Open Access
Issue
E3S Web Conf.
Volume 426, 2023
The 5th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2023)
Article Number 01049
Number of page(s) 6
Section Integrated Sustainable Science and Technology Innovation
DOI https://doi.org/10.1051/e3sconf/202342601049
Published online 15 September 2023
  1. M. Quiroz, R. Patiño, J. Diaz-Amado, and Y. Cardinale, “Article group emotion detection based on social robot perception,” Sensors, vol. 22, no. 10, 2022, doi: 10.3390/s22103749. [CrossRef] [PubMed] [Google Scholar]
  2. A. Debowska, B. Horeczy, D. Boduszek, and D. Dolinski, “A repeated cross-sectional survey assessing university students’ stress, depression, anxiety, and suicidality in the early stages of the COVID-19 pandemic in Poland,” Psychol Med, vol. 52, no. 15, 2022, doi: 10.1017/S003329172000392X. [Google Scholar]
  3. D. Zhu, Y. Fu, X. Zhao, X. Wang, and H. Yi, “Facial emotion recognition using a novel fusion of convolutional neural network and local binary pattern in crime investigation,” Comput Intell Neurosci, vol. 2022, pp. 1–14, Sep. 2022, doi: 10.1155/2022/2249417. [Google Scholar]
  4. M. A. H. Akhand, S. Roy, N. Siddique, M. A. S. Kamal, and T. Shimamura, “Facial emotion recognition using transfer learning in the deep CNN,” Electronics (Switzerland), vol. 10, no. 9, 2021, doi: 10.3390/electronics10091036. [Google Scholar]
  5. W. H. Abdulsalam, R. S. Alhamdani, and M. N. Abdullah, “Facial emotion recognition from videos using deep convolutional neural networks,” International Journal of Machine Learning and Computing, vol. 9, no. 1, 2019, doi: 10.18178/ijmlc.2019.9.1.759. [Google Scholar]
  6. S. Depuru, A. Nandam, P. A. Ramesh, M. Saktivel, K. Amala, and Sivanantham, “Human emotion recognition system using deep learning technique,” Journal of Pharmaceutical Negative Results, vol. 13, no. 4, 2022, doi: 10.47750/pnr.2022.13.04.141. [Google Scholar]
  7. P. Kedari, M. Kapile, D. Kadole, and S. Jaikar, “Face emotion detection using deep learning,” in ACCESS 2021 - Proceedings of 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, 2021. doi: 10.1109/ACCESS51619.2021.9563343. [Google Scholar]
  8. L. Ali, F. Alnajjar, H. Al Jassmi, M. Gochoo, W. Khan, and M. A. Serhani, “Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures,” Sensors, vol. 21, no. 5, 2021, doi: 10.3390/s21051688. [Google Scholar]
  9. M. F. Ali, M. Khatun, and N. Turzo, “Facial emotion detection using neural network,” The International Journal of Scientific and Engineering Research, vol. 11, no. 3, 2020. [Google Scholar]
  10. Z. Deng, Q. Zhu, P. He, D. Zhang, and Y. Luo, “A saliency detection and gram matrix transform- based convolutional neural network for image emotion classification,” Security and Communication Networks, vol. 2021, 2021, doi: 10.1155/2021/6854586. [Google Scholar]
  11. J. Omar, N. Husna Shabrina, A. N. Bhakti, and A. Patria, “Emotion recognition using convolutional neural network on virtual meeting image,” Ultima Computing: Jurnal Sistem Komputer, vol. 13, no. 1, 2021. [Google Scholar]
  12. X. Lu, “Deep learning based emotion recognition and visualization of figural representation,” Frontiers in psychology, vol. 12, 2022, doi: 10.3389/fpsyg.2021.818833. [Google Scholar]
  13. M. S. Raghav Puri, Archit Gupta, “Emotion detection using image processing in Python,” in 12th INDIACom; INDIACom-2018; IEEE Conference ID: 42835 2018 5th International Conference on “Computing for Sustainable Global Development”, 14th - 16th March, 2018, 2018. [Google Scholar]
  14. A. Keshri, A. Singh, B. Kumar, D. Pratap, and A. Chauhan, “Automatic Detection and classification of human emotion in real-time scenario,” Journal of ISMAC, vol. 4, no. 1, pp. 41–53, 2022, doi: 10.36548/jismac.2022.1.005. [CrossRef] [Google Scholar]
  15. A. Khlyzova, C. Silberer, and R. Klinger, “On the complementarity of images and text for the expression of emotions in social media,” in WASSA 2022 - 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Proceedings of the Workshop, 2022. doi: 10.18653/v1/2022.wassa-1.1. [Google Scholar]
  16. C. L. Lee, W. Pei, Y. C. Lin, A. Granmo, and K. H. Liu, “Emotion detection based on pupil variation,” Healthcare (Switzerland), vol. 11, no. 3, 2023, doi: 10.3390/healthcare11030322. [Google Scholar]
  17. D. Wang, J. Mo, G. Zhou, L. Xu, and Y. Liu, “An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images,” PLoS One, vol. 15, no. 11, 2020, doi: 10.1371/journal.pone.0242535. [Google Scholar]
  18. J. C. Obi, “A comparative study of several classification metrics and their performances on data,” World Journal of Advanced Engineering Technology and Sciences, vol. 8, no. 1, pp. 308–314, 2023, doi: 10.30574/wjaets.2023.8.1.0054. [Google Scholar]

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