## 3 Materials and Methods
The SVM involves the separation of objects in an n-dimensional space using a hyperplane. The closest vectors between these categories are referred to as support vectors [54]. The initial n-dimensional spatial domain is thus transformed into a space of a higher degree of complexity, known as a Hilbert space, wherein the dot product is determined by a function designated as the 'kernel'. The SVM is equipped with a variety of kernels, encompassing linear, polynomial, Gaussian, and sigmoidal types [55]. The performance of the SVM model is dependent upon the construction of the hyperplane; if this is executed competently, the model will demonstrate high performance [42]. SVMs are characterized by their strong capacity for generalization, which renders them particularly useful when dealing with many attributes and limited data points [56]. In K-NN classification, all available data are stored and the class of a new instance is predicted based on the similarity measurements from the nearest neighbors in terms of likelihood. K-NN reduces the computational cost of checking and applying the appropriate distance function between the new observation and all k numbers of different data points stored. It then determines the label of the new data point without constructing a mapping function or internal model, in contrast to other classification algorithms [40]. The K-NN classifier is employed to allocate the query into the class that is most prevalent among its K-nearest neighbors in the training set, in accordance with the majority voting rule [54; 57]. The K-NN's primary shortcoming lies in the requirement for recalculation of the distance from all \(k\) neighbors for each new instance, which results in a significant computational expenditure [42]. The ANN can be defined as a powerful data modeling tool that simulates the cognitive learning process of the human brain system. The key strength of the ANN lies in its capacity to represent both linear and nonlinear relationships and to learn such relationships directly from the data being modeled. The structural composition of an ANN entails interconnected neuron clusters, with the fundamental configuration comprising three layers: the input layer and the output layer, which are employed for the training of the model, and the hidden layer(s), which facilitate the establishment of a connection between the input and output [58]. The structural design of the artificial neural network (ANN) model is determined by the specific characteristics of the problem and the data, and consequently,there is no single standard structure that can be universally applied to all problems [59]. The training of the network can be defined as a process of changing the weights of neurons, where the initial weights of the neurons are randomly determined [60]. The objective of a training algorithm is to identify a decision function that facilitates the modification of the network's weights. A multitude of training algorithms have been documented in the extant literature; however, identifying the most appropriate algorithm is a challenging task. The backpropagation (BP) ANN is extensively utilized in the modeling of nonlinear and complex problem types [61; 62]. The BP training algorithm employs a methodology (first-order steepest descent techniques) to adjust the weights of the neurons based on the estimation of the gradient of the sum squared error for each layer. This process is intended to increase the training speed and to avoid local minimum solutions. The error term is defined as the difference between the network response and the expected output. The rectified linear unit (ReLU), as proposed by [PERSON] and [PERSON] [63], has been shown to exhibit excellent nonlinear properties and is considered to be one of the most widely utilized activation functions in ANNs [64]. For each input element, the ReLU activation function performs a threshold operation. If the argument is negative, the value is zero; if the argument is non-negative, the value is the variable itself.