# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 4 Results and discussions
### Fine Tuning Models Hyperparameters  
To improve the effectiveness of our system, optimizing hyperparameters is crucial. Table 8 lists the hyperparameters commonly used in VGG16, LSTM, and LightGBM models. In this section, a grid search technique was used on the selected dataset to optimize hyperparameters like learning rate, dropout value, LSTM unit, regularization L2, and optimizer. The grid search involved changing the hyperparameter values so that the results of the best combination of them could be compared together. Table 9 shows the evaluated and optimal values for each hyperparameter, determined by maximizing accuracy and minimizing computation time. For the learning rate, the tested values were (0.1, 0.01, 0.001, 0.05, and 0.09), and the optimized value was (0.05). The dropout values were tested at (0.2, 0.25, 0.5, and 3), and the optimized value was (0.2). The LSTM unit values were tested at (32, 64, and 128), and the optimized value was (32). Regularization was tested at (0.01, 0.001, 0.0001, 0.09, and 0.9) and the optimized value (0.09). Finally, optimizer techniques (RMSprop and Adam) were tested, with Adam selected as the optimal choice. \begin{table}
\begin{tabular}{c c c c c c} \hline
**GPU** & **Learning Rate** & **Number of Leaves** & **Feature Fraction** & **Loss** & **Validation** \\ \hline Google & & & & & \\ Collaboratory T4 & 0.05 & 50 & 0.8 & Multi log loss & 10-fold \\ \hline \end{tabular}
\end{table} 
Table 6: Hyperparameters for LightGBM model. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Loss** & **Training Time (s)** \\ \hline VGG19 & 94.4\% & 98.6\% & 98.9\% & 98.7\% & 18.2 & 194.535 \\ Inception V3 & 93.7\% & 98.1\% & 98.7\% & 98.4\% & 14.5 & 228.726 \\ Densenet121 & 93.7\% & 97.5\% & 98.4\% & 97.8\% & 13.5 & 227.383 \\ VGG16 & 98.7\% & 98.2\% & 98.7\% & 98.4\% & 6.2 & 205.521 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison between different models with VGG16. \begin{table}
\begin{tabular}{c c c c c} \hline
**GPU** & **Activation Function** & **Optimizer** & **Loss** \\ \hline Google Collaboratory T4 & Softmax & Adam & Categorical cross-entropy \\ \hline \end{tabular}
\end{table} 
Table 5: Hyperparameters for LSTM model. Among the seven experiments conducted to train the models for biometric human identification, the best-performing model was achieved by optimizing the hyperparameters in Table 9. As we can see from Table 10, experiment 4 achieves the best performance. In this experiment, the estimators were set to 25, the number of leaves in each tree was set to 50, the maximum depth of each tree was set to 10, and the feature bagging and fraction were set to 0.8. These hyperparameters were chosen after careful experimentation and tuning, and they helped to improve the accuracy of the model significantly. Overall, by carefully selecting and optimizing these hyperparameters, the model achieved a high level of accuracy in biometric human identification tasks. This approach has significant potential for improving security and surveillance systems in a range of applications.