## 4 Discussion  
Significant findings have emerged. Firstly, the study highlighted the potential of an approach that involves transforming a sparse and incomplete dataset of vessel paths into a consistent set of features that could be universally applicable across all types of vessels, despite variations in the number of observations. While the individual techniques \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**ANN** & **Class1** & **Others** & **A.Mean** & **W.Mean** & **Errors** \\ \hline Deep\_Conic(C3) & 76.36\% & 88.70\% & 82.53\% & 87.57\% & 223 \\ FFBm(C3) & 75.76\% & 90.67\% & 83.21\% & 89.30\% & 192 \\ Conic(C3) & 73.94\% & 93.43\% & 83.69\% & 91.64\% & 150 \\ Conic(C3) & 72.73\% & 93.25\% & 82.99\% & 91.36\% & 155 \\ DeepBp(C3) & 71.52\% & 92.69\% & 82.11\% & 90.75\% & 166 \\ kNN(C3) & 70.30\% & 98.16\% & 84.23\% & 95.60\% & 79 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification of Class 3: TMC. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**ANN** & **Class1** & **Others** & **A.Mean** & **W.Mean** & **Errors** \\ \hline DeepBm(C5) & 88.00\% & 88.11\% & 88.06\% & 88.10\% & 207 \\ DeepBp(C5) & 81.33\% & 93.90\% & 87.62\% & 92.82\% & 125 \\ Conic(C5) & 80.67\% & 96.42\% & 88.54\% & 95.06\% & 86 \\ kNN(C5) & 80.00\% & 98.93\% & 89.47\% & 97.30\% & 47 \\ FFBp(C5) & 80.00\% & 97.67\% & 88.84\% & 96.15\% & 67 \\ DeepConic(C5) & 79.33\% & 96.92\% & 88.13\% & 95.40\% & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification of Class 6: TUG. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**ANN** & **Class1** & **Others** & **A.Mean** & **W.Mean** & **Errors** \\ \hline DeepBm(C5) & 88.00\% & 88.11\% & 88.06\% & 88.10\% & 207 \\ DeepBp(C5) & 81.33\% & 93.90\% & 87.62\% & 92.82\% & 125 \\ Conic(C5) & 80.67\% & 96.42\% & 88.54\% & 95.06\% & 86 \\ kNN(C5) & 80.00\% & 98.93\% & 89.47\% & 97.30\% & 47 \\ FFBp(C5) & 80.00\% & 97.67\% & 88.84\% & 96.15\% & 67 \\ DeepConic(C5) & 79.33\% & 96.92\% & 88.13\% & 95.40\% & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification of Class 5: TU. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**ANN** & **Class1** & **Others** & **A.Mean** & **W.Mean** & **Errors** \\ \hline DeepBm(C5) & 88.00\% & 88.11\% & 88.06\% & 88.10\% & 207 \\ DeepBp(C5) & 81.33\% & 93.90\% & 87.62\% & 92.82\% & 125 \\ Conic(C5) & 80.67\% & 96.42\% & 88.54\% & 95.06\% & 86 \\ kNN(C5) & 80.00\% & 98.93\% & 89.47\% & 97.30\% & 47 \\ FFBp(C5) & 80.00\% & 97.67\% & 88.84\% & 96.15\% & 67 \\ DeepConic(C5) & 79.33\% & 96.92\% & 88.13\% & 95.40\% & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification of Class 6: TUG. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**ANN** & **Class1** & **Others** & **A.Mean** & **W.Mean** & **Errors** \\ \hline DeepBm(C5) & 88.00\% & 88.11\% & 88.06\% & 88.10\% & 207 \\ DeepBp(C5) & 81.33\% & 93.90\% & 87.62\% & 92.82\% & 125 \\ Conic(C5) & 80.67\% & 96.42\% & 88.54\% & 95.06\% & 86 \\ kNN(C5) & 80.00\% & 98.93\% & 89.47\% & 97.30\% & 47 \\ FFBp(C5) & 80.00\% & 97.67\% & 88.84\% & 96.15\% & 67 \\ DeepConic(C5) & 79.33\% & 96.92\% & 88.13\% & 95.40\% & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification of Class 6: TUG. employed to convert vessel trajectories into fixed features were not new, the innovation lay in their combined application, resulting in the transformation of temporal flows into spatial features, derived from highly heterogeneous data. The second significant result was the development of the TOCAT (take one class at a time) research design. While the TOCAT strategy itself is not entirely new, as it is already used in support vector machine algorithms [36; 37] for multinomial classifications, our innovative application of this processing strategy was significant. Existing methods typically rely on the development of a singular, albeit sophisticated, convolutional or recurrent neural network, which shoulders the full responsibility of recognition.