumn{4}{c}{**Shallow**} \\ \multicolumn{2}{c}{**Training**} & \multicolumn{1}{c}{**ANN**} & \multicolumn{1}{c}{**2D**} & \multicolumn{1}{c}{**2D**} \\ \multicolumn{2}{c}{**Dataset Configuration**} & \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**ANN**} & \multicolumn{1}{c}{**ANN**} \\ \multicolumn{2}{c}{**FC Layers**} & \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Model**} \\ \hline \multicolumn{4}{c}{**Initial Training Dataset**} & **81** & **83** & **83** \\ \hline \multicolumn{4}{c}{+12\% synthetic samples for each cluster} & 78 & 98 & 85 \\ \hline \multicolumn{4}{c}{+11\% synthetic samples for each cluster} & 78 & 98 & 84 \\ \hline \multicolumn{4}{c}{+10\% synthetic samples for each cluster} & 77 & 82 & 84 \\ \hline \multicolumn{4}{c}{+9\% synthetic samples for each cluster} & 78 & 91 & 83 \\ \hline \multicolumn{4}{c}{+8\% synthetic samples for each cluster} & 78 & 95 & 80 \\ \hline \multicolumn{4}{c}{+7\% synthetic samples for each cluster} & 79 & 94 & 84 \\ \hline \multicolumn{4}{c}{+5\% synthetic samples for each cluster} & 79 & 93 & 85 \\ \hline \multicolumn{4}{c}{+3\% synthetic samples for each cluster} & 78 & 97 & 85 \\ \hline \multicolumn{4}{c}{+10\% synthetic samples only for morning cluster} & 79 & 91 & 83 \\ \hline \multicolumn{4}{c}{+10\% synthetic samples only for afternoon cluster} & 79 & 97 & 84 \\ \hline \multicolumn{4}{c}{+10\% synthetic samples only for evening cluster} & 80 & 97 & 84 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Prediction performance for classification of synthetic samples by using trained ANN models. data.