## 5 Results and Evaluations
In the weighted average case, the precision score is 92%, the recall score is 93%, the f1 score is 92%, and the support score is 201. This classification report examined the performance evaluation metrics of our proposed model. Figure 9: The metric performance analysis of our proposed BiGR approach. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Epoch**} & \multicolumn{2}{c}{**Training Time/Step**} & \multicolumn{2}{c}{**Training Loss**} & \multicolumn{2}{c}{**Training Accuracy**} & \multicolumn{1}{c}{**Validation Loss**} & \multicolumn{1}{c}{**Validation Accuracy**} \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 7: The performance effects of the number of epochs on the BiGR model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Average Case**} & \multicolumn{6}{c}{**Performance Metrics**} \\ \cline{2-7}  & **Precision \%** & **Recall \%** & **F1 Score \%** & **Support Score** \\ \hline Macro Average & 91 & 90 & 91 & 201 \\ Weighted Average & 92 & 93 & 92 & 201 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The BiGR classification report analysis by the average case. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Proposed**} & \multicolumn{6}{c}{**Performance Metrics**} \\ \cline{2-7}  & **Epoch** & **Training Accuracy \%** & **Testing Accuracy \%** & **Precision\%** & **Recall\%** & **F1 Score\%** & **ROC Accuracy \%** & **Log Loss** \\ \hline BiGR & 5 & 72 & 69 & 48 & 69 & 57 & 50 & 10.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The proposed BiGR model results in evaluations without hyperparameter tuning. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Proposed**} & \multicolumn{6}{c}{**Performance Metrics**} \\ \cline{2-7}  & **Epoch** & **Training Accuracy \%** & **Testing Accuracy \%** & **Precision\%** & **Recall\%** & **F1 Score\%** & **ROC Accuracy \%** & **Log Loss** \\ \hline BiGR & 20 & 94 & 93 & 92 & 93 & 92 & 90 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The proposed BiGR model results in evaluations with hyperparameter tuning. the model loss is decreased. The analysis of training accuracy and validation accuracy demonstrates that with the increase in the epochs, the accuracy scores also increased. This analysis validates the performance of the proposed model in time series. The ROC curve analysis of our proposed BiGR model is examined in Figure 11. The ROC curve analysis is related to determining the area under the curve. The higher the ROC curve area better the proposed model is in classification. The analysis shows that our proposed model achieved an efficient ROC curve area. This analysis validates our proposed model in the best classification task. Figure 11: The ROC (receiver operating characteristic) curve analysis of POP at different threshold values. Figure 10: The time series analysis of proposed BiGR is represented as: (**a**) the time series analysis of proposed BiGR among the training loss and validation loss; (**b**) the time series analysis of proposed BiGR among the training accuracy and validation accuracy. The comparative performance analysis of the proposed model with other state-of-the-art studies is analyzed in Table 12. The state-of-the-art studies from the year 2019 to 2022 are taken for comparison. The proposed approach outperformed with high accuracy compared to the past applied state-of-the-art studies. This analysis demonstrates that our proposed model achieved high results in comparison.