A hybrid super ensemble learning model for the early-stage prediction of diabetes risk.

Diabetes mellitus has become a rapidly growing chronic health problem worldwide. There has been a noticeable increase in diabetes cases in the last two decades. Recent advances in ensemble machine learning methods play an important role in the early detection of diabetes mellitus.

A novel color labeled student modeling approach using e-learning activities for data mining.

Student modeling approaches are important to identify students' needs, learning styles, and to monitor their improvements for individual modules. Lecturers may incorrectly identify the students' needs and learning styles based on solely an exam grade or performance in the class. In doing so, students need to be classified using more parameters such as e-learning activities, attendance to virtual live class (for theory and practice) and submission time of the assignment, etc.

Correlation value determined to increase Salmonella prediction success of deep neural network for agricultural waters.

The use of computer-based tools has been becoming popular in the field of produce safety. Various algorithms have been applied to predict the population and presence of indicator microorganisms and pathogens in agricultural water sources. The purpose of this study is to improve the Salmonella prediction success of deep feed-forward neural network (DFNN) in agricultural surface waters with a determined correlation value based on selected features.