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This study proposes an innovative solution for credit card fraud detection, utilizing a stacking ensemble of machine learning classifiers enhanced with sophisticated data resampling techniques. The model demonstrates exceptional performance, achieving an F1-score of 0.92, precision of 0.95, recall of 0.88, an AUPRC of 0.96, and a perfect ROC-AUC of 1.00. These results significantly outperform standalone models like XGBoost and Decision Tree, showcasing the strength of the proposed approach. The study addresses two critical challenges, class imbalance and overfitting, by employing K-means SMOTEENN to balance minority class representation while reducing synthetic data noise, thus lowering the risk of overfitting. Additionally, the stacking ensemble’s integration of diverse classifiers produces a generalized decision boundary, enhancing model robustness in real-world scenarios. This ensures a precise fraud detection mechanism without compromising sensitivity. Furthermore, the proposed model incorporates Explainable AI (XAI) techniques to enhance interpretability and trust. The study identifies key features driving model predictions by leveraging Local Interpretable Model-Agnostic Explanations (LIME), illustrating how base learners and the meta-learner contribute to the final decision. This transparency bolsters stakeholder confidence and provides actionable insights for financial institutions. Overall, the proposed approach marks a notable step in protecting financial transactions from fraud.

This qualitative descriptive study provides a comprehensive profile of scientific academic writing skills among university students enrolled in mathematics education and primary school teacher education programs. Using a self-assessment survey, 110 students evaluated their proficiency across various dimensions of scientific writing, including clarity and coherence, organization of scientific arguments, use of scientific terminology, citation and referencing, critical evaluation of literature, and independent thinking. The data were analyzed using descriptive statistics to identify trends, strengths, and areas for improvement in students' writing skills. The findings revealed that students generally feel confident in their ability to construct clear academic paragraphs and use appropriate vocabulary and tone, but struggle with higher-order writing tasks such as synthesizing information from multiple sources and organizing extended scientific texts. Many students also expressed difficulties in applying proper citation and referencing styles, particularly APA format. Key strengths identified in the study include competence in basic grammar, punctuation, and paragraph structure, while notable weaknesses were found in areas such as critical evaluation, organizing complex arguments, and correct application of citation conventions. These results suggest a need for targeted instructional interventions to help students develop the advanced scientific writing skills necessary for academic success. The study's findings contribute to the ongoing discourse on improving scientific academic writing pedagogy, offering insights for educators to better support students in mastering these critical skills. [ABSTRACT FROM AUTHOR]