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Real-time multimodal emotion classification system in E-Learning context

Author
Nandi, A.; Xhafa, F.; Subirats, L.; Fort, S.
Type of activity
Presentation of work at congresses
Name of edition
22nd International Conference on Engineering Applications of Neural Networks
Date of publication
2021
Presentation's date
2021-06
Book of congress proceedings
Proceedings of the 22nd Engineering Applications of Neural Networks Conference, EANN 2021
First page
423
Last page
435
Publisher
Springer
DOI
10.1007/978-3-030-80568-5_35
Project funding
TutorAI Augmented Workspace
Repository
http://hdl.handle.net/2117/349313 Open in new window
URL
https://link.springer.com/chapter/10.1007%2F978-3-030-80568-5_35 Open in new window
Abstract
Emotions of learners are crucial and important in e-learning as they promote learning. To investigate the effects of emotions on improving and optimizing the outcomes of e-learning, machine learning models have been proposed in the literature. However, proposed models so far are suitable for offline mode, where data for emotion classification is stored and can be accessed boundlessly. In contrast, when data arrives in a stream, the model can see the data once and real-time response is required f...
Citation
Nandi, A. [et al.]. Real-time multimodal emotion classification system in E-Learning context. A: International Conference on Engineering Applications of Neural Networks. "Proceedings of the 22nd Engineering Applications of Neural Networks Conference, EANN 2021". Berlín: Springer, 2021, p. 423-435. ISBN 978-3-030-80568-5. DOI 10.1007/978-3-030-80568-5_35.
Keywords
Affective computing, Feed forward neural network, Real-time multimodal emotion classification system, e-learning
Group of research
IMP - Information Modelling and Processing

Participants

  • Arijit, Nandi  (Corresponding author)
  • Xhafa Xhafa, Fatos  (author and speaker )
  • Subirats Maté, Laia  (author and speaker )
  • Fort, Santiago  (author and speaker )