## 3 Materials and Methods
### Proposed Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Output Shape** & **Param \#** & **Connected to** & **Parameter** & **Value** \\ \hline Input\_1 (InputLayer) & (None, 19,264) & 0 & & & \\ \hline conv1d (Conv1D) & (None, 19,264) & 256 & input\_1[0][0] & filters & 64 \\ \hline transformer\_block\_1 & (None, 19,264) & 0 & input\_1[0][0] & embed\_dim & 64 \\ \hline conv1d\_1 (Conv1D) & (None, 19,264) & 12,352 & conv1d [0][0] & kernel\_size & 3 \\ \hline  & & & & & stride \\ \hline conv1d\_2 (Conv1D) & (None, 19,264) & 12,352 & conv1d\_1[0][0] & padding & \({}^{*}\)same\({}^{\prime}\) \\ \hline lstm (LSTM) & (None, 19,264) & 16,896 & input\_1[0][0] & activation & \({}^{*}\)relu\({}^{\prime}\) \\ \hline transformer\_block\_2 & (None, 19,264) & 25,216 & transformer\_block\_1[0][0] & ff\_dim & 64 \\ \hline conv1d\_3 (Conv1D) & (None, 19,264) & & conv1d\_2[0][0] & rate & 0.1 \\ \hline lstm\_1 (LSTM) & (None, 19,264) & 33,024 & lstm [0][0] & nodes & 64 \\ \hline flatten (Flatten) & (None, 12,288) & 0 & conv1d\_3[0][0] & return\_sequences & True \\ \hline lstm\_2 (LSTM) & (None, 64) & 53,824 & lstm\_1[0][0] & & \\ \hline flatten\_1 (Flatten) & (None, 12,288) & 0 & & & \\ \hline concatenate (Concatenate) & (None, 24,640) & 0 & flatten\_1[0][0], lstm\_2[0][0] & concatenate\_axis & 1 \\ \hline dense\_13 (Dense) & (None, 8) & 192,128 & concatenate [0][0] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ensemble model summary. Figure 6: Ensemble model architecture. (3) LSTM: The model's long-term LSTM layers process the sequence data with the LSTM accepting the original (192, 1) input feature vector. The LSTM architecture comprises two 64-unit layers. The first outputs its hidden state at every time step because the return sequences are labeled \"True\", and the next LSTM layer receives each output. This implies that this setting is required when stacking the LSTM layers. The second LSTM layer does not include this parameter; therefore, it only returns the last output, which is then fed into dense layers to obtain the final predictions. Notably, LSTM networks can predict sequence data, remember information, and avoid the vanishing-gradient problem associated with traditional recurrent neural networks. For each time step (t), the LSTM component simultaneously receives x_t from the input sequence as well as c_t_1 and h_t_1. The LSTM component then calculates the input gate (i_t), output gate (o_t), and cell candidate (g_t) using various combinations of the present input, preceding hidden state, and trainable weights according to the following equations:  
\[\text{i\_t}=\text{sigmoid}\Big{(}\text{W}_{\text{i}}*\Big{[}\text{h}_{\{t-1 \}},\text{x}_{\text{t}}\Big{]}+\text{b}_{\text{i}}\Big{)}. \tag{7}\]  
\[\text{f\_t}=\text{sigmoid}\Big{(}\text{W}_{\text{f}}*\Big{[}\text{h}_{\{t-1 \}},\text{x}_{\text{t}}\Big{]}+\text{b}_{\text{f}}\Big{)}. \tag{8}\]  
\[\text{o\_t}=\text{sigmoid}\Big{(}\text{W}_{\text{o}}*\Big{[}\text{h}_{\{t-1 \}},\text{x}_{\text{t}}\Big{]}+\text{b}_{\text{o}}\Big{)}. \tag{9}\]  
\[\text{g\_t}=\tanh\Big{(}\text{W}_{\text{g}}*\Big{[}\text{h}_{\{t-1\}},\text{ x}_{\text{t}}\Big{]}+\text{b}_{\text{g}}\Big{)}. \tag{10}\]