Intelligent Ensemble Learning Framework for Prediction of Students Academic Performance Using Extreme Gradient Boosting and Random Forest Algorithms (Published)
A key component of educational data mining (EDM) and learning analytics is the prediction of students’ academic achievement. Institutions can increase overall learning results, identify at-risk students, and carry out focused interventions by utilizing machine learning approaches. The Intelligent Ensemble Learning Framework presented in this paper combines Extreme Gradient Boosting (XGBoost) and Random Forest (RF) to increase prediction accuracy. XGBoost a powerful boosting strategy noted for its effectiveness in managing huge datasets and minimizing overfitting, combines multiple decision trees to reduce variation and improve model stability. The study uses information gathered from Federal Polytechnic Ukana, including attendance, demographics, and academic records of 400 students, among other pertinent characteristics. 16 important features were found based on eigenvalues and explained variance following data preprocessing, which included normalization and feature selection using Principal Component Analysis (PCA). The dataset was divided into subsets for testing (20%) and training (80%), and a bagging technique was used to create the ensemble model. Experimental results demonstrate that the ensemble model outperforms individual RF and XGBoost models in predicting students’ cumulative grade point average (CGPA). The performance evaluation, based on standard regression metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-Squared Score (R2), Explained Variance Score (EVS), and Median Absolute Error (MedAE), indicates superior predictive accuracy. The ensemble model achieved an R2 score of 0.9900, outperforming RF (0.9888) and XGBoost (0.9800). Visualizations using scatter plots, grouped bar charts, and heat maps further validate the effectiveness of the proposed approach. This research contributes to the growing body of work in machine learning applications in education, demonstrating the potential of ensemble regression models in academic performance prediction. The findings underscore the importance of advanced predictive models in educational institutions, facilitating proactive decision-making and student support strategies to enhance academic success.
Keywords: : Academic Performance, Ensemble learning, Framework, Intelligence, Prediction, extreme gradient boosting and random forest
A Comprehensive Framework for Strengthening USA Financial Cybersecurity: Integrating Machine Learning and AI in Fraud Detection Systems (Published)
Financial cybersecurity is of paramount importance in today’s digital age, particularly in the United States, where the financial sector plays a crucial role in the global economy. With the increasing frequency and sophistication of cyber threats, traditional fraud detection systems are facing significant challenges in keeping pace with evolving risks. This paper presents a comprehensive framework for strengthening US financial cybersecurity by integrating machine learning (ML) and artificial intelligence (AI) techniques into fraud detection systems. The framework begins with an exploration of the fundamental concepts of financial cybersecurity, highlighting key threats and regulatory considerations. It then delves into the fundamentals of ML and AI, discussing their applications in fraud detection and the associated benefits and limitations. The design of the framework encompasses data collection, preprocessing, feature engineering, model selection, and integration with existing systems, emphasizing scalability and adaptability. Through case studies and best practices, the paper illustrates successful implementations of ML/AI in financial cybersecurity and draws lessons from real-world applications. Ethical and privacy considerations are addressed, emphasizing the importance of ethical guidelines, privacy protection, and regulatory compliance. Looking to the future, the paper discusses emerging trends in cyber threats and advancements in ML/AI technologies, while also acknowledging anticipated challenges. In conclusion, the framework outlined in this paper offers a holistic approach to enhancing US financial cybersecurity, emphasizing the critical role of ML and AI in mitigating cyber risks and safeguarding financial institutions and their customers. Recommendations for future research and implementation efforts are provided to further strengthen the resilience of financial systems against evolving cyber threats.
Keywords: AI, Framework, US financial cybersecurity, fraud detection systems., integrating machine learning, strengthening