Analysis of Teaching-Learning Efficiency Using Attribute Based Double Bounded Rough Neutrosophic Set Driven Random Forests

Abstract

Face-on-Face interaction constitutes an integral part of the classroom atmosphere as it provides teachers with an opportunity to understand their students intimately. Hence, this study deals with attribute based double bounded rough neutrosophic set driven random forests using Gini Impurity based split to arrive at a decision regarding the teaching-learning efficiency. A mathematical model is constructed using double bounded rough neutrosophic set which is utilised to evaluate the expression of the students with the help of a real-time data by capturing the images of the students against different subjects. The decisions made are then used to fit a random forest model to establish inferences regarding the teaching-learning efficiency for different subjects. The constructed model is then validated using newly added test objects.

Keywords

• image processing
• neutrosophic image processing
• image segmentation
• rough sets
• neutrosophic sets
• fuzzy sets
• face expressions
• student expression detection
• facial key points