The integration of technology into educational institutions has led to the generation of vast data, creating opportunities for Educational Data Mining (EDM) to improve learning outcomes. This study introduces a novel feature selection model, “Dynamic Feature Ensemble Evolution for Enhanced Feature Selection” (DE-FS), which combines traditional methods such as correlation matrix analysis, information gain, and Chi-square with heat maps to select the most relevant features for predicting student performance. The core innovation of DE-FS lies in its dynamic and adaptive thresholding mechanism, which adjusts thresholds based on evolving data patterns, addressing the limitations of static methods and mitigating issues like overfitting and underfitting. This research makes three key contributions: it introduces an advanced ensemble-based feature selection methodology, incorporates dynamic and adaptive thresholding to improve accuracy and flexibility, and demonstrates DE-FS’s superior predictive performance across diverse educational datasets. The results highlight DE-FS’s ability to adapt to fluctuating data patterns, enabling precise and reliable student performance predictions, supporting targeted interventions, and improving resource allocation to enhance personalized learning experiences.

The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios. In response to these challenges, we propose the "hybrid raven roosting intelligence framework" (HRIF) algorithm. HRIF draws inspiration from the dynamic behaviors of roosting ravens and computational intelligence. What distinguishes HRIF is its effective capacity to adeptly navigate the clustering landscape, evading local optima and converging toward optimal solutions. An essential enhancement in HRIF is the incorporation of the Gaussian mutation operator, which adds stochasticity to improve exploration and mitigate the risk of local minima. This research presents the development and evaluation of HRIF, showcasing its unique fusion of nature-inspired optimization techniques and computational intelligence. Extensive experiments with diverse benchmark datasets demonstrate HRIF's competitive performance, particularly its capability to handle complex data and avoid local minima, resulting in accurate clustering outcomes. HRIF's adaptability to challenging datasets and its potential to enhance clustering efficiency and solution quality position it as a promising solution in the world of data exploration.

Population-based metaheuristic optimization algorithms have gained prominence for tackling complex optimization problems. They balance exploration and exploitation, essential for finding optimal solutions. While algorithms like Genetic Algorithms, Particle Swarm Optimization, and Gravitational Search Algorithm have shown success, they have limitations, such as premature convergence and sensitivity to parameters. To address these issues, we have introduced Quantum-Inspired Gravitationally Guided Particle Swarm Optimization (QIGPSO) for addressing complex optimization challenges, particularly in the context of medical data analysis for diagnosing Non-Communicable Diseases (NCDs). The Quantum Particle Swarm Optimization (QPSO) and Gravitational Search Algorithm (GSA) are both used in QIGPSO. It takes advantage of each algorithm’s strengths in both global and local search processes. We used an absolute Gaussian random variable to improve the search, changed the position update equations and used a wrapper-based method with Support Vector Machine (SVM) for feature selection and classification. The findings suggest that QIGPSO is effective at identifying key features, achieving high accuracy rates, and lowering the number of incorrect classifications across several NCD datasets. Doctors can use QIGPSO data to make better treatment decisions for their patients. QIGPSO overcomes the limitations of conventional optimization methods by faster convergence while improving exploitation balance.

In our data-driven world, the healthcare sector faces significant challenges in the early detection and management of Non-Communicable Diseases (NCDs). The COVID-19 pandemic has further emphasized the need for effective tools to predict and treat NCDs, especially in individuals at risk. This research addresses these pressing concerns by proposing a comprehensive framework that combines advanced data mining techniques, feature selection, and meta-heuristic optimization. The proposed framework introduces novel hybrid algorithms, including the Hierarchical Genetic Multiple Reduct Selection Algorithm (H-GMRA) and the Customized Function-based Particle Swarm Optimization with Rough Set Theory for NCD Feature Selection (CPSO-RST-NFS). These algorithms aim to address the challenges of feature selection, computational complexity, and disease classification accuracy. H-GMRA outperforms traditional methods by identifying minimal feature sets with high dependency ratios. CPSO-RST-NFS combines meta-heuristic optimization with feature selection, resulting in improved efficiency and accuracy. Through extensive experimentation on diverse NCD datasets, this research demonstrates the framework’s ability to select informative features, improve classification accuracy, and contribute to better patient outcomes. By bridging the gap between computational efficiency and disease classification accuracy, this work offers valuable insights for healthcare practitioners and data analysts, ultimately advancing the field of NCD research. The proposed framework presents a significant step towards enhancing the early detection and management of NCDs, offering hope for more precise clinical predictions and improved patient care.

Effective student performance evaluation is essential for improving education, especially in higher and technical schools. Data mining helps solve educational and administrative problems. School performance prediction is a key field of Educational Data Mining (EDM), however manual computation and data mining methods struggle with the expanding volume of complicated data from varied sources, leaving research gaps and unresolved challenges. An integrated, multi-phase strategy to these issues is presented in this work. This study uses the Hybrid Probabilistic Ensemble Fuzzy C-Medoids with Feature Selection (HPEFCM-FSP) algorithm to cluster students by academic performance in Phase I to identify those who need extra help. The NeuroEvoClass algorithm mixes evolutionary strategies inspired by swarm intelligence and artificial neural networks (ANN) to improve student performance prediction in Phase II. Particle Swarm Optimization (PSO) optimizes neural network weight assignments, dynamically fine-tuning network topologies depending on the complex student dataset. The algorithm improves prediction power through progressive convergence. The proposed methods outperform traditional models in accuracy, precision, recall, and F1-score, according to this study. Since NeuroEvoClass reliably identifies pupils at risk of academic underperformance, it is promising for Early Warning Systems (EWS) in educational institutions. The study’s multi-phase approach helps educators and policymakers make data-driven decisions about student academic achievement. HPEFCM-FSP consistently outperforms K-means and Fuzzy C-means in clustering educational data by getting higher Silhouette Scores and Dunn Index values on benchmark datasets. This algorithm’s strong feature selection and clustering help target educational interventions by revealing student learning behaviors. By identifying well-separated groups of high-achieving, above-average, and struggling students, HPEFCM-FSP helps institutions personalize support and interventions. Educational administrators, teachers, and policymakers can use the algorithm to handle huge, heterogeneous educational datasets due to its efficiency and robustness.