Using Neutrosophic Trait Measures to Analyze Impostor Syndrome in College Students after COVID-19 Pandemic with Machine Learning

Authors

  • Riya Eliza Shaju School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Katpadi, Vellore, Tamil Nadu, 632014, India
  • Meghana Dirisala School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Katpadi, Vellore, Tamil Nadu, 632014, India
  • Muhammad Ali Najjar School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Katpadi, Vellore, Tamil Nadu, 632014, India
  • Ilanthenral Kandasamy School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Katpadi, Vellore, Tamil Nadu, 632014, India
  • Vasantha Kandasamy School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Katpadi, Vellore, Tamil Nadu, 632014, India
  • F Smarandache University of New Mexico, Gallup, NM, United States

Keywords:

Neutrosophic psychology, Impostor syndrome, Neutrosophic trait measure, SVM, KNN, Random forest

Abstract

Impostor syndrome or Impostor phenomenon is a belief that a person thinks their success is due
 to luck or external factors, not their abilities. This psychological trait is present in certain groups like women.
 In this paper, w e propose a neutrosophic trait measure to represent the psychological concept of the trait
anti trait using re ned neutrosophic sets. This study analysed a group of 200 undergraduate students for
 impostor syndrome, perfectionism, introversion and self-esteem: after the COVID pandemic break in 2021.
 Data labelling w a s carried out using these neutrosophic trait measures. Machine learning models like Support
 Vector Machine(SVM), K-nearest neighbour (K-NN), and random forest were used to model the data; SVM
 provided the best accuracy of 92.15%.

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Published

2023-12-01

Issue

Vol. 60 (2023): Neutrosophic Sets and Systems

Section

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

License

Copyright (c) 2023 Neutrosophic Sets and Systems

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

Riya Eliza Shaju, Meghana Dirisala, Muhammad Ali Najjar, Ilanthenral Kandasamy, Vasantha Kandasamy, & F Smarandache. (2023). Using Neutrosophic Trait Measures to Analyze Impostor Syndrome in College Students after COVID-19 Pandemic with Machine Learning. Neutrosophic Sets and Systems, 60, 317-334. https://fs.unm.edu/nss8/index.php/111/article/view/3768