The role of diversity and ensemble learning in credit card fraud detection.

The number of daily credit card transactions is inexorably growing: the e-commerce market expansion and the recent constraints for the Covid-19 pandemic have significantly increased the use of electronic payments. The ability to precisely detect fraudulent transactions is increasingly important, and machine learning models are now a key component of the detection process. Standard machine learning techniques are widely employed, but inadequate for the evolving nature of customers behavior entailing continuous changes in the underlying data distribution.

Sex and cultural differences in the acceptance of functional foods: a comparison of American, Canadian, and French college students.

Objective: Functional foods (FF)--foods containing nutritional supplements in addition to natural nutrients--have an increasing presence in the marketplace. Expanding on previous research, the authors investigated college students' acceptance of FF.

Participants: In September--March 2004, 811 undergraduates in Canada, the United States, and France participated in the study.