## 7 Results and Discussion
Their correlation coefficients range from medium to very strong concerning loss, validation loss, RMSE, and validation RMSE. When the number of filters increases, the capacity of the model (trainable parameters) also increases, which allows it to fit the training data better and improve its effectiveness (see Figure 17). In contrast, the loss and RMSE ratios are close to \(1\), as seen in Figure 18, which means the model makes good decisions. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**No. of Parameters**} & \multicolumn{2}{c}{**Interpretation**} \\ \hline Loss & \(-0.8977\) & Strong linear inverse correlation \\ Val. loss & \(-0.7891\) & Strong linear inverse correlation \\ RMSE & \(-0.8863\) & Strong linear inverse correlation \\ Val. RMSE & \(-0.7029\) & Strong linear inverse correlation \\ No. of filters & \(0.9997\) & Very strong linear direct correlation \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Pearson correlation coefficients between a number of parameters and results’ information and their interpretation. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Standard Hyperparameters**} & \multicolumn{1}{c}{**Values**} \\ \hline Colour mode & \multicolumn{1}{c}{Grayscale} \\ Eye diagram image size (height \(\times\) width) & \(2366\times 3125\) \\ Model & Functional API \\ Optimizer & Adam \\ Loss function & MAE \\ Metric & RMSE \\ Batch size & 15 \\ No. of convolutional layers & 5 \\ No. of max pooling layers & 5 \\ Filters size & 10 \\ Filters stride & 1 \\ Pooling size & 3 \\ Pooling stride & 3 \\ No. of hidden layers & 1 \\ Activation function in the hidden layer & ReLU \\ Activation function in the output layer & linear \\ Learning rate & \(10^{-5}\) \\ Epochs & 250 \\ Dropout rate & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Standard hyperparameters for CNN model platform structure. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Training**} & \multicolumn{1}{c}{**Predicting**} & \multirow{2}{*}{**Loss**} & \multicolumn{1}{c}{**Validation**} & \multirow{2}{*}{**RMSE**} & \multicolumn{1}{c}{**Validation**} & \multirow{2}{*}{**Loss**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**No. of**} & \multicolumn{1}{c}{**No. of**} \\
**Time (h)** & & & & & & & & & \\
9.82 & 0.18551209 & 0.519 &  
On the other hand, the positive upward curve displays a very strong linear direct correlation between the number of filters and the number of parameters; it forms a perfectly straight line (see Figure 19). The reason is that the total number of values inside all filters increases when the filters are increased. These are considered parameters, so the number of trainable parameters increases. To assess the performance of the CNN models that work on the optical receiver, which can handle the ISI noise in UWOC, the relationship between the actual and predicted values of SNR and BER is drawn in Figure 20. This result illustrates the models' outcomes using a 0.45 dropout rate and a learning rate of \(10^{.5}\) with 28 filters, as shown in the figure below. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **No. of Filters** & **Interpretation** \\ \hline Training Time & \(-0.1923\) & Very weak linear inverse correlation \\ Predicting Time & \(0.1793\) & Very weak linear direct correlation \\ Loss & \(-0.9053\) & Very strong linear inverse correlation \\ Validation Loss & \(-0.7897\) & Strong linear inverse correlation \\ RMSE & \(-0.8429\) & Strong linear inverse correlation \\ Validation RMSE & \(-0.6416\) & Moderate linear inverse correlation \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Pearson correlation coefficients between a number of filters and results’ information and their interpretation. Figure 16: Number of filters vs. training and predicting time for all CNN models. Figure 17: Number of filters vs. training and validation for both loss and RMSE for all CNN models.