## 2 Materials and Methods
### Artificial Neural Network Methods  
Artificial neural networks (ANNs) are computational models derived from the architecture and operation of the neural networks in the human brain. These models simplify biological neural processes to create interconnected systems capable of various tasks [31]. ANNs, often referred to as neural networks or direct neural networks in academic and engineering contexts, consist of numerous nodes (neurons) that work together to process information [32].  
Each node in the network performs a specific output function, commonly known as the activation function. The connections between nodes are associated with weights, which determine the signal strength transmitted through these links. These weights are akin to the memory of the neural network and play a crucial role in its learning process [33]. The overall output of the network depends on the structure of its connections, the assigned weight values, and the chosen activation functions. ANNs can approximate complex algorithms or functions, often representing logical or strategic operations [34].  
The architecture of artificial neural networks (ANNs) is typically organized into three main layers: the input layer, hidden layer(s), and output layer. The input layer serves as the interface between the network and the external environment, receiving signals and data for processing [35]. The hidden layer(s), situated between the input and output layers, handles the intermediate processing of the received data. These layers are not directly observable from the outside but play a crucial role in extracting features and learning patterns [36]. Finally, the output layer generates the network's results, delivering the processed information to the user or an external system. By modifying the connectivity between nodes, adjusting the weights, and selecting appropriate activation functions, ANNs can be tailored to perform a wide range of tasks, from basic computations to tackling complex, abstract problems.  
This study focuses on developing a model based on artificial neural networks (ANNs). The first stage of the model starts with a basic ANN architecture. The building blocks of this basic model are as follows:  
* Input Layer: The layer where the data will be introduced to the model.
* Five Hidden Layers: Layers that allow the model to learn more complex relationships. These layers contain the following components:
* Linear Transformation: Processing the data in a linear manner.
* Normalization: Normalization is conducted to process the data in a standard manner.
ReLU Activation: The ReLU (rectified linear unit) activation function is a function that sets negative inputs equal to zero and leaves positive inputs as they are: \[f(x)=max(0,x)\] (4) It is frequently used in deep learning models thanks to its simplicity and linear piecewise structure. - Dropout Layer: Some neurons are randomly turned off to prevent the model from overfitting. * Output Layer: This is the last layer where the model produces the results, and here, the LogSoftmax activation function is used. This function normalizes the probabilities of each class by taking the logarithm of the outputs. Formula: \[\text{LogSoftmax}(z)_{i}=\log\left(\frac{e^{z_{i}}}{\sum_{j=1}^{K}e^{z_{j}}}\right)\] (5) Here: * Vector z is the output, which is the classification score of the model; * i represents each element of these scores; * K represents the total number of classes.