## 3 Results  
### Architectures of DL Models with Optimal Performance  
This study established the initial architecture of the 1 DTempCNN, 1 DSpecCNN, and LSTM models by evaluating the models' accuracy across multiple candidate values for three hyperparameters in each model's architecture (Table 1). The performance of each of the 100 designed architectures was assessed based on the macro-average of the F1-score on the validation dataset. Heatmaps were used to visualize the accuracy of each designed architecture for each model (Figure 3). For 1 DTempCNNs, accuracy declined as the number of layers increased, while better performance was often observed with a high number of filters per layer and lower dropout rates. Similarly, for 1 DSpecCNNs, increasing the number of layers led to a decrease in accuracy, indicating that deeper architectures may not necessarily enhance classification accuracy on the validation dataset (i.e., the model's generalizability). The 1 DSpecCNN model generally performed better with fewer filters per layer, despite fluctuations in accuracy. Lower dropout rates were also linked to higher accuracy for 1 DSpecCNN architectures. In the LSTM model, accuracy generally improved up to two layers, with higher performance often seen when using a greater number of cells per layer and lower dropout rates, although some fluctuations in accuracy were noted. It is thus recommended to use relatively shallow architectures with lower dropout rates to improve the model's classification accuracy. To examine the impact of each hyperparameter in the model's architecture on its accuracy, this study employed the ANOVA method in conjunction with the heatmap (Figure 3). ANOVA is a statistical method used to assess the impact of independent variables on a dependent variable. It evaluates whether the dependent variable's average values vary significantly across each independent variable's levels. For each factor (i.e., independent variable), ANOVA calculates an F-statistic that quantifies the variance attributed to that factor concerning random error. A high F-value and a low \(p\)-value suggest that the observed differences in means are likely meaningful and not due to random chance, indicating a significant effect of the factor on the outcome. [51]. Table 2 outlines the results of the ANOVA method for each hyperparameter in the architecture of all three DL-based models. The ANOVA results indicated that the number of layers and the dropout rate significantly impacted model accuracy more than the number of filters per layer in both 1 DTempCNN and 1 DSpecCNN models. In the LSTM model, all three hyperparameters had a significant impact on performance, with the number of layers and dropout rate having the largest effects, followed by the number of cells per layer with a slightly lower influence. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Hyperparameters of the**} & \multirow{2}{*}{**Candidate Values**} & \multirow{2}{*}{**Optimal Value**} \\  & **Architecture** & & \\ \hline \multirow{3}{*}{1 DTempCNNs} & Number of layers & 2, 4, 6, 8 & 2 \\ \cline{2-4}  & Number of filters per layer & 32, 64, 128, 256, 512 & 256 \\ \cline{2-4}  & Dropout rate & 0.1, 0.2, 0.3, 0.4, 0.5 & 0.1 \\ \hline \multirow{3}{*}{1 DSpecCNNs} & Number of layers & 2, 4, 6, 8 & 2 \\ \cline{2-4}  & Number of filters per layer & 32, 64, 128, 256, 512 & 64 \\ \cline{2-4}  & Dropout rate & 0.1, 0.2, 0.3, 0.4, 0.5 & 0.1 \\ \hline \multirow{3}{*}{LSTM} & Number of layers & 1, 2, 3, 4 & 2 \\ \cline{2-4}  & Number of cells per layer & 32, 64, 128, 256, 512 & 512 \\ \cline{1-1} \cline{2-4}  & Dropout rate & 0.1, 0.2, 0.3, 0.4, 0.5 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Candidate values for three hyperparameters and their corresponding optimal values in the initial architecture configuration of DL models.