# Study on Classification of Fishing Vessel Operation Types Based on Dilated CNN-IndRNN
## 4 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence Length** & **Batch Size** & **Acc** \\ \hline
32 & 64 & 91.74 \\
32 & 128 & 91.38 \\
32 & 256 & 91.16 \\
64 & 64 & 92.54 \\
64 & 128 & 92.39 \\
64 & 256 & 92.32 \\
128 & 64 & 93.12 \\
128 & 128 & 92.98 \\
128 & 256 & 92.43 \\
256 & 64 & 93.01 \\
256 & 128 & 92.76 \\
256 & 256 & 92.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison table for different sequence lengths and batch sizes. \begin{table}
\begin{tabular}{c c} \hline \hline
**Dilation Rate** & **Acc** \\ \hline
2 & 92.45 \\
3 & 93.04 \\
4 & 92.88 \\
5 & 92.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different dilation rates. gradually increases with the increase in epoch, and the loss value gradually decreases with the increase in epoch, and finally, tends to converge. This study selected a separate dilated CNN model, an Ind RNN model, a CNN model, LSTM, and other models for comparative analysis, and also selected some models studied by other scholars. The summary of their respective model evaluation indicators is shown in Table 5. Among them, accuracy is the proportion of correctly classified instances out of the total instances. Precision is the proportion of true positive instances among all predicted positive instances. It focuses on the accuracy of positive predictions. Recall is the proportion of true positive instances that were correctly identified by the model. It measures the ability of the model to capture all relevant instances. The F1 score is the harmonic mean of precision and recall. It provides a balance between precision and recall. Compared with other models, we can find that the model proposed in this work is the best working model based on all indicators. Figure 11: Loss graph. Figure 10: Accuracy graph. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc** & **Pre** & **macro-F1** & **Recall** \\ \hline Dilated CNN & 0.8874 & 0.8880 & 0.8869 & 0.8882 \\ IndRNN & 0.9090 & 0.9099 & 0.9094 & 0.9101 \\ CNN-LSTM & 0.9156 & 0.9132 & 0.9122 & 0.9124 \\ Dilated CNN-IndRNN & 0.9312 & 0.9310 & 0.9310 & 0.9314 \\ CNN & 0.8687 & 0.8692 & 0.8677 & 0.8694 \\ LSTM & 0.9076 & 0.9103 & 0.9068 & 0.9066 \\ Transformer & 0.8956 & 0.8949 & 0.8960 & 0.8950 \\ TCN & 0.8645 & 0.8631 & 0.8630 & 0.8645 \\ GRU & 0.8663 & 0.8649 & 0.8642 & 0.8657 \\ Bi-LSTM & 0.8905 & 0.8897 & 0.8898 & 0.8903 \\ Bi-GRU & 0.8725 & 0.8728 & 0.8715 & 0.8713 \\ RNN & 0.8587 & 0.8571 & 0.8582 & 0.8569 \\ LightGBM & 0.9301 & 0.9208 & 0.9168 & 0.9064 \\ ConvLSTM & 0.9105 & 0.9102 & 0.9111 & 0.9106 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison table of model results. The receiver operating characteristic curve and the area under curve are graphical indicators for evaluating the performance of binary classification models. The ROC curve is a curve with the false positive rate as the abscissa and the true positive rate as the ordinate, which is used to evaluate the performance of the binary classification model under different thresholds. The AUC value is the area under the ROC curve, which is used to measure the ability of the model to sort the samples. The larger the AUC value, the better the model performance. The curves of the dilated CNN-IndRNN model are shown in Figure 12. All three ROC curves of the model are close to the upper left corner, and the area under the ROC curve is close to 1, indicating that the model classifies well and is able to perform the fishing vessel classification task well. The confusion matrix is a kind of evaluation index of a machine learning model. It is a two-dimensional matrix, in which rows represent real categories, and columns represent predicted categories. The value of each cell represents the number of samples predicted by the model on the test set as a certain category, and the value in the diagonal cell is the correct number of predictions. The confusion matrix heat map visualization can intuitively represent the predicted distribution of the model and use the depth of the color to represent the size of the value.