Predicting young imposter syndrome using ensemble learning
Background. Imposter syndrome (IS), associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students' IS using the machine learning ensemble appr...
Saved in:
Main Authors: | Khan, Md. Nafiul Alam, Miah, M. Saef Ullah, Shahjalal, Md., Sarwar, Talha, Rokon, Md. Shahariar |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2022
|
Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/33586/1/Predicting%20young%20imposter%20syndrome%20using%20ensemble%20learning.pdf http://umpir.ump.edu.my/id/eprint/33586/ https://doi.org/10.1155/2022/8306473 https://doi.org/10.1155/2022/8306473 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding
by: Sarwar, Talha, et al.
Published: (2022) -
Study of keyword extraction techniques for electric double-layer capacitor domain using text similarity indexes: An experimental analysis
by: Miah, M. Saef Ullah, et al.
Published: (2021) -
Prediction accuracy measurements for ensemble classifier
by: Abdullah, ,, et al.
Published: (2012) -
Sentence boundary extraction from scientific literature of electric double layer capacitor domain: Tools and techniques
by: Miah, M. Saef Ullah, et al.
Published: (2022) -
Improving ensemble decision tree performance using Adaboost and Bagging
by: Hasan, Md Rajib, et al.
Published: (2015)