# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 5 Performance Evaluations
### Ablation Study
ADAM is used as an optimizer with a learning rate of 0.001. Binary cross-entropy is the loss function used. An AUC of 0.86 indicates that the model is good from a discrimination point of view; that is, it clearly separates positive and negative classes. Generally, an AUC score above 0.5 is better than random guessing, and the higher the score, the better the performance. A score of 0.86, then, suggests that the model has a pretty good performance. Curves are one of the popular methods of assessing diagnostic capability for binary classification problems. A graph of true positive rate (TPR = TPr/TPr + FNr) versus false positive rate (FPR = FPr/FPr + TNr) at various classification thresholds is plotted. Figure 4 shows the feature virtualization of the DNA sequence classification pipeline. One whole cycle of the training data is referred to as an epoch in machine learning. It is a hyperparameter of how many times the algorithm must iterate to obtain better accuracy. In every epoch, the network is trained with each instance of the dataset. Figure 5 shows the traditional architecture of LSTM. This is standard LSTM unit architecture, including cell state and gates (input, forget, output). Arrows indicate data transfer between memory cells and gates. Gate operation equations (sigmoid/tanh) are assumed. Architecture facilitates  
The Confusion matrix in [Figure 6] plots the network processing for different learning rates. As the number of iterations is increased with the learning rate, accuracy varies. At a learning rate of 0.001, the merger of CNN and LSTM produced very good accuracy. Figure 4: CNN feature visualization. Figure 5: The traditional architecture of LSTM. Figure 6: The confusion matrix. The Area under the curve (AUC) and the receiver operating characteristic (ROC) plots in Figure 7 show the Receiver Operating Characteristic (ROC) curve with True Positive Rate (TPR) on the y-axis and False Positive Rate (FPR) on the x-axis. Area Under Curve (AUC) is 0.86, which shows good binary classification performance. Diagonal represents random guessing. How close the curve is to the top-left, high sensitivity/specificity it shows. This metric achieves high discrimination power. A line graph graphs training and validation loss versus epochs during model training in Figure 8, illustrating that Loss decreases rapidly before leveling off, showing convergence. Gaps in training/validation lines may indicate overfitting if large. Early stopping likely halted training when validation loss no longer improved. This monitors optimization progress and generalization. Figure 8: Epoch vs. loss. Figure 7: Area under curve and receiver operating characteristic (ROC). The Learning rate vs. accuracy plot in Figure 9 depicts the impact of learning rate (LR) on model accuracy (AUC/ROC). It is capable of plotting accuracy values vs. LR values (say, 0.1, 0.01, 0.001). Optimal LR (0.001 here) is in balance between convergence speed and stability. Curves indicate that very high or very low LR is not optimal. Hyperparameter tuning is necessary for maximum efficiency. Model performance. Bar plot in Figure 10 indicates test loss (0.518), accuracy (0.784), precision (0.791), recall (0.559), and F1-score (0.655). All these indicate the performance of the model on unseen data. The trade-offs between precision (false positives) and recall (false negatives) are evident. The F1-score balances to take a complete measurement. From the model performance results presented in Figure 10, the model seems to have attained a very good level of performance in test loss, test accuracy, precision, recall, and F1  
Figure 10: Model performance. Figure 9: Learning rate vs. accuracy. score. The test loss of 0.518 would mean that the model would be capable of predicting the target variable for new data with a very low number of errors. The test set accuracy of 0.784 shows that the model can correctly classify most instances of the test set. The precision of 0.791 demonstrates that, when the model predicts the positive class, it is correct about 79.1% of the time.