## 4 Experimental Results
\[Recall=\left(\frac{true\ positives}{true\ positives+false\ positives}\right)_{for\ i^{th} class} \tag{9}\]  
\[F1-score=\left(2\times\frac{precision\times recall}{precision+recall}\right)_{for\ i^{th} class} \tag{10}\]  
A total of 2000 scalograms were input into the suggested model. To ensure that the findings could be used generally, the recommended model was also trained and evaluated using the 10-fold cross-validation method. We divided the dataset into 10 groups to provide a more specific illustration. A tiny sample of the data was tested, and the remaining nine portions were utilized to train the proposed model. This process was carried out 10 times. We utilized a confusion matrix to measure the model's performance for the digital modulation classification problem. The resultant confusion matrix is shown in Figure 7. Table 2 displays the classification results for each class of the confusion matrix. In other words, the classification performance of the suggested model was good. Table 2 lists the outcomes of Experiment 2. The overall accuracy, precision, recall, and \(F1\)-score for the proposed model are 99.938%, 99.87%, 99.865%, and 99.867%, respectively. With a classification accuracy of 99.99%, \(PSK\) is the most accurately identified modulation type, while \(FSK\) has the lowest classification accuracy at 99.77%. The experimental results show that the recommended model can successfully carry out the modulation categorization task. As can be seen in Table 2, the accuracy of the proposed method increased with the increase of SNR. The reason for this is that, by increasing the SNR, the signal power is \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **\(F\)-Score** \\ \hline _ASK_ (Proposed) & 99.90 & 99.80 & 99.90 & 99.93 \\ _FSK_ (Proposed) & 99.77 & 99.49 & 99.9 & 99.69 \\ _PSK_ (Proposed) & 99.99 & 99.99 & 99.99 & 99.99 \\ QASK (Proposed) & 99.99 & 99.99 & 99.99 & 99.99 \\ QPSK (Proposed) & 99.99 & 99.90 & 99.61 & 99.75 \\ QPSK (Proposed) & 99.99 & 99.99 & 99.80 & 99.88 \\ Avg. (Proposed) & 99.938 & 99.87 & 99.865 & 99.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The proposed model’s results covered all SNR rates in each class. Figure 7: Confusion matrix of categorization results, including all SNR rates in each class. increased, and as a result, the effect of the noise decreases, so it becomes easier to detect the type of modulation, and the detection accuracy increases. We have created a novel model for classifying different forms of digital modulations based on CWT and deep convolutional neural networks (CNN). Our data source was the CWT, which creates scalograms. We tried to use the VGG-16, VGG-19, and GoogLeNet for well over an hour. Figure 8, Table 3 compared with the different CNN architectures on the digital modulation categorization problem, presents this information, and it is clear to see that the suggested approach is, on average, quicker and more accurate than all other methods. We compared the proposed method against a number of previously trained CNN-based models for categorizing digital modulation. Table 4 demonstrates that the CNN models of proposed method were successful in achieving a high degree of classification accuracy (over 99% accuracy).