Abstract Random forest is a powerful ensemble learning technique celebrated for its heightened predictive performance and robustness in handling complex datasets; nevertheless, it is criticized for its computational expense, particularly with a large number of trees in the ensemble. Moreover, the model’s interpretability diminishes as the ensemble’s complexity increases, presenting challenges in understanding the decision-making process. Although various pruning techniques have been proposed by researchers to tackle these issues, achieving a consensus on the optimal strategy across diverse datasets remains elusive. In response to these challenges, this paper introduces an innovative machine learning algorithm that integrates random forest with Naïve Bayes to predict student performance. The proposed method employs the Naïve Bayes formula to evaluate random forest branches, classifying data by prioritizing branches based on importance and assigning each example to a single branch for classification. The algorithm is utilized on two sets of student data and is evaluated against seven alternative machine-learning algorithms. The results confirm its strong performance, characterized by a minimal number of branches.

This research paper addresses the challenge of detecting communities in sparse social graphs and presents a novel approach that leverages node importance and label propagation. The proposed method consists of three phases: initialization, label assignment, and filtering. In the initialization phase, we carefully identify and designate key nodes using their local information and associate them with different labels. Subsequently, in the label assignment phase, the assigned labels are propagated to neighboring nodes, which are organized in a multilevel manner, taking into account their relevance and significance. Through the filtering phase, we effectively eliminate irrelevant labels, enhancing the accuracy of community assignments and resulting in an optimized community structure. To assess the effectiveness of our approach, we conducted experiments on both real-world networks and synthetic networks. A comparative analysis was performed against several established community detection techniques from existing literature. The results clearly demonstrate that our proposed algorithm surpasses existing methods in terms of accuracy and efficiency.

Community detection in social networks is an area, which has witnessed many studies in recent years, and therefore several algorithms have been proposed. The majority of these methods are based on the relationships among users (structural information) to identify communities in social networks. However, these methods take into account only the strength of connections among users, but they ignore the content information such as the topics shared by users. In this paper, we propose a method of community detection in social networks that combines the content and the structural information. To meet this end, first, we propose a new approach to detect the topics involved in social networks by exploit the statistical and semantic measures. Second, we divide users into different groups according to their topics of interest, and then we perform a static community detection algorithm to detect communities in each group of users. The experimental results on real life datasets have shown that our method finding extracts more meaningful communities in social networks, and improves the quality of communities from the perspective of topics and links.