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All issues Volume 79 (2025) ITM Web Conf., 79 (2025) 01043 References
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Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01043
Number of page(s) 8
DOI https://doi.org/10.1051/itmconf/20257901043
Published online 08 October 2025
  1. R.A. Jaswal, S. Dhingra, Empirical analysis of multiple modalities for emotion recognition using convolutional neural network. Meas. Sens. 26, 100716 (2023). https://doi.org/10.1016/j.measen.2023.100716 [Google Scholar]
  2. H.M. Shahzad, S.M. Bhatti, A. Jaffar, S. Akram, M. Alhajlah, A. Mahmood, Hybrid facial emotion recognition using CNN-based features. Appl. Sci. 13, 5572 (2023). https://doi.org/10.3390/app13095572 [Google Scholar]
  3. M. Hussain, H.A. AboAlSamh, I. Ullah, Emotion recognition system based on two-level ensemble of deep-convolutional neural network models. IEEE Access 11, 16875–16895 (2023). https://doi.org/10.1109/ACCESS.2023.3245830 [Google Scholar]
  4. M.C. Gursesli, S. Lombardi, M. Duradoni, L. Bocchi, A. Guazzini, A. Lanata, Facial emotion recognition (FER) through custom lightweight CNN model: performance evaluation in public datasets. IEEE Access 12, 45543–45559 (2024). https://doi.org/10.1109/ACCESS.2024.3380847 [Google Scholar]
  5. Y. Huang, D. Bo, Emotion classification and achievement of students in distance learning based on the knowledge state model. Sustainability 15, 2367 (2023). https://doi.org/10.3390/su15032367 [Google Scholar]
  6. Z. Huang, Y. Ma, R. Wang, W. Li, Y. Dai, A model for EEG-based emotion recognition: CNN-BI- LSTM with attention mechanism. Electronics 12, 3188 (2023). https://doi.org/10.3390/electronics12143188 [Google Scholar]
  7. H.D. Le, G.S. Lee, S.H. Kim, S. Kim, H.J. Yang, Multi-label multimodal emotion recognition with transformer-based fusion and emotion-level representation learning. IEEE Access 11, 1474214751 (2023). https://doi.org/10.1109/ACCESS.2023.3244390 [Google Scholar]
  8. E.E. Arslan, M.F. Akşahin, M. Yilmaz, H.E. Ilgın, Towards emotionally intelligent virtual environments: classifying emotions through a biosignal-based approach. Appl. Sci. 14, 8769 (2024). https://doi.org/10.3390/app14198769 [Google Scholar]
  9. D. Ngo, A. Nguyen, B. Dang, H. Ngo, Facial expression recognition for examining emotional regulation in synchronous online collaborative learning. Int. J. Artif. Intell. Educ. 34, 650–669 (2024). https://doi.org/10.1007/s40593-023-00378-7 [Google Scholar]
  10. J. Qi, H. Tang, Z. Zhu, Exploring an affective and responsive virtual environment to improve remote learning. Virtual Worlds 2, 53–74 (2023). https://doi.org/10.3390/virtualworlds2010004 [Google Scholar]
  11. S.G. Rajesh, S.V. Madangarli, G.S. Pisharady, R. Subrahmanyam, Enhancement of virtual assistants through multimodal AI for emotion recognition. IEEE Access. 13, 102159–102179 (2025). https://doi.org/10.1109/ACCESS.2025.3577664 [Google Scholar]
  12. M. Shomoye, R. Zhao, Automated emotion recognition of students in virtual reality classrooms. Comput. Educ.: X Reality 5, 100082 (2024). https://doi.org/10.1016/j.cexr.2024.100082 [Google Scholar]
  13. M. Aly, Revolutionizing online education: Advanced facial expression recognition for realtime student progress tracking via deep learning model. Multimed. Tools Appl. 84, 12575–12614 (2025). https://doi.org/10.1007/s11042-024-19392-5 [Google Scholar]
  14. M. Aly, A. Ghallab, I.S. Fathi, Enhancing facial expression recognition system in online learning context using efficient deep learning model. IEEE Access 11, 121419–121433 (2023). https://doi.org/10.1109/ACCESS.2023.3325407 [Google Scholar]
  15. C. Gautam, K.R. Seeja, Facial emotion recognition using handcrafted features and CNN. Procedia Comput. Sci. 218, 1295–1303 (2023). https://doi.org/10.1016/j.procs.2023.01.108 [Google Scholar]
  16. KDEF dataset link: KDEF Database [Google Scholar]
  17. RAF-DB dataset link: RAF-DB DATASET [Google Scholar]

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