Search

In educational institutions, it is now more important than ever to deliver high-quality academic instruction, and educational data mining is essential for resolving problems that arise from challenging unstructured data in this field. Using machine learning (ML) approaches, the performance of students and traits related to academia, a crucial indicator of higher education, is examined. In the proposed study, the educational dataset is subjected to feature ranking algorithms, including MRMR, ReliefF, Chi-Square, ANOVA, and Kruskal–Wallis, followed by important feature selection using Shapley. The dataset has 16 attributes of integer, categorical type, and after feature ranking approaches, the features with the most important information are chosen and ML techniques are applied to them. It takes two phases to complete the work. The results are obtained after the first phase, in which all features are taken into account for ML training. The second phase of ML training takes into account selective features that are derived using ranking approaches. ML models with only selective attributes are compared to models with all features in order to determine which is more precise. In comparison, the results of the ML models with selective attributes outperformed the models with all attributes. Overall, the ensemblers, i.e., bagged tree and AdaBoost, outperformed other ML techniques such as decision trees, neural networks, naive Bayes, K-nearest neighbor, and support vector machines presented in the proposed study. Bagged trees achieved an accuracy of 81.0 percent, while AdaBoost achieved an accuracy of 74.2 percent. [ABSTRACT FROM AUTHOR]

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. [ABSTRACT FROM AUTHOR]