## 4 Results and Discussion
### Evaluation of All Investigated Dimensionality Reduction Methods for Clustering Based on MoD Data
Thus, there are 4200 training samples assigned to each group of hourly subclusters which correspond to morning, afternoon and evening clusters. All other configurations of training datasets include the initial training dataset combined with synthetic samples generated by the proposed Conv2D VAE framework. The first group of combined datasets configuration augments each of the morning, afternoon and evening clusters by 3%, 5%, and 10% synthetic samples. Those percentages are computed based on the initial training dataset. This gradual increase in added synthetic samples for initial data clusters was chosen since it is necessary to examine at which percentage the improvements in the learning process are noticeable. Moreover, a percentage of the improvement in the learning process is also observed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Shallow FC Layers Based ANN Model**} \\ \hline
**Layer** & **Type of** & **Activation** & **Nodes** & **Dropout** \\
**Number** & **Layer** & **Function** & **Rate** \\ \hline
1 & Dense (FC) & ReLu & 256 & \\ \hline
2 & Dropout & & & 0.3 \\ \hline
3 & Dense (FC) & ReLu & 64 & \\ \hline
4 & Dropout & & & 0.3 \\ \hline
5 & Dense (FC) & ReLu & 32 & \\ \hline
6 & Dropout & & & 0.3 \\ \hline
7 & Dense (FC) & Softmax & 3 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The architecture of the shallow ANN model based on FC layers which uses vector-alike inputs for the classification of MoD-based data samples in three distinctive clusters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**2-D Convolution Based ANN Model**} \\ \hline
**Layer** & **Type of** & **Filter** & **Kernel** & **Strides** & **Activation** & **Nodes** \\
**Number** & **Layer** & **Number** & **Size** & **Strides** & **Function** & **Nodes** \\ \hline
1 & Conv2D & 128 & (12,12) & (2,2) & ReLu & \\ \hline
2 & Batch & & & & \\  & Normalization & & & & \\ \hline
3 & MaxPooling & & (5,5) & (1,1) & & \\ \hline
4 & Conv2D & 64 & (8,8) & (1,1) & ReLu & \\ \hline
5 & Batch & & & & \\  & Normalization & & & & \\ \hline
6 & MaxPooling & & (3,3) & (1,1) & & \\ \hline
7 & Conv2D & 32 & (3,3) & (2,2) & ReLu & \\ \hline
8 & Batch & & & & \\  & Normalization & & & & \\ \hline
9 & MaxPooling & & (3,3) & (1,1) & & \\ \hline
10 & Flatten & & & & \\ \hline
11 & Dense (FC) & & & ReLu & 32 \\ \hline
12 & Dense (FC) & & & Softmax & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The 2-dimensional convolution ANN model architecture which uses image-alike inputs for classification of MoD data samples in three distinctive clusters. of synthetic samples that are too large can distort learning goals. The second group of training dataset configurations is based on synthetic samples which augment only one of the three initial data clusters with 10% of synthetic samples. This group of training dataset configurations is investigated since it is needed to evaluate whether the targeted augmentation of individual initial data clusters can improve overall learning performance. The convergence of learning error for shallow ANN model based on FC layers can be seen in Figure 7. In all training datasets, augmentations with synthetic samples induce faster learning convergence compared to the initial real-world dataset. Thus, augmentation of the initial dataset with any percentage of synthetic samples can speed up the learning of shallow ANN models for classification. Moreover, those results are achieved without _overfitting_ or reducing final learning errors. Those results suggest that synthetic samples created by the proposed Conv2D VAE framework are consistent with the features of a shared cluster for which they are generated. Those results are expected for a shallow ANN-based model since it does not utilise complex mechanisms for deeper feature extraction.