## 3 Methodology
Simple Logistic Regression (SLR) performs outstandingly with linear data, whereas it may perform poorly with nonlinear or complex data. It also fails to address datasets with missing data [27]. Bagging is an algorithm that operates using the ensemble learning method. Each ensemble model is trained on a subset of the current dataset. Because the models work independently, it is possible to train them concurrently. Combining the decisions of the classifiers results in the classification of a new test instance by the ensemble model. The goal is to achieve better performance by using multiple classifiers [29]. JRip is one of the most used machine learning algorithms. Its operation principle involves analyzing classes as they expand and creating the initial set of rules for these classes with gradually decreasing error rates. It is ideal to use this algorithm to classify all samples in each dataset in the training data and to search for a set of rules that apply to all members of that dataset. It then proceeds to the next class and repeats the procedure until all classes have been evaluated [30]. J48 is a machine learning classifier that handles features with missing values, performs rule derivation, and manages continuous-valued feature ranges. This algorithm generates rules to establish a distinct data identity. The purpose of using this classifier is to iteratively expand the decision tree until it achieves a balance between versatility and accuracy [30]. A Multiclass Classifier is a supervised classification algorithm used when there are more than two possible outcomes in a classification task [31]. It operates based on ensemble theory. Initially, several algorithms are trained on a subset of data, and then the algorithms with the highest performance are evaluated. This classifier is considered successful in machine learning because its testing procedure involves multiple algorithms, each of which is assessed for effectiveness. ### Deep Neural Network Learning Models

Deep learning is a significant and trending topic in the artificial intelligence discipline. The development of deep learning techniques resides in exemplifying the human brain, nervous system, and its functions [32; 33]. Deep learning is an extraction process of knowledge from data utilizing Artificial Neural Networks inspired by nerve cells and multi-layered hidden architectures. Data is transferred through several layers in a deep learning algorithm, with each layer progressively extracting features and sending data to the next layer. The initial layers extract low-level features and combine them with subsequent layers to create a comprehensive representation. The conventional machine learning classification task entails preprocessing, feature extraction and selection, and classification/model setting stages. The correct feature selection is a primary factor in the high prediction performance of machine learning systems. As illustrated in Figure 3, on the other hand, deep learning models concurrently perform feature extraction, feature selection, learning, and classification procedures. Such versatility of the deep learning algorithms makes it advantageous for executing numerous tasks. Deep learning techniques using deep neural networks have gained popularity in parallel with the advancements in high-performance computer opportunities. Several newly developed techniques and numerous studies applying these methods to address various challenges have been increasing gradually. These techniques are still in use today and are broadly applicable to diverse branches of natural language processing. Due to the neural networks' capacity to learn representations with various degrees of abstraction, deep learning has been applied to natural language processing to attain cutting-edge performance in numerous tasks, including language building [34]. Convolutional Neural Networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gateway Repetitive Units, and Bidirectional Gateway Repetitive Units are all deep learning algorithms.