# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 3 Artificial Intelligence
### Classification Algorithms  
1. DTs' prevalence as a classification and prediction tool [47] is attributable mainly to their accessibility. A DT comprises a root element, which receives incoming data, and leaf nodes that represent the categorization of queries and answers, respectively. Every reaction functions as a necessary condition to enable subsequent investigation within the next layer [48]. This classification tool is characterized by its simplicity and ease of use, making it highly advantageous in the context of automotive networks, for detecting misbehavior, managing roadway signals, and making routing decisions [49]. 2. SVM, also known as Large Margin Separators, is a category of learning methods that were initially designed for predicting a binary qualitative variable or discriminating [50]. These predictions were subsequently extrapolated to a quantitative variable. Discrimination of a binary variable is achieved through the identification of the optimal margin hyperplane, which properly divides or classifies records while remaining as far from every measurement as possible. This method is robust due to its capacity to process data containing a substan  
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tial quantity of attributes but a limited number of input cases. Therefore, it might be a viable method for identifying malicious actors and mitigating and identifying security breaches within the VANET environment. Additionally, it facilitates cluster optimization and spectrum allocation improvement [51]. 3. The Naive Bayes (NB) technique used for Supervised Learning for a classification approach categorizes a collection of samples by the rules that the technique generates [52]. Given the assumption that the categories within the training dataset can be distinguished, the supervised character of the program is reflected in the Naive Bayesian classifier. Naive Bayesian demonstrates suitability for addressing various VANET challenges, including driver behavior prediction, broadcast storm avoidance, and transgression awareness [53]. This is owing to its robustness against irrelevant attributes, straightforward deployment, and minimal training needs. 4. The KNN algorithm is a method of Supervised Learning. It is capable of performing both classification and regression. To generate a forecast, the KNN algorithm employs the complete dataset. In the context of predicting an observation that is not present in the information set, the technique conducts a search for the K instances within the collection that exhibit the highest degree of proximity to the desired insight [54]. Using the corresponding images of these K neighbors, the algorithm then computes the value of the predicted output of the observation. KNN is an effective architecture for location identification, privacy preservation, and stability [55]. 5. Random Forest generates results that can be generalized and are easy to train and comprehend. A Random Forest is constructed from a collection of distinct DTs [56]. This problem is viewed in a fragmented fashion by all trees due to the implementation of a twofold random selection process. Random Forest is resistant to overfitting and does not require feature selection. It requires a minimal number of input values. Due to these merits, it is an effective method for resolving congestion forecast issues, executing handovers (particularly for the transmission of multimedia records), and organizing bandwidth allotment [57; 58]. 6. Linear Classification: The application of linear classification in ML and data mining is beneficial. Although linear classifiers are inadequate for handling inseparable data, they can be satisfactory for processing data in a high-dimensional space. On document data, for instance, linear classifiers have been demonstrated to perform comparably to nonlinear classifiers. One significant benefit of linear classification is the marked improvement in the efficiency of training and testing procedures.