# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 5 Performance Evaluations
### Ablation Study  
To identify the contribution of individual architectural elements, we carried out an ablation study by comparing the performance of the full LSTM + CNN model with its components. The accuracy of the CNN-alone model was 74.33%, and that of the LSTM-alone model was 71.21%. The performance comparison of individual and combined architectures is illustrated in [Table 4]. The hybrid model had a significantly higher accuracy of 100%, which points to the additive character of combining spatial feature learning (CNN) and temporal sequence learning (LSTM). The prediction performance of the hybrid CNN-LSTM for cardiac disease is examined in this section. Numerous experiments were conducted on hybrid CNN-LSTM networks to determine the ideal parameters for deep learning, which requires a large number of parameters. The following describes the pseudocode. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model Architecture** & **Accuracy (\%)** & **Remarks** \\ \hline CNN-only & 74.33 & Effective at extracting spatial features \\ \hline LSTM-only & 71.21 & Captures temporal dependencies in sequences \\ \hline Hybrid LSTM + CNN & 100.00 & Combines spatial and temporal learning for optimal performance \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study: performance comparison of individual and combined architectures. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Measured Value** & **System Specification** \\ \hline Training Time per Epoch & 2.1 s & 16 GB RAM, NVIDIA \\  & & GTX 1080 GPU \\ \hline Total Training Duration (200 Epochs) & \(\sim\)420 s (7 min) & \\ \hline Average Memory Usage (Training) & 3.5 GB & \\ \hline Inference Time per DNA Sequence & 0.07 s & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational efficiency analysis. Normalization is applied so that the inputs range from 0 to 1. The chosen features are input into the convolutional layer, which has two parameters, the activation function and the kernel size, which determine the convolutional window's dimensionality. Here, ReLU is utilized. After the fully connected layer's findings are received, the model is set up in the compile function. The parameters utilized are (1) a loss function to minimize algorithmic loss and (2) an optimizer to compile the model. When the model is fitted, it is assessed using the test data. The last calculation's accuracy indicates how well the model worked with the test data. A total of 200 iterations of CNN + LSTM are performed. When employed as an optimizer, AD-AM has a learning rate of 0.001. The loss function that is employed is binary cross-entropy. Results indicate the accuracy of six models for DNA sequence classification with accuracy as the metric. Among the models under comparison, the highest accuracy of 100% was achieved by the Hybrid LSTM + CNN, which indicated how powerful the model was with the classification task and proved its ability to learn spatial and sequential patterns of DNA data. This was followed very closely by the DeepSea model, which had an accuracy of 76.59%, hence proving the suitability of convolutional neural networks for this task. Dimensionality reduction had an accuracy of 68.15%, outperforming DeepVariant that had 67.00%. Among the models that were tested, the transformation model obtained comparatively lower accuracies of 43.75%, whereas the graph neural network obtained 30.71%, which indicates scope for improvements while handling this classification problem. These findings clearly indicate the role of hybrid architecture and sophisticated feature extraction algorithms towards performing DNA sequence analysis. The model is initialized in the compile function after receiving outputs from the fully connected layer, and the following parameters are used:  
1. Loss function to reduce the loss suffered by the algorithm;
2. Optimizer to train the model. The model is tested on the test data when fit. Accuracy is calculated at the end, and it shows how well the model has worked on the test data. CNN + LSTM is trained for 200 epochs.