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Brain Tumor is the abnormal growth of cancerous cells in the brain. Being able to diagnose them quickly allows practitioners to take preventive measures early, increasing survival rate. Currently, most research utilizes the popular CNN for classification. In this study we aim to find a better classifier by implementing an ensemble method, specifically using CNN and XG-Boost. The dataset we used consists of 3652 MRI images under 2 classes: tumor and no tumor. Our experiment starts off by developing a CNN model. We then used the model for feature extraction and trained an XG-Boost Classifier on the extracted features. The results show an improvement from a 97.61% to a 99.43% testing accuracy going from the CNN to the ensemble model. This demonstrates the potential of ensemble methods for enhancing standard CNN models for tasks like this. This could also open avenues for further advancements in clinical image diagnosis by utilizing the potential of deep learning in combination with ensemble learning. [ABSTRACT FROM AUTHOR]

The rising prevalence of digital transactions in contemporary financial systems has heightened the risk of credit card fraud (CCF), necessitating effective detection techniques. Machine learning (ML) technologies show promise, but dataset imbalance impedes fraud detection accuracy. This study investigates the effectiveness of ensemble learning techniques and data balancing methods for detecting CCF in imbalanced datasets. We compare four ML algorithms—Logistic Regression, Decision Tree, Random Forest, and XGBoost—and evaluate data balancing techniques including SMOTE, ADASYN, Random Oversampling, CNN, NCR, and Random Undersampling, along with hybrid methods like SMOTE with NCR and ADASYN with NCR. Our experiments use a dataset of credit card transactions made by European cardholders, which includes 30 attributes and exhibits severe class imbalance, with fraud events accounting for only 0.172% of total transactions. Applying balancing techniques led to significant improvements: Random Oversampling with XGBoost achieved the highest F1-Score of 92.43%, with Precision of 97.383% and Recall of 88.418%. Random Forest with Random Oversampling followed with an F1-Score of 91.554%, and NCR with Random Forest achieved an F1-Score of 91.508%. These findings highlight the effectiveness of oversampling techniques in enhancing model performance for imbalanced datasets. Precision, Recall, and F1-Score were emphasized, while specificity and sensitivity were not directly used; macro averages ensured balanced evaluation across all classes. This study concludes that combining Random Forest or XGBoost classifiers with oversampling techniques like Random Oversampling or undersampling methods such as NCR provides accurate and unbiased results for detecting CCF in imbalanced datasets, contributing to reliable fraud detection systems for real-world applications. [ABSTRACT FROM AUTHOR]