RESUMO
Credit card fraud is a growing problem nowadays and it has escalated during COVID-19 due to the authorities in many countries requiring people to use cashless transactions. Every year, billions of Euros are lost due to credit card fraud transactions, therefore, fraud detection systems are essential for financial institutions. As the classes' distribution is not equally represented in the credit card dataset, the machine learning trains the model according to the majority class which leads to inaccurate fraud predictions. For that, in this research, we mainly focus on processing unbalanced data by using an under-sampling technique to get more accurate and better results with different machine learning algorithms. We propose a framework that is based on clustering the dataset using fuzzy C-means and selecting similar fraud and normal instances that have the same features, which guarantees the integrity between the data features.
RESUMO
INTRODUCTION: Glycogen Rich Clear Cell Carcinoma (GRCC) is a rare variant of breast carcinomas and believed to be linked with a poor prognosis. CASE SUMMARY: We are presenting a 60-year-old Saudi lady with a 2cm GRCC carcinoma associated with clear cell ductal carcinoma in situ (DCIS) and no axillary lymph node involvement. The tumor was Estrogen and Progesterone receptors (ER & PR) positive and HER 2-neu negative. She underwent mastectomy with sentinel lymph node biopsy followed by hormonal therapy. She is alive and free of disease for 35 months. CONCLUSION: The prognosis of GRCC may not be different from other types of invasive breast cancer.