This study proposes the novel machine learning concepts to enhance both prediction accuracy of diabetes detection and interpretation of diagnostic models. First, the methodology uses multiple imputations by chained equations (MICE) to complete data before analysis through missing data imputation procedures. The class imbalance problem is solved through the implementation of Synthetic Minority Over-sampling Technique (SMOTE). The Interquartile Range (IQR) outlier detection method helps remove outliers because it enhances model robustness. The hybrid RFE-WWO selection process combines Recursive Feature Elimination (RFE) with Water Wave optimization (WWO) to select important features that strike the right balance between model complexity and prediction accuracy. The HFM framework contains the Hybrid Fusion Model as its essential component, which merges AdaBoost's and CatBoost's most favorable aspects. The hyperparameter optimization with TPE leads to model tuning which reaches a prediction accuracy of 97.84% through the application of Tree-Structured Parzen Estimator. The entire approach delivers enhanced accuracy and it improves precision along with recall metrics and F1 score performance of the predictive model. The framework shows significant potential for early diagnosis by merging these advanced techniques since ensemble methods are essential for healthcare data analysis while accurate interpretable models are vital to create dependable diagnostic tools.

Coronavirus disease 2019 (COVID-19) has led many colleges and students to use online learning. In educational databases with so much data, evaluating student development is difficult. E-learning is essential for egalitarian education since it uses technology and contemporary learning techniques. This review research found three ways for predicting online course performance: i) To choose the best features to raise student performance; ii) The most effective algorithms for transforming unbalanced data into balanced data; and iii) The best machine learning algorithms to predict online course performance. This study also offered insights into using hybrid techniques and optimization algorithms to educational data sets to improve student performance prediction. The utilization of data from independent e-learning products to enhance education today requires data processing to ensure quality. In addition to these techniques, our abstract highlights the effectiveness of hybrid feature selection methods like L2 regularization (Ridge) and recursive feature elimination (RFE) and ensemble learning models like random forest, gradient boosting, and AdaBoost. These approaches considerably improve prediction accuracy and tackle huge and sophisticated educational dataset challenges. Our work uses advanced machine-learning approaches to optimize e-learning settings and boost academic achievements in the shifting online education landscape caused by the COVID-19 pandemic.

The rising demand for e-learning platforms requires reliable predictive models to improve learning results. This study establishes ElimRidge-Hybrid Fusion Model (HFM) as a new predictive framework for e-learning data sets that solves the problems of feature selection, model overfitting, and potential dataset complexity. Thus, the objective of the study is as follows: Enhance the predictive precision and interpretability of the model by applying feature selection techniques using Ridge Regularization (L2) in conjugation with Recursive Feature Elimination (RFE) and using a Hybrid Fusion Model (HFM), which is the combination of AdaBoost and CatBoost. For the hyperparameter tuning, Gaussian Process-Enhanced Random Search (GP-RS) is used to explore the hyperparameters of the space efficiently with the least number of random trials. For data clearing this framework uses Synthetic Minority Over-sampling Technique (SMOTE) for class balancing, Interquartile Range (IQR) for outlier removal and, Z-score normalization for normalization. Experimental results demonstrate that ElimRidge-HFM significantly improves predictive performance, achieving 97% accuracy, 97% precision, and 97% F1-score. Statistical validation (p < 0.05) confirms its superiority over traditional models, effectively handling imbalanced and noisy e-learning datasets. These findings highlight its potential for enhancing personalized learning strategies and optimizing student engagement. These results further strengthen the claims that ElimRidge-HFM has great ability to handle imbalanced and noisy data sets due to its scalability. This research contributes to the development of e-learning techniques by providing opportunities to introduce decisions and/or strategies when the students’ performance is low or during cases of inequality in students’ learning opportunities.

In the rapidly evolving cybersecurity landscape, this study specifically addresses the challenge of accurately detecting rare and evolving network attacks—particularly infrequent types such as Root-to-Local (R2L) and User-to- Root (U2R) attacks—in highly imbalanced datasets. This study aims to develop an advanced TripleBoost ensemble model that integrates AdaBoost, CatBoost, and XGBoost to overcome the limitations of conventional IDS in dynamic network environments, Intrusion Detection Systems (IDS) are essential for identifying and mitigating malicious activities within network environments. This study presents a novel IDS framework designed to address critical challenges in the field, including handling class imbalances, outlier detection, and feature selection inefficiencies. A comprehensive preprocessing pipeline is employed, utilizing the Synthetic Minority Over-Sampling Technique (SMOTE) to manage class imbalances, the Z-score method for outlier detection, and ridge regression for effective feature selection. The core innovation lies in the development of a TripleBoost ensemble model, which integrates AdaBoost, CatBoost, and XGBoost to leverage their complementary strengths. This approach achieves a significant performance boost, evidenced by an accuracy of 97.38%, precision of 95.34%, recall of 99.56%, and an F1-score of 96.40%. The model successfully overcomes limitations faced by traditional IDS models, such as poor detection of rare attack types and scalability issues in dynamic network environments. This framework significantly enhances IDS technology by improving both detection accuracy and generalization capabilities, making it more effective against evolving cyber threats. Future work will explore real-time detection optimizations and the adaptability of the model in complex network paradigms, further enhancing its potential to secure modern network infrastructures.

E-Learning platforms change fast, and real-time behavioural analytics with machine learning provides the most powerful means to enhance learner outcomes. The datasets undergo preprocessing techniques like Z-score outlier detection, Min-Max scaling for feature normalization, and Ridge-RFE (Ridge regression and Recursive Feature Elimination) for feature selection in order to improve the accuracy and reliability of the predictions. Applying the Gradient Boosting Machine, classification accuracy up to a 94% level with respect to the model about predictions on learner outcomes was achievable. Thus, applying this, feedback systems may offer timely recommendations or directions in class that propel students toward better understanding on how to raise participation and success percentages. However, this approach has some potential benefits but there are still various challenges such as managing the data imbalance for models that generalize in a dynamic environment. Though hybrid methods mitigate this problem, real-time data pipelines with behaviour analytics incorporation call for significant computer-intensive resources and infrastructure. This integration has very high paybacks. It makes possible more responsive E-Learning platforms with individual needs almost met in real-time manners, thus giving instantaneous feedback, content suggestions, and timely interventions. Finally, convergence of real-time analytics with ML models culminates in adaptive learning environments which improve student engagement, retention, and quality of academic results.