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Original Article
Emotion Detection System for Online Learning
R.Suresh kumar1
V.Boopesh2
S.Deepan3
S.Dheenadhayalan4
Suresh kumar A5
1 2 3 4 Department of CSE, Rathinam Technical Campus Coimbatore, Tamil Nadu, India. 5 Assistant Professor, Department of CSE, Rathinam Technical Campus Coimbatore, Tamil Nadu, India.
Published Online: November-December 2025
Pages: 01-08
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250606001
References
1. P. Ekman and W. V. Friesen, Facial Action Coding System: A Technique for the Measurement of Facial Movement, Palo Alto: Consulting Psychologists Press, 1978.
2. Y. Li, S. Li, and H. Li, “Real-Time Facial Emotion Recognition Using Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 123456–123467, 2020.
3. C. Zhang, X. Wang, and L. Wang, “Speech Emotion Recognition Using RNN and MFCC Features,” International Journal of Speech Technology, vol. 22, pp. 345–356, 2019.
4. R. Poria, E. Cambria, D. Hazarika, and K. Kwok, “Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baseline,” IEEE Intelligent Systems, vol. 32, no. 6, pp. 18–25, Nov.-Dec. 2017.
5. S. D’Mello and A. Graesser, “Multimodal Affect Detection: A Survey of Literature and Trends,” User Modeling and User-Adapted Interaction, vol. 22, pp. 31–64, 2012.
6. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
7. I. Livingstone and F. Russo, “The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A Dynamic, Multimodal Set of Facial and Vocal Expressions,” PLoS ONE, vol. 13, no. 5, e0196391, 2018.
8. I. Mohammad, M. A. Khan, and R. Ahmad, “Emotion Recognition in E-Learning Using Facial and Speech Analysis,” International Journal of Emerging Technologies in Learning, vol. 15, no. 2, pp. 120–132, 2020.
9. A. Mollahosseini, D. Chan, and M. H. Mahoor, “Going Deeper in Facial Expression Recognition Using Deep Neural Networks,” 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10, 2016.
10. Z. Yin, H. Zeng, and S. Wang, “Multimodal Emotion Detection for Online Learning Systems,” Journal of Educational Technology & Society, vol. 23, no. 4, pp. 101–114, 2020.
2. Y. Li, S. Li, and H. Li, “Real-Time Facial Emotion Recognition Using Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 123456–123467, 2020.
3. C. Zhang, X. Wang, and L. Wang, “Speech Emotion Recognition Using RNN and MFCC Features,” International Journal of Speech Technology, vol. 22, pp. 345–356, 2019.
4. R. Poria, E. Cambria, D. Hazarika, and K. Kwok, “Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baseline,” IEEE Intelligent Systems, vol. 32, no. 6, pp. 18–25, Nov.-Dec. 2017.
5. S. D’Mello and A. Graesser, “Multimodal Affect Detection: A Survey of Literature and Trends,” User Modeling and User-Adapted Interaction, vol. 22, pp. 31–64, 2012.
6. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
7. I. Livingstone and F. Russo, “The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A Dynamic, Multimodal Set of Facial and Vocal Expressions,” PLoS ONE, vol. 13, no. 5, e0196391, 2018.
8. I. Mohammad, M. A. Khan, and R. Ahmad, “Emotion Recognition in E-Learning Using Facial and Speech Analysis,” International Journal of Emerging Technologies in Learning, vol. 15, no. 2, pp. 120–132, 2020.
9. A. Mollahosseini, D. Chan, and M. H. Mahoor, “Going Deeper in Facial Expression Recognition Using Deep Neural Networks,” 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10, 2016.
10. Z. Yin, H. Zeng, and S. Wang, “Multimodal Emotion Detection for Online Learning Systems,” Journal of Educational Technology & Society, vol. 23, no. 4, pp. 101–114, 2020.
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