## 3 Fishing Gear Type Identification from Automatic Identification System Trajectory Data
### The Proposed Deep Neural Network Model for Fishing Gear Type Identification
#### 3.4.2 Feature Extraction and Prediction Modules
For each node of a fully connected layer, the input dimension is \(m\times 1\) where m is the number of nodes in the preceding layer, and the output dimension is \(p\times 1\). In the fully connected layers, each node is connected to all nodes of the preceding layer. The output \(N_{i}^{l+1}\) of the \(i\)-th node at layer 1 is computed as follows:  
\[N_{i}^{l+1}\ =\ f\left(\sum_{j\,=\,1}^{n}w_{i}^{l+1}N_{j}^{l}+b_{i}^{l+1}\right) \tag{9}\]  
here, \(w_{(i,j)}^{l+1}\) is the connection weight between the \(i\)-th node at layer 1 and the \(j\)-th node at layer 1 +1, \(b_{i}^{l+1}\) is the bias term for the \(i\)-th node at layer \(l+1\), and \(f\) denotes the activation function. In FCNN, the ReLU activation function is used. The outputs of the 1D CNN-based feature extraction submodule for ship movement data and the FCNN-based feature extraction module for environmental data are concatenated and passed to the prediction module with the following architecture:  
\[Input(60,1)-FCL(50)-Dropout(0.2)-FCL(30)-Dropout(0.2)-SOFTMAX-Output(6,1)\]  
The prediction module consists of a fully connected network with two hidden layers, each of which has 50 and 30 nodes, respectively, and the output layer has 6 nodes, which equals the number of fishing gear types. The final output is computed by applying the Softmax operation to the computed results of output nodes. Moreover, in order to improve the performance, the proposed modules contain some additional operational layers for batch normalization and dropout operations. The Softmax operation [27] is computed using Equation (10), which makes the final outputs nonnegative and sum to one. \[f_{k}\ =\ \frac{\exp(\sum_{j\,=\,1}^{m}w_{jk}x_{j})}{\sum_{l\,=\,1}^{K}\exp( \sum_{j\,=\,1}^{m}w_{jk}x_{j})} \tag{10}\]  
where \(f_{k}\) denotes the final output for the \(k\)-th node, which corresponds to the probability of the input belonging to the \(k\)-th fishing gear type. \(x_{i}\) is the output of the i-th node at the preceding layer, \(w_{jk}\) is the weight between the \(j\)-th node at the preceding layer and the \(k\)-th node at the output layer, \(m\) is  
Figure 6: Convolutional neural network (CNN)-based feature extraction module. the number of nodes at the preceding layer, and \(K\) is the number of nodes (i.e., the number of fishing gear types) at the output layer. The proposed model is trained to minimize the error function using a gradient descent-based training algorithm. The fishing gear-type classification problem is a kind of multi-class classification problem. The proposed method hence uses the categorical cross-entropy function as the error function \(E\), which is defined as follows [28]:  
\[E\ =\ -\sum_{i\ =\ 1}^{K}t_{i}\log f_{i} \tag{11}\]  
where \(t_{i}\) is the ground truth value for the i-th fishing gear type, and \(f_{i}\) is the final output for the \(i\)-th fishing gear type, which is computed by the prediction module. For the performance evaluation of the trained model, the test data was generated from the trajectory data of the days from which the training data was not generated. A total of 480 trajectory window data records \(TW(i)\) of each fishing ship were generated by moving the sliding trajectory window TW by 3 min (length of 24 h per each day) for testing as shown in Figure 4. Among the generated trajectory window records, the records for non-fishing activity are ignored. We used the performance index TPI, called the trajectory window-wise performance index, which is defined in Equation (12). \[\text{TPI}\ =\ \frac{\sum_{d\ =\ 1}^{D_{br}}\sum_{s\ =\ 1}^{S_{d}}\sum_{n\ =\ 1}^{K_{d}}\delta\left(f\left(TW_{s}^{d}(n)\right),\ t_{s}^{d}(n)\right)}{ \sum_{d\ =\ 1}^{D_{br}}\sum_{s\ =\ 1}^{S_{d}}K_{s}^{d}} \tag{12}\]  
where \(D_{br}\) is the number of days for which trajectory data are used for testing, \(S_{d}\) is the number of ships engaged in fishing activity in a specific day \(d\), and \(K_{s}^{d}\) is the number of trajectory window data records for day \(d\) and ship \(s\).