Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model-Reference-Cited by-同舟云学术

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Brain–computer interface for assessment of mental efforts in e‐learning using the nonmarkovian queueing model

Published:2020-02-27 Issue:2 Volume:29 Page:394-410
ISSN:1061-3773
Container-title:Computer Applications in Engineering Education
language:en
Short-container-title:Comput Appl Eng Educ

Author:

Balamurugan B.¹²^ORCID,Mullai M.³,Soundararajan S.⁴,Selvakanmani S.⁵,Arun D.⁶

Affiliation:

1. Alagappa University Karaikudi India

2. Department of Mathematics, Faculty of Mathematics Velammal Institute of Technology Chennai India

3. Department of Mathematics, Faculty of Mathematics Alagappa University Karaikudi India

4. Department of Mathematics, Faculty of Computer Science and Engineering Velammal Institute of Technology Chennai India

5. Department of Computer Science and Engineering, Faculty of Computer Science and Engineering Velammal Institute of Technology Chennai India

6. Department of Computer Science and Engineering, Faculty of Computer Science and Engineering Arasu Engineering College Kumbakonam India

Publisher

Wiley

Subject

General Engineering,Education,General Computer Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cae.22209

Reference39 articles.

1. A.AbouyahyaandS.El Fkihi(2018 May). An optimization of the K‐Nearest Neighbor using Dynamic Time Warping as a measurement similarity for facial expressions recognition. InProceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications(p. 31). ACM.

2. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

3. A model of factors affecting learning performance through the use of social media in Malaysian higher education

4. Empirical characterization of random forest variable importance measures

5. Human stress classification using EEG signals in response to music tracks

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