## 3 Proposed Approach
```
for\(i\) in reversed network:  Layer \(\leftarrow\)network\({}_{i}\) if\(i\) is last layer then:  for\(j\) in Layer: \(\textit{Error}_{C}\leftarrow\textit{Binary cross entropy}\) \(\textit{Error}_{R}\leftarrow\textit{MSE}\) \(\textit{Loss}\leftarrow\beta(\textit{Error}_{C})+(1-\beta)(\textit{Error}_{R})\) update the network weights
```  
**Algorithm 1**Backpropagation  
For the testing phase, an unseen a Zakat declarations dataset is conveyed to the trained model to classify cases into \"under-valuation\" or \"actual declaration\", and predict each declaration revenue at risk using the regression layer. Further, one can note that the designed architecture was fine-tuned empirically. In other words, the final network architecture as well as the hyper-parameter settings were  
Figure 1: The proposed simultaneous classification and regression based approach. optimized through comprehensive experiments. In particular, the appropriate number of layers along with the number of neurons per layer were determined empirically. Additionally, the network hyper-parameters, such as the batch size, optimizer, the number of epochs and the learning rate, were investigated during the training phase. Accordingly, the architecture proposed in Figure 1 encloses an input layer with five hidden layers. The first four hidden layers are, respectively, followed by dropout layers. In fact, the latter layers represent a regularization technique that prevents the proposed neural networks from overfitting. Specifically, these dropout layers are intended to modify the network itself to avoid overfitting. On the other hand, the last hidden layer is followed by two output layers. The first one is meant to perform the regression task while the second layer is dedicated for the classification purpose. Figure 2 illustrates the structure of the proposed architecture. Accordingly, Table 1 details each layer of the proposed architecture. As it can be seen, the input layer is fed with 94-dimentional data and conveys them to the next hidden layer  
Figure 2: The proposed Model Architecture. which is also composed of 94 neurons. Then, a dropout layer is placed to randomly omit 10% of the neurons in order to increase the resulting model generalization capability and avoid overfitting. On the other hand, the next hidden layer encloses 1000 neurons, followed by a 10% dropout layer, while the third hidden layer encloses 1000-dimensional features and yields 500-dimensional features through 500 neurons prior to a dropout layer. Similarly, the 250-neuron fourth hidden layer is coupled with the last 10% dropout layer, and yields 250-dimensional features to be processed by 50 neurons. Note that a l2 regularizer was associated with a ReLU activation function for all hidden layers. Further, the prediction is synchronously performed through the considered output layers. In particular, as illustrated in Figure 2, a \"reg_output\" activation function is dedicated for the revenue at risk prediction, while a \"class_output\" layer that consists of a sigmoid function is intended to classify Zakat declarations into the \"under-reporting\" and \"actual declaration\" categories.