The rapid development of technology has digitized customer payment behavior towards a cashless society. To a certain extent, this has created a feast for miscreants to commit fraud. According to Nilson (2020), global fraud loss is projected to reach over $35 billion by 2025. Consequently, the need for a novel method to prevent this menace is undisputed. This research was conducted on the IEEE-CIS Fraud Detection Dataset provided by Vesta Corporation. Based on the logic of labeling for converting the entire account to ''Fraud=1'' once the credit card has fraud, we navigate the research process towards predicting fraudulent credit cards rather than fraudulent transactions. The key idea behind the proposed model is user separation, in which we divide users into old and new people before applying CatBoost and Deep Neural Network to each category, respectively. In addition, a variety of techniques to improve detection accuracy, namely handling heavily imbalanced datasets, feature transformation, and feature engineering, are also presented in detail in this paper. The experimental results showed that our model performed well, as we obtained AUC scores of 0.97 (CatBoost) and 0.84 (Deep Neural Network).
Social media refers to apps or websites that allow its users to connect and exchange data via the Internet. The rise of social media platforms has created great opportunities for open science, including exchanging scientific knowledge and communicating in more interactive ways between researchers and with the general population. Therefore, scientists are increasingly using multiple platforms of social media for communication in science. Through a content analysis of related articles published in academic journals, this paper would offer an overview of how scientists have been using various social media platforms for science communication purpose. A review of existing literature revealed that over the past decade, several social media platforms have been used to disseminate the research results outside of academia and increase public engagement. Besides, scientists also use social media to create scholarly connections, share and discuss their research findings and ideas with the scientific community across the globe, as well as to stay update with changes in science communication. Facebook, Twitter, ResearchGate, Academia.edu, and blogs are the most commonly used platforms by scientists. The current paper can help better understand how scientists are currently using social media as a medium for communication inside and outside the scientific world.
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