Modern universities must strategically analyze and manage student performance, utilizing knowledge discovery and data mining to extract valuable insights and enhance efficiency. Educational Data Mining (EDM) is a theory-oriented approach in academic settings that integrates computational methods to improve academic performance and faculty management. Machine learning algorithms are essential for knowledge discovery, enabling accurate performance prediction and early student identification, with classification being a widely applied method in predicting student performance based on various traits. Utilizing the Naive Bayes classifier (NBC) model, this research predicts student performance by harnessing the robust capabilities inherent in this classification tool. To bolster both efficiency and accuracy, the model integrates two optimization algorithms, namely Jellyfish Search Optimizer (JSO) and Artificial Rabbits Optimization (ARO). This underscores the research's commitment to employing cutting-edge machine learning and algorithms inspired by nature to achieve heightened precision in predicting student performance through the refinement of decision-making and prediction quality. To classify and predict G1 and G3 grades and evaluate students' performance in this study, a comprehensive analysis of the information pertaining to 395 students has been conducted. The results indicate that in predicting G1, the NBAR model, with an F1_Score of 0.882, performed almost 1.03% better than the NBJS model, which had an F1_Score of 0.873. In G3 prediction, the NBAR model outperformed the NBJS model with F1_Score values of 0.893 and 0.884, respectively.

The expansion of Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau) has triggered native forest retreat and a range of ecological issues, especially for the Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) forests with similar growing conditions. In order to reveal the stoichiometric characteristics of Moso bamboo succession and scientifically control the forest retreat caused by the expansion of Moso bamboo into Chinese fir, mixed forests including 0%, 30%, 50%, 60%, and 80% of Moso bamboo expanded into Chinese fir forests were used to simulate the expansion stages I, II, III, IV, and V, respectively. In addition, by measuring the C, N, and P contents in Moso bamboo leaves and soils and calculating the correlation stoichiometric ratios, the correlation and coupling of which were explored and combined with an ecological homeostasis model at different stages of Moso bamboo expansion. The results demonstrated that P was a key element for the high utilization of Moso bamboo growth, and the expansion principle was influenced by N limitation. The conclusion was that the anthropogenic regulation of C content in soil could achieve the purpose of expansion control and exploit the carbon sequestration capacity in the mixed forest with half Moso bamboo and half Chinese fir, which should discourage the expansion.