## 3 Results
### Comparison Results with Other Methods  
To verify the classification superiority of the proposed CRAMNet model, we designed a series of experiments to compare it with several commonly used classification methods. These methods include Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), Dense Convolutional Network (DenseNet), and Convolutional Neural Network combined with Transformers (CNN-Transformers). The specific comparison methods are shown in Table 5. The experimental training and test results of several methods are shown in Figure 9. \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Parameters** \\ \hline CNN & 2*[Conv-Pool]-fc1-fc2-Classifier \\ LSTM & 3*lstm-fc1-fc2-Classifier \\ DenseNet & 3*dense-Classifier \\ CNN-Transformers & 2*[Conv-Pool]-fc1-fc2-transforms-Classifier \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Structure and parameter setting of comparison methods. Figure 8: Visualized results during training (**a**) ResNet1D features, (**b**) ResNet2D features, (**c**) attention features, (**d**) FC1 features, (**e**) FC2 features, and (**f**) FC3 features. From Figure 9a,b of the training stage, it can be seen that the CNN and LSTM training accuracy rapidly rises and stabilizes, ultimately stabilizing at a higher level. The training accuracy and training loss of the DenseNet method change trends are similar to those of CNN, but the starting point is lower. The training accuracy and training loss of the CNN-Transformer method change fluctuates greatly, which may be caused by the instability of training due to data augmentation. The training accuracy of CRAMNet rapidly rises in the early stage and ultimately reaches an accuracy of nearly 100%, and the training loss also significantly decreases, showing good training effects. From Figure 10c,d of the testing stage, it can be seen that the testing accuracy and testing loss of CNN, LSTM, and DenseNet methods are generally poor, and the testing loss decreases slowly. The testing accuracy and testing loss of the CNN-Transformer method fluctuate greatly, showing a certain instability. The testing accuracy of CRAMNet remains at a high level throughout the training process and gradually increases. The testing loss rapidly decreases and remains at a lower level, showing good generalization ability. In summary, CRAMNet performs well in both training and testing stages, with the advantages of fast convergence, high accuracy, and low loss. Compared with other methods, CRAMNet demonstrates better performance and robustness, proving its superiority. The confusion matrix results of several methods are shown in Figure 10. The results of the confusion matrix in Figure 10 reveal varying classification performances among different models. Notably, CRAMNet model (MultiHeadAttention_12 Head) achieves a remarkable 100% accuracy across all categories, surpassing other models significantly. Conversely, the LSTM model exhibits the poorest performance with increased misclassifications, particularly for categories 1 and 2, resulting in substantial confusion. While CNN-Transformer models perform well overall, they struggle specifically with categories 2 and 6. Similarly, the DenseNet model demonstrates strong performance across most categories but experiences more misclassifications in categories 1, 2, and 6. Lastly, although the CNN model displays high accuracy in numerous categories, it underperforms notably in categories 1, 2, 5 and 6. Figure 9: Comparison of the results of different methods: (**a**) train accuracy of different models, (**b**) train loss of different models, (**c**) test accuracy of different models, and (**d**) test loss of different models. accuracy of the LSTM model is about 60%, and the model does not perform well when dealing with the current task. The test accuracy of DenseNet model is slightly higher than that of LSTM, nearly 70%. The test accuracy of the CNN-Transformer model is significantly improved with an average accuracy of about 80% and small errors.