## 5 Results and Evaluations  
The results and evaluations of our proposed novel approach for asteroseismology classification are examined in this section. The data analysis and deep learning model building are based on the Python TensorFlow tool. The performance evaluation metrics are the accuracy score, precision score, recall score, f1 score, ROC accuracy score, and log loss. The performance evaluation metrics are examined to validate our proposed approach scientifically. The accuracy score is the correct number of predicted values by the proposed model. The accuracy score shows how well our model predicts the target label. The accuracy score of our model on training data is 94%, and on testing data is 93%. This shows that our proposed model is in generalized form. The mathematical notation to express the accuracy score is analyzed in Equation (9). \[\text{Accuracy}\,=\frac{\text{Correct\,Predictions}}{\text{All\,Predictions}}, \tag{9}\]  
The recall and precision evaluation metrics measure the model outcome data relevancy. The precision score analyzes how reliable the proposed model is in classifying data samples as positive. The recall determines the dataset's positive class prediction number of all positive samples. The precision score of our proposed model is 92%, and the recall score of our model is 93%. The mathematical notation to express recall and precision are Equations (10) and (11), respectively, where the true positive is correctly classified as a positive sample, and the false negative is incorrectly classified as a positive sample, which is unfavorable. The false positive is incorrectly classified as a negative sample. Figure 8: The architectural analysis of our proposed BiGR approach. \[\text{Recall}\,=\frac{\text{\it True\ Positives}}{\text{\it True\ Positives}+ \text{\it False\ Negatives}}, \tag{10}\]  
\[\text{Precision}\,=\frac{\text{\it True\ Positives}}{\text{\it True\ Positives}+ \text{\it False\ Positives}}, \tag{11}\]  
The F1 score sums up the predictive performance by combining the recall and precision values. The F1 score is based on the harmonic mean of recall and precision values. The f1 score value of our proposed model is 92%. The ROC curve accuracy analysis is conducted to evaluate our model using different probability threshold measures. The ROC curve accuracy determines the trade-off between specificity and sensitivity between each class. The ROC curve accuracy score of our proposed model is 90%. The mathematical notations to analyze the F1 score and ROC curve accuracy is expressed in Equations (12) and (13), respectively. \[\text{F1 score}\,=2*\frac{\text{\it Recall}\,*\,\text{\it Precision}}{\text{\it Recall }+\text{\it Precision}}, \tag{12}\]  
\[\text{ROC curve accuracy}\,=\int_{-\infty}^{+\infty}AUC_{x}dF_{(x|D=1)(x)}, \tag{13}\]  
The bar chart-based performance analysis of our proposed approach is analyzed in Figure 9. The performance effects of the number of epochs during training of the proposed BiGR model are examined in Table 7. The analysis is based on the training time, training loss, accuracy, validation loss, and validation accuracy of one epoch at a time. The analysis demonstrates that training time and loss are high when the model is trained at the first epoch. The accuracy score values are low. The loss and time decreased during training with the increase in an epoch. The accuracy results are increased. This analysis examined the training validity of our proposed model. The proposed BiGR model results without and with hyperparameter tuning are examined in Tables 8 and 9, respectively. The analysis demonstrates that we have achieved the best performance metrics accuracy scores by applying hyperparameter tuning to our proposed model. The best-fit parameters that give us the highest possible score in asteroseismology classification are analyzed. The proposed BiGR model classification report is analyzed in Tables 10 and 11. The analysis is based on the parameter's precision, recall, f1 score, support score category-wise, and the average case.