# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 4 Results and discussions
### Fine Tuning Models Hyperparameters
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Model** & **Hyperparameter** & **Description** \\ \hline VGG16 & Learning Rate (LR) & Determines the step size for gradient updates during training. & \\  & Batch Size & Number of samples processed before updating the model weights. & Number of times the entire dataset is passed through the model during training. & \\  & Optimizer & The optimization algorithm used for weight updates (e.g., SGD, Adam, RMSprop). & \\  & Dropout Rate & Probability of dropping out neurons during training to prevent overfitting. & \\  & Weight Decay (L2 & Penalty added to the loss function to discourage large weight values. \\ LSTM & Number of LSTM Units & Number of memory cells (neurons) in the LSTM layer. & \\  & Learning Rate (LR) & Step size for updating LSTM model parameters during training. & \\  & Batch Size & Number of sequences processed together before updating the model. & \\  & Number of Epochs & Number of iterations over the entire dataset during training. & \\  & Optimizer & The optimization algorithm used for LSTM weight updates (e.g., Adam, RMSprop). & \\  & Dropout Rate & Probability of dropping out LSTM neurons during training to prevent overfitting. & \\  & Weight Decay (L2 & Penalty added to the loss function to discourage large weight values. \\ LightGBM & Regularization) & Penalty added to the loss function to discourage large weight values. \\  & Learning Rate (LR) & Step size for updating the boosting model during training. & \\  & Number of Estimators & Number of boosting rounds (trees) in the LightGBM model. & \\  & Max Depth & Maximum depth of the decision trees in the boosting model. & \\  & Min Child Samples & Minimum number of samples required in a leaf node to split a tree. & \\  & Feature Fraction & Fraction of features used in each boosting iteration to prevent overfitting. & \\  & Bagging Fraction & Fraction of training data used in each boosting iteration to prevent overfitting. & \\  & Lambda (L2 Regularization) & L2 regularization term applied to the leaves’ values in the decision trees. & \\ \hline \end{tabular}
\end{table} 
Table 8: The commonly used hyperparameters in VGG16, LSTM, and LightGBM models. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Model** & **Learning** & **Dropout** & **LSTM Unit** & **Regularization** & **Optimizer** & **Estimators** & **Max Depth** & **Min Child** & **Feature \& Bagging Fraction** \\ \hline
1 & 0.001 & 0.2 & 32 & 0.001 & [PERSON] & 100 & 6 & 20 & 0.7 \\
2 & 0.01 & 0.5 & 32 & 0.01 & [PERSON] & 50 & 6 & 20 & 0.7 \\
3 & 0.09 & 0.5 & 32 & 0.001 & [PERSON] & 120 & 8 & 75 & 0.8 \\
4 & 0.05 & 0.2 & 32 & 0.09 & [PERSON] & 25 & 10 & 50 The testing performance of the pre-trained models we compared in terms of the confusion matrix is shown in Figure 8. The main diagonal of the confusion matrix represents the correctly classified instances where the predicted class matches the actual style. Off-diagonal elements indicate misclassifications, with each cell showing how many models were incorrectly assigned to a particular category. As shown in the figure, incorrect predictions are represented in the red squares. The comparison shows the accuracy of each feature extraction model. As we can see from Figure 9, the VGG16 model got the best accuracy at 98.7%. \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & 97.7\% & 97.7\% & 97.7\% & 0.97 \\
2 & 98\% & 96\% & 98\% & 0.98 \\
3 & 97.7\% & 95.4\% & 96.3\% & 0.97 \\
4 & 98.7\% & 98.01\% & 97.1\% & 0.98 \\
5 & 97.5\% & 96.3\% & 95.1\% & 0.97 \\
6 & 97.3\% & 95.1\% & 96.2\% & 0.97 \\
7 & 94.9\% & 94.2\% & 95.3\% & 0.94 \\ \hline \end{tabular}
\end{table} 
Table 10: Experimental results were obtained by a proposed ensemble classifier (VGG16-LSTM-LightGBM) with a preprocessing step on seven different runs.