A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

Missile injury to the pediatric brain in conflict zones.

OBJECT: This study was conducted both prospectively and retrospectively at one center over a period of 8 years. The population consisted of all patients with both an age 18 years or younger and a diagnosed penetrating missile injury (PMI) during the study interval. The authors analyzed factors determining outcome and demographic trends in this population, and they compared them with those in the more developed world. METHODS: Fifty-one patients were the victims of armed conflict, although no one was directly a party to any battle. This mechanism of injury is in strong opposition to data in the literature from developed countries, in which most missile injuries are the result of suicide or homicide or are even sports related. Moreover, all previous studies on the pediatric population have considered only injuries from gunshots, but authors of the current study have included injuries from other penetrating missiles as well. RESULTS: On cross tabulation analysis using the chi-square test, the factors shown to correlate with outcome included the Glasgow Coma Scale (GCS) score, pupillary abnormalities, patient age, hemodynamic status, and bihemispheric damage. On multinomial regression analysis, the two strongest predictors of death were GCS score and pupillary abnormalities. The GCS score and hemodynamic status were the strongest predictors of disability. CONCLUSIONS: There was no difference in the prognostic factors for PMI between developing or more developed countries. Glasgow Coma Scale score, pupillary abnormalities, and hemodynamic status were the strongest predictors of outcome. In conflict zones in developing countries the victims were mostly innocent bystanders, whereas in the more developed countries homicides and suicides were the leading etiological factors.