## 2 Materials and Methods
### One-Dimensional Convolutional Neural Network (I-D CNN)
#### 2.4.2 Training Configuration and Hyperparameteters  
The training of the 1-D CNN was configured to optimize performance and accuracy. The key settings include the following:  
* Optimizer: The Adam optimizer was selected for its adaptive learning rate properties, aiding faster convergence;
* Execution environment: Training was expedited by utilizing a GPU, harnessing its computational power to process data in parallel;
* Epochs and learning rate: The network was trained for a total of 600 epochs, with an initial learning rate of 0.01, offering a balance between learning efficiency and the opportunity for fine-tuning;
* Data padding and validation: Sequences were left-padded to ensure uniformity in length, with a separate validation dataset provided to monitor generalization capabilities during training;
* Performance monitoring: Training progress and accuracy metrics were tracked in real time, enabling immediate adjustments and insights into the model's learning trajectory.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Name** & **Type** & **Activations** & **Learnable Sizes** \\ \hline \multirow{2}{*}{1} & sequenceinput (Sequence & \multirow{2}{*}{Sequence Input} & \multirow{2}{*}{308 (C) \(\times\) 1 (B) \(\times\) 1 (T)} & \multirow{2}{*}{-} \\  & input with 308 dimensions) & & & \\ \hline \multirow{2}{*}{2} & conv1d\_1 (128 5 \(\times\) 308 & \multirow{2}{*}{1-D Convolution} & \multirow{2}{*}{128 (C) \(\times\) 1 (B) \(\times\) 1 (T)} & Weights: 5 \(\times\) 308 \(\times\) 128 \\  & convolutions with stride 1 & & & Bias: 1 \(\times\) 128 \\  & and padding ‘causal’) & & & \\ \hline \multirow{2}{*}{3} & relu\_1 (ReLU) & ReLU & 128 (C) \(\times\) 1 (B) \(\times\) 1 (T) & - \\ \hline \multirow{2}{*}{4} & layernorm\_1 (Layer & \multirow{2}{*}{Layer Normalization} & \multirow{2}{*}{128 (C) \(\times\) 1 (B) \(\times\) 1 (T)} & \multirow{2}{*}{Offset: 128 \(\times\) 1 Scale: 128 \(\times\) 1} \\  & normalization with 128 channels) & & & \\ \hline \multirow{2}{*}{5} & conv1d\_2 (256 5 \(\times\) 128 & \multirow{2}{*}{1-D Convolution} & \multirow{2}{*}{256(C) \(\times\) 1(B) \(\times\) 1(T)} & Weights: 5 \(\times\) 128 \(\times\) 256 \\  & and padding ‘causal’) & & & Bias: 1 \(\times\) 256 \\ \hline \multirow{2}{*}{6} & relu\_2 (ReLU) & ReLU & 256 (C) \(\times\) 1 (B) \(\times\) 1 (T) & - \\ \hline \multirow{2}{*}{7} & layernorm\_2 (Layer & \multirow{2}{*}{Layer Normalization} & \multirow{2}{*}{256 (C) \(\times\) 1 (B) \(\times\) 1 (T)} & \multirow{2}{*}{Offset: 256 \(\times\) 1 Scale: 256 \(\times\) 1} \\  & normalization with 256 channels) & & & \\ \hline \multirow{2}{*}{8} & globalavgpool1d (1-D global & \multirow{2}{*}{1-D Global Average Pooling} & \multirow{2}{*}{256 (C) \(\times\) 1 (B)} & \multirow{2}{*}{-} \\  & average pooling) & & & \\ \hline \multirow{2}{*}{9} & fc (3 fully connected layer) & Fully Connected & 3 (C) \(\times\) 1 (B) & Weights: 3 \(\times\) 256 Bias: 3 \(\times\) 1 \\ \hline \multirow{2}{*}{10} & softmax (softmax) & Softmax & 3 (C) \(\times\) 1 (B) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Layer information of 1-D CNN architecture.  
This 1-D CNN architecture and its training regimen exemplify the application of convolutional neural networks to sequence data, highlighting their potential to capture the temporal dependencies and features critical for accurate sequence classification.