## 4 Experiments and Results
### Results  
As described previously, the dataset was partitioned into training and test sets using an 80:20 split, preserving the original protein sequence order to maintain intra-chain structural integrity. Additionally, 20% of the training set was reserved for validation during model training using the validation_split parameter in Keras. The proposed architecture comprises five stacked bidirectional LSTM layers with hidden unit sizes of 1024, 512, 256, 128, and 64, respectively. Each Bi-LSTM layer is followed by a dropout layer with a rate of 0.2 to prevent overfitting. The final layer is a fully connected softmax classifier, configured with three output units for Q3 classification or eight units for Q8 classification, depending on the task. The input to the model is a tensor of shape \((L,D)\), where \(L\) denotes the fixed subsequence length (25, 50, 75, or 100 residues) and \(D\) corresponds to the embedding dimension, ranging from 32 to 1024, as determined by autoencoder-based dimensionality reduction. Model training was conducted using the Adam optimizer with a learning rate of 0.00005, a batch size of 128, and a maximum of 100 epochs. Early stopping was employed with a patience of five epochs based on validation loss to prevent overfitting. All models were implemented using TensorFlow 2.9 and Keras. Training was carried out on a workstation equipped with an 11 th Gen Intel(r) Core(tm) i7-11700F @ 2.50 GHz processor and 32 GB RAM, including an RTX 3060 Ti GPU card. This proposed recurrent architecture was selected after comparative evaluations with alternative deep learning configurations, offering a robust trade-off between classification accuracy and computational efficiency. Model performance was evaluated under multiple configurations of reduced feature dimensions and subsequence lengths, with Q3/Q8 results reported separately to capture task-specific behavior. Figure 6 shows training and validation loss curves for different subsequence lengths. All settings demonstrate stable convergence, though 50-residue subsequences yield the most balanced learning curve. Longer subsequences (75-100) exhibit smoother validation loss but require more training epochs. From a biological standpoint, secondary structure elements such as \(\alpha\) helices and \(\beta\) strands typically span 8-15 and 5-10 residues, respectively. However, these motifs do not exist in isolation; rather, they often occur in organized clusters or domains that range from 40 to 60 residues in length, as noted in previous studies [44; 45]. Thus, a 50-residue window aligns well with the typical size of such secondary structure-rich regions, enabling the model to capture inter-motif dependencies and local folding patterns that would be lost in shorter subsequences. Table 4 shows that 1024-dimensional features consistently deliver the highest Q3 classification performance. Notably, reducing the feature size to 256 dimensions preserves over 99% of performance while substantially lowering training cost. Performance degradation becomes more noticeable at 32 dimensions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature Dimension**} & \multicolumn{4}{c}{**Subsequence Length**} \\ \cline{2-5}  & **25** & **50** & **75** & **100** \\ \hline
1024 & 0.804 & 0.8049 & 0.8045 & 0.8016 \\
512 & 0.7976 & 0.8031 & 0.8031 & 0.7975 \\
256 & 0.7991 & 0.8023 & 0.8002 & 0.7981 \\
128 & 0.7990 & 0.8005 & 0.7969 & 0.7964 \\
64 & 0.7989 & 0.7976 & 0.7946 & 0.7976 \\
32 & 0.7760 & 0.7699 & 0.7559 & 0.7822 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: F1 scores for 3-class (Q3) PSSP classification. Table 5 reveals that models with larger feature dimensions converge faster but require more GPU memory. The 256D-50L configuration offers the best balance between convergence speed and accuracy. Table 6 shows that Q8 classification is more sensitive to dimensionality reduction than Q3. The 32D representation yields up to 9.8% lower F1 scores, though longer subsequences (e.g., 75 residues) partially mitigate this loss.