## 4 Experimental Validation  
The proposed FSSML-EDM model was simulated using a benchmark dataset from UCI repository, which comprises of 649 samples with 32 features and 2 class labels as illustrated in Table 1. The parameter settings are provided as follows: learning rate, 0.01; dropout, 0.5; batch size, 5; and number of epochs, 50. For experimental validation, the dataset is split into 70% training (TR) data and 30% testing (TS) data. Figure 3 highlights a set of confusion matrices produced by the FSSML-EDM model on the test data. The figure indicates that the FSSML-EDM model resulted in effectual outcomes. On entire dataset, the FSSML-EDM model identified 545 samples into pass and 86 samples into fail. In addition, on 70% of the training dataset, the FSSML-EDM model identified 390 samples into pass and 53 samples into fail. Moreover, on 30% of testing dataset, the FSSML-EDM model identified 155 samples into pass and 33 samples into fail. Table 2 offers a comprehensive EDM outcome of the FSSML-EDM model on test dataset. The experimental values indicated that the FSSML-EDM model accomplished maximum outcomes on all datasets. Figure 4 provides brief classification results of the FSSML-EDM model on entire dataset. It can be inferred from the figure that the FSSML-EDM model classified pass instances for \(accu_{y},\;prec_{n},\;reca_{l},\;F_{score},\;MCC\), and kappa of 97.23%, 97.50%, 99.27%, 98.38%, and 89.08% respectively. Moreover, the figure shows that the FSSML-EDM model classified fail instances for \(accu_{y},\;prec_{n},\;reca_{l},\;F_{score},\;MCC\), and kappa of 97.23%, 95.56%, 86%, 90.53%, and 89.08%, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Details** & **Values** \\ \hline Number of samples & 649 \\ \hline Feature count & 32 \\ \hline Class count & 2 \\ \hline \multirow{2}{*}{Source} & [[https://archive.ics.uci.edu/ml/datasets/student+performance](https://archive.ics.uci.edu/ml/datasets/student+performance)]([https://archive.ics.uci.edu/ml/datasets/student+performance](https://archive.ics.uci.edu/ml/datasets/student+performance)) (accessed on 30 January 2022) \\ \end{tabular}
\end{table} 
Table 1: Dataset details. Figure 5 provides detailed classification results of the FSSML-EDM model on 70% of the training dataset. The figure reveals that the FSSML-EDM model classified pass instances for \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(F_{score}\), \(MCC\), and kappa of 97.58%, 97.50%, 99.74%, 98.61%, and 89.57%, respectively. In addition, the figure shows that the FSSML-EDM model classified fail instances for \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(F_{score}\), \(MCC\), and kappa of 97.58%, 98.15%, 84.13%, 90.60%, and 89.57% respectively. Figure 3: Confusion matrix of FSSML-EDM technique on test data. (**a**) Entire dataset, (**b**) 70% of training dataset, and (**c**) 30% of testing dataset. Figure 6 offers brief classification results of the FSSML-EDM approach on 30% of testing dataset. The figure exposes that the FSSML-EDM algorithm classified pass instances for \(accu_{y}\), \(prec_{n}\), \(reca_{I}\), \(F_{score}\), \(MCC\), and kappa of 96.41%, 97.48%, 98.1%, 97.798%, and 88.22%, respectively. Moreover, the figure shows that the FSSML-EDM approach classified fail instances for \(accu_{y}\), \(prec_{n}\), \(reca_{I}\), \(F_{score}\), \(MCC\), and kappa of 96.41%, 91.67%, 89.19%, 90.41%, and 88.22%, respectively. Figure 7 illustrates the training and validation accuracy inspection of the FSSML-EDM technique on the applied dataset. The figure of the FSSML-EDM approach offers maximum training/validation accuracy on the classification process. Figure 4: Result analysis of FSSML-EDM technique on entire dataset. Figure 5: Result analysis of FSSML-EDM technique on 70% of training dataset. Next, Figure 8 reveals the training and validation loss inspection of the FSSML-EDM approach on the applied dataset. The figure shows that the FSSML-EDM algorithm offers reduced training/accuracy loss on the classification process of the test data.