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

Authors

  • Praba B Department of Mathematics, Sri Siva Subramaniya Nadar College of Engineering, Kalavakkam- 603110
  • Sri Siva Subramaniya Department of Electronics and Communication Engineering, Sri Siva Subramaniya Nadar College of Engineering, Kalavakkam- 603110;
  • D.Nagarajan Department of Mathematics, Rajalakshmi Institute of Technology, Chennai- 600124
  • Broumi Said Laboratory of Information Processing, Faculty of Science Ben M’Sik, University of Hassan II, Casablanca, Morocco

Keywords:

Image processing, Neutrosophic image processing, Image segmentation, DICOM image: ouble Bounded Rough Sets, Rough Sets, Neutrosophic Sets, Fuzzy Sets, Face Expressions, Student expression detection, Facial key points, Random forests, Decision Trees, Gini Index Impurity s

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.

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Published

2023-05-01

Issue

Vol. 55 (2023): Neutrosophic Sets and Systems

Section

SI#1,2024: Neutrosophical Advancements And Their Impact on Research

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

 Creative Commons Attribution (CC BY) license.

How to Cite

Praba B, Sri Siva Subramaniya, D.Nagarajan, & Broumi Said. (2023). Analysis of Teaching-Learning Efficiency Using Attribute Based Double Bounded Rough Neutrosophic Set Driven Random Forests. Neutrosophic Sets and Systems, 55, 13-34. http://fs.unm.edu/nss8/index.php/111/article/view/3177