## 2 Materials and Methods
### Classification Learning Algorithms
Hence, let's first update the formulas for how each data point is represented in space, and then consider the equation of a line that will aid in dividing comparable categories, and lastly, consider the formula for the distance between a data point and a line (a boundary separating the categories). SVM, one of the finest classifiers, displays some linearity. It can handle non-linear situations using a non-linear basis function and is backed by solid mathematical intuition [22]. In categories of n-dimensional space, the SVM rule aims to establish a single decision boundary or line. This will enable us to swiftly assign novel data to the appropriate class over the long run [23]. One supervised learning method that is widely used to categorize data depending on how its neighbors are ranked is the K-Nearest Neighbor algorithm. It keeps track of all the available instances and classifies new cases based on a similarity score. The number of nearest neighbors to be considered when choosing the winner by majority vote is indicated by the KNN parameter. Because it is a \"lazy learner\", we can apply it when the dataset is labeled, noise-free, and small. Finding the closest point from the given data set to the input point is the definition of \"finding nearest neighbors\". Using the majority votes of its K neighbors, the algorithm classifies new cases by storing all existing cases (test data). Data points are first converted into their mathematical values before employing KNN (vectors). The algorithm operates by measuring the separation between these points' mathematical values. The distance between each data point and the test data is calculated, and the likelihood that the facts are similar to the test data is estimated. For classification, the most likely set of points is utilized. The distance functions that can be used are the Hamming, Minkowski, and Euclidean distances. The KNN approach only saves the dataset during the preparation phase, and when it receives new data, it organizes that data into a collection that is abundant and equal to the incoming information [24]. A deep neural network has many layers and neurons in each layer. When the output of one layer serves as the input for another layer in the forward direction, all layers in a deep neural network will alternately turn on and off. In a deep network, there are three types of data flow: input, output, and sequential [25; 26]. The difference between an ANN and a DNN is that the former only comprises one input layer, one output layer, and perhaps one hidden layer. However, the latter ought to have more hidden layers [2; 27]. The only neural networks we utilize are feed-forward ones. Three hidden layers have been used in the proposed work to extract a high-quality feature from the dataset. The Keras model was used to implement the DNN model. A grid of potential discrete Hyperparameter values has been constructed to fit the model. We keep track of the model output for each set and then choose the combination that has delivered the best outcome. The DNN models have recently become quite popular because of their exceptional capacity to learn the underlying structure of the input data vectors and the non-linear input-output mapping [25; 28; 29].