## 4 Experimental Results
### Classification Results
#### 4.3.3 Deep Learning Baselines  
In this section, we delve into the performance of various deep learning models, specifically focusing on LSTM and CNN architectures, as well as a hybrid model that combines LSTM and CNN (LSTM-CNN). These models are evaluated and compared to our proposed LSTM-Attention-BiTCN model. The configuration details for each of these deep learning models are as follows:  
* LSTM-CNN Ensemble Model: This model integrates the features of both LSTM and CNN. It comprises an input layer, an embedding layer, and a single LSTM layer with 100 units, followed by a CNN layer equipped with three filters, a kernel size of eight, and employing a ReLu activation function. The model structure includes a dropout layer after the initial input layer, with a dropout rate of 0.1, and a flattening layer preceding the final dense layer. The dense layer, with a single unit and a \"sigmoid\" activation function, is responsible for the final classification task. Training of this model \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Baseline Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline K-Nearest Neighbor & 0.2195 & 0.7196 & 0.7195 & 0.7195 \\ Random Forest & 0.8037 & 0.8037 & 0.8037 & 0.8037 \\ Decision Tree & 0.7687 & 0.7688 & 0.7687 & 0.7687 \\ Gradient Boost & 0.8005 & 0.8005 & 0.8005 & 0.8005 \\ XGBoost & 0.8396 & 0.8401 & 0.8396 & 0.8396 \\ Logistic Regression & 0.7257 & 0.7351 & 0.7257 & 0.7228 \\ LSTM-Attention-BiTCN & 0.9405 & 0.9385 & 0.9424 & 0.9405 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison performance of machine learning baseline models and the proposed model. utilized the \"adam\" optimizer at a default rate of 0.001 and binary cross-entropy loss, spanning up to 30 epochs and batch size of 64. Similar to the LSTM-Attention-BiTCN model, EarlyStopping and ReduceLROnPlateau callbacks were applied. The model training concluded early at epoch 24 with a learning rate of \(1\times 10^{-5}\). * LSTM Model: This model begins with an input layer followed by an embedding layer and a dropout layer with a dropout rate of 0.1. A single LSTM layer with 100 units is situated immediately after the dropout layer. A flattening layer follows before culminating in the final dense layer, which mirrors the settings of the LSTM-CNN model designed for classification. The training process also utilized the \"adam\" optimizer with a standard rate of 0.001 and binary cross-entropy loss, spanning up to 30 epochs and a batch size of 64. Incorporating EarlyStopping and ReduceLROnPlateau callbacks, the training early stopped at epoch 21 with a final learning rate of \(1\times 10^{-4}\). * Bidirectional LSTM (BiLSTM) Model: The architecture begins with an input layer, which is immediately followed by an embedding layer and a dropout layer set at a rate of 0.1. Next, a Bidirectional Long Short-Term Memory (BiLSTM) layer with 100 units is employed, allowing the model to process data in both forward and backward directions for a more comprehensive understanding. A flattening layer is then applied, ensuring that the output is structured appropriately for the final stage. The structure concludes with a dense layer, similar to the LSTM model. With the training settings mirroring those of the LSTM model, the BiLSTM model was stopped early at epoch 22 and had a final learning rate of \(1\times 10^{-5}\). * CNN Model: In the CNN setup, the input layer is followed by an embedding layer and a dropout layer with an identical dropout rate as the other models. The key feature of this model is a one-dimensional convolution layer configured with a filter size of three and a kernel size of eight and utilizing a ReLu activation function. The architecture concludes with a flattening layer, leading into a dense layer that performs the classification, similar to the other models mentioned. The training parameters of this model included the \"adam\" optimizer at a default 0.001 rate and a binary cross-entropy loss over 30 epochs with a batch size of 64.