## 7 Results and Discussion  
The proposed CNN models have successfully predicted SNR with high performance. Figures 13-15 depict the learning curves, which show the training and validation results for both loss and RMSE and their ratio in relation to the validation values. We discarded the scatter plot because it created point-overlapping distortion. The graphs in this study, referred to as \"standard hyperparameters,\" display the dropout rate, learning rate, and number of epochs. The quantity of filters utilised in this study was modified, as indicated in Tables 3 and 4. The training and validation curves show a gradual decrease in loss and RMSE as the number of epochs increases, eventually converging to a similar value. The loss and RMSE in training and validation curves gradually decrease with epochs, and they become close to each other. When the number of filters (16, 20,24, and 28) in the CNN architecture increases, the training and validation loss and RMSE decrease, as seen in Figure 13. The crucial metrics are the loss and RMSE ratios, approximately equal to 1. This indicates that the models are highly accurate and efficient in predicting the actual SNR values. Figures 14 and 15 demonstrate a decrease in both the loss and RMSE. However, a slight divergence was observed between the training and validation curves, indicating a minimal gap between the loss and RMSE values for the training and validation datasets. Table 5 provides a comprehensive summary of the models' performance via train/validation loss and train/validation RMSE at the last epoch, as well as the number of trainable parameters and the loss and RMSE ratios. The equation for each one is as follows:  
\[\text{Loss Ratio}=\frac{\text{Loss}}{\text{Validation Loss}} \tag{25}\]  
\[\text{RMSE Ratio}=\frac{\text{RMSE}}{\text{Validation RMSE}} \tag{26}\]  
The nearer to 1 the ratio is, the more fitting the model is so that the model can make a correct decision, and the more likely the predicted SNR will approach the actual value. The statistical analysis includes the results' minimum, maximum, and mean information. For example, the training time ranges between 8.33 and 10.99, whereas the range of predicting time ranges from 0.1732 to 0.2098. We observed no significant fluctuation in time, although the number of filters changed. The maximum difference between 1 and loss or RMSE ratios are 0.3381 and 0.4153, respectively, using 48 and 56 filters in CNN implementation. In contrast, the minimum difference between 1 and loss or RMSE ratios are 0.0297 and 0.0183, respectively, when using 20 filters. In addition, the average of the \(|1-Loss\ Ratio|\) is 0.2107, and for \(|1-RMSE\ Ratio|\) it is 0.2551, which is very close to zero, as shown in Table 3.  
Figure 12: Scheme of calculating the MAE of True and Predicted data. Figure 13: Learning curves of CNN regression models using (**a**) 16, (**b**) 20, (**c**) 24, and (**d**) 28 filters, measured via loss and RMSE. Figure 14: Learning curves of CNN regression models using (**a**) 32, (**b**) 36, (**c**) 40, and (**d**) 44 filters, measured via loss and RMSE. Table 7 represents the Pearson correlation coefficient between the number of filters and other results' information, like training and predicting times, loss, and RMSE, and the validation for both interpretations of these correlations. On the other hand, the graphs below, Figures 16-19 show the relationship between the number of filters used in the CNN models and their information. Figure 16 shows the weak correlation between the number of filters and training and predicting times, which indicates that the curve is almost constant. While it is expected that increasing the number of filters in a CNN would result in increased computations and, therefore, more time to complete them, using a highly capable VM mitigates the impact of increased computations on time, making it negligible. Figure 15: Learning curves of CNN regression models using (**a**) 48, (**b**) 52, (**c**) 56, (**d**) 60 and (**e**) 64 filters, measured via loss and RMSE.