## 4 Experiments
In fact, this meets the objective of the proposed model which does not only classify Zakat declarations but also prioritizes the under-reporting risk based on the expected revenue at risk for a better governance of the auditing resources. Further investigation of the results reported in Figure 3b showed that the leveled segment of the graph is caused by considerable subset of test instances that were correctly assigned to the \"under-reporting\" \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Data**} & \multirow{3}{*}{**Learning Rate**} & \multirow{3}{*}{**Batch Size**} & \multicolumn{6}{c}{**Validation**} \\ \cline{3-10}  & & & \multicolumn{4}{c}{**Classification**} & \multicolumn{4}{c}{**Regression**} \\ \cline{3-10}  & & & **Accuracy** & **Precision** & **Recall** & **F1-Measure** & **MSE** & **RMSE** & **MAE** \\ \hline \multirow{3}{*}{No resampling} & \(1\times 10^{-3}\) & 256 & 0.96 & 0.72 & 0.77 & 0.74 & 0.390 & 0.625 & 0.276 \\ \cline{2-10}  & \(1\times 10^{-4}\) & 512 & 0.94 & 0.64 & 0.75 & 0.67 & 0.390 & 0.624 & 0.299 \\ \hline \multirow{3}{*}{SMOTE} & \(1\times 10^{-3}\) & 256 & 0.98 & 1.00 & 0.96 & 0.98 & 0.502 & 0.708 & 0.294 \\ \cline{2-10}  & \(1\times 10^{-4}\) & 512 & 0.97 & 0.80 & 0.74 & 0.77 & 0.475 & 0.689 & 0.289 \\ \hline \multirow{3}{*}{SMOTE + RU} & \(1\times 10^{-3}\) & 256 & 0.99 & 0.99 & 0.99 & 0.99 & 0.478 & 0.691 & 0.326 \\ \cline{2-10}  & \(1\times 10^{-4}\) & 512 & 0.99 & 0.99 & 0.99 & 0.99 & 0.484 & 0.696 & 0.290 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained using the proposed model for different batch sizes and learning rates. class with high probability confidences; however, the associated expected revenue at risk was relatively small. Finally, in Figure 4, we compare the performance of the best-proposed model with five typical machine learning models, namely k-Nearest Neighbors (KNN), the Naive Bayes (NB) classifier, Logistic Regression (LR) [46], CART decision tree and Linear Discriminant Analysis (LDA). Particularly, the proposed model outperforms CART with an increase of 6% and 5% in terms of accuracy and F1-score, respectively. Moreover, it overtakes the LDA model with an improvement of 17% and 11% in terms of accuracy and F1-score, respectively. One should mention that in addition to this improvement recorded at the classification level, the proposed model allows effective prioritization of the detected under-valuation cases as illustrated in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{\(\beta\)} & \multicolumn{6}{c}{**Validation**} \\ \cline{3-10}  & & \multicolumn{6}{c}{**Classification**} & \multicolumn{6}{c}{**Regression**} \\ \cline{3-10}  & & **Accuracy** & **Precision** & **Recall** & **F1-Measure** & **MSE** & **RMSE** & **MAE** \\ \hline Resample by 10\% & \(\beta=0.4\) & 0.98 & 1.00 & 0.96 & 0.98 & 0.547 & 0.739 & 0.335 \\ \cline{2-10} SMOTE & \(\beta=0.5\) & 0.96 & 0.72 & 0.76 & 0.74 & 0.457 & 0.689 & 0.289 \\ \hline Resample by 15\% & \(\beta=0.4\) & 0.98 & 1.00 & 0.96 & 0.98 & 0.479 & 0.722 & 0.319 \\ \cline{2-10} SMOTE & \(\beta=0.5\) & 0.96 & 0.72 & 0.76 & 0.74 & 0.417 & 0.663 & 0.274 \\ \hline Resample by 10\% & \(\beta=0.3\) & 0.99 & 0.99 & 0.99 & 0.99 & 0.502 & 0.707 & 0.296 \\ \cline{2-10} SMOTE + RU & \(\beta=0.4\) & 0.97 & 0.99 & 0.95 & 0.97 & 0.497 & 0.689 & 0.311 \\ \hline Resample by 15\% & \(\beta=0.3\) & 0.99 & 0.99 & 0.99 & 0.99 & 0.402 & 0.583 & 0.264 \\ \cline{2-10} SMOTE + RU & \(\beta=0.4\) & 0.99 & 0.99 & 0.99 & 0.99 & 0.427 & 0.587 & 0.263 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results achieved by the proposed model using different \(\alpha\) values after data resampling by 10% and 15% using SMOTE and RU. Figure 3: Results obtained using the proposed model: (**a**) ROC curve and (**b**) revenue at risk vs. auditing rate.