## 4 Experiments  
This section outlines the experiment settings and the dataset used to develop the proposed approach. Moreover, the data preparation and pre-processing techniques considered for this research, in addition, to the training strategy adopted to build the model are revealed. Furthermore, the standard performance measures used to assess the regression and classification results achieved by the model are defined. In this research, Keras library [41] was coupled with the TensorFlow platform [42] and a Spyder [43] open-source environment to implement the proposed work. On the other hand, the hardware specifications include 16 GB dual-channel RAM, an Intel i7 9700 CPU, and Intel UHD GPU 630. Zakat declarations used in this research were provided by the Saudi Zakat, Tax, and Customs Authority (ZATCA) subject to releasing only information related to data and research findings that are considered as non-sensitive by ZATCA. The rationale that supports this decision is to avoid publishing information, relevant to ZATCA strategies, that can be exploited by some taxpayers to adapt their fraudulent behavior. Specifically, the data used in this research experiment consist of Zakat filings randomly selected from the Zakat declarartions collected by ZATCA between January 2018 and April 2022. Table 2 presents a high-level description of this data collection. The attributes enclosed in the ZATCA [1] dataset consist of relevant fields extracted from Zakat forms. Moreover, derived features were designed to enrich the considered dataset. However, due to confidentiality, the authors of this research can share a limited amount of details about the variables that were engineered based Zakat declaration forms. One should note that \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Layer**} & \multicolumn{1}{c}{**Layer**} & \multicolumn{1}{c}{**Neurons**} & \multicolumn{1}{c}{**Activation Function**} & \multicolumn{1}{c}{**Regularizer**} & \multicolumn{1}{c}{**Dropout Ratio**} & \multicolumn{1}{c}{**Input Size**} & \multicolumn{1}{c}{**Output Size**} \\ \hline Input & Input & - & - & - & - & 94 & 94 \\ \hline hidden\_1 & Dense & 94 & Relu & L2 & - & 94 & 94 \\ \hline dropout\_1 & Dropout & - & - & - & 10\% & 94 & 94 \\ \hline hidden\_2 & Dense & 1000 & Relu & L2 & - & 94 & 1000 \\ \hline dropout\_2 & Dropout & - & - & - & 10\% & 1000 & 1000 \\ \hline hidden\_3 & Dense & 500 & Relu & L2 & - & 1000 & 500 \\ \hline dropout\_3 & Dropout & - & - & - & 10\% & 500 & 500 \\ \hline hidden\_4 & Dense & 250 & Relu & L2 & - & 500 & 250 \\ \hline dropout\_4 & Dropout & - & - & - & 10\% & 250 & 250 \\ \hline hidden\_5 & Dense & 50 & Relu & L2 & - & 250 & 50 \\ \hline Reg\_output & Output & \(1\) & Linear & - & - & 50 & \(1\) \\ \hline Class\_output & Output & \(1\) & sigmoid & - & - & 50 & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the proposed network layers. this does not affect the effectiveness of the proposed approach that can be exploited by other researchers and fed with different set of variables. In order to handle the outliers that may affect the model performance, the Winsorizing [44] function was deployed on the training data. Additionally, the StandardScaler function was employed to normalize the data attributes, which exhibit highly variant scales. Moreover, the dataset exhibits some data distribution imbalance. Furthermore, an oversampling technique, namely, the Synthetic Minority Oversampling Technique (SMOTE) [45] was employed to address the considerable data imbalance and generates instances based on their closest neighbors from the minority class. Similarly, an under-sampling of the majority class was randomly performed. This yielded equally balanced class distributions. To build the proposed model, we split the ZATCA dataset into a 60% training set and a 20% validation set to adjust the hyper-parameters, while the remaining 20% set was dedicated for testing the model's performance.