## 3 Materials and Methods
### Artificial Neural Networks (ANNs)  
ANNs epitomize a subset of machine learning models that derive their foundational concept from biological neural networks' structural and operational paradigm. ANNs encompass a network of interconnected artificial neurons systematically organized in layers. These layers are instrumental in processing and disseminating information via weighted connections [37]. The artificial neuron mimics a biological neuron's input, processing, and output properties. Figure 3 shows the results produced by the network. The net input  
Figure 2: Research methodology steps.  
obtained by multiplying the information entered into the network by its weights (W) is processed with the transfer function and taken from the output layer [38; 39; 40].  
Classification employing ANNs necessitates training a neural network model to categorize data into distinct classes or categories. ANNs have been extensively applied to classification tasks due to their ability to learn intricate patterns and relationships in the data. The model encompasses an input layer, a hidden layer equipped with ten neurons, a subsequent hidden layer endowed with five neurons, and an output layer having a neuron count equivalent to the number of classes within the classification task. The Rectified Linear Unit (ReLU) is utilized in the hidden layers, while the sigmoid activation function is employed in the output layer.  
Multilayer Perceptrons are used to learn if there is no linear relationship between the inputs and outputs of ANNs. Therefore, this method was used in the study.  
Model Architecture: A sequential ANN model is constructed using the Keras deep learning framework. This model encompasses an input layer, two hidden layers, and an output layer. The input layer is designed with a neuron count equivalent to the number of input features. The two concealed layers possess 10 and 5 neurons, applying the Rectified Linear Unit (ReLU) as their activation function. The output layer comprises a neuron count mirroring the number of classes in the target variable and employs the Sigmoid activation function, hence providing class probabilities as the output.  
Model Compilation: The categorical cross-entropy loss function is widely recognized as an optimal choice for multi-class classification problems due to its ability to effectively measure the discrepancy between predicted probabilities and actual class labels. The model undergoes compilation utilizing the Adam optimization algorithm and the categorical cross-entropy loss function, an optimal arrangement for addressing multi-class classification quadaries [41].  
Model Training: the model is trained on the training data for a specified number of epochs (50). The batch size (50) is also specified.  
Model Evaluation: the model's performance on the test set is evaluated, and the accuracy of the predictions is reported.  
Results Visualization: A confusion matrix and a classification report are constructed to delineate the model's performance on the test set. The confusion matrix provides a graphical representation of the model's predictive success and failure for each class, enumerating correct and incorrect predictions. In parallel, the classification report offers salient classification metrics such as precision, recall, and F1-Score for each class. These tools collectively facilitate a comprehensive understanding of the model's class-wise performance.  
Figure 3: Artificial neural network structure.  
The Sigmoid and ReLU activation functions used in the ANNs are calculated as follows:  
\[\text{Sigmoid}:\ f(a)=\frac{1}{1+e^{-a}}=\frac{e^{a}}{1+e^{a}} \tag{1}\]  
\[\text{ReLU}:\ (0,a)=0,\ if\ a<0;\ (0,a)=a,\ if\ a\geq 0 \tag{2}\]