Open Access
Issue
ITM Web Conf.
Volume 81, 2026
International Conference on Emerging Technologies for Multidisciplinary Innovation and Sustainability (ETMIS 2025)
Article Number 01028
Number of page(s) 8
DOI https://doi.org/10.1051/itmconf/20268101028
Published online 23 January 2026
  1. Singh, J., Wazid, M., Singh, D. P., & Pundir, S. (2022). An embedded LSTM-based scheme for depression detection and analysis. Procedia Computer Science, 215, 166–175. [Google Scholar]
  2. Sridharan, S., Aishwarya, S., Aishwarya, S. P., & Santhiya, S. (2023). Depression detection in social media users using deep learning. American Journal of Psychiatric Rehabilitation, 26(1), 45–58. [Google Scholar]
  3. Orabi, A. H., Buddhitha, P., Orabi, M. H., & Inkpen, D. (2018). Deep learning for depression detection of Twitter users. In CLPsych Workshop (pp. 88-97). [Google Scholar]
  4. Devi, T. J., & Gopi, A. (2024). The evaluation of deep learning models for detecting mental disorders. International Journal of Intelligent Systems and Applications in Engineering (IJISAE), 12(2), 345–352. [Google Scholar]
  5. Coppersmith, G., Leary, R., Crutchley, P., & Fine, A. (2018). Natural language processing of social media as screening for suicide risk. Biomedical Informatics Insights, 10, 1–11. [Google Scholar]
  6. Narayan, U., & Kumar, D. (2024). Sentiment analysis using transformer-based model. International Journal of Innovative Research in Computer Science & Technology (IJIRCST), 12(S1), 210–218. [Google Scholar]
  7. Kour, N., & Gupta, D. (2023). A hybrid deep learning approach for depression prediction using CNN-BiLSTM. Multimedia Tools and Applications, 82, 40963–40988. [Google Scholar]
  8. Bokolo, A. J., & Liu, Y. (2023). Deep learning-based depression detection: Machine learning vs transformers. Electronics, 12(21), 4396. [Google Scholar]
  9. Bendebane, H., et al. (2023). Multi-class deep learning for depressive/anxiety disorders. Algorithms, 16(12), 543. [Google Scholar]
  10. Sajib, M. H., Hasan, M. R., & Rahman, M. M. (2024). Depression detection from Bangla text using RNNs. arXiv:2412.05861. [Google Scholar]
  11. Singh, R., Kumar, A., & Sharma, V. (2024). Sentence-BERT ensemble for depression detection. arXiv:2409.13713. [Google Scholar]
  12. Ta, N., Alhassan, A., & Burley, D. (2025). Detecting signs of depression. Safety and Health at Work, 16, 100827. [Google Scholar]
  13. Rahman, M. M., Sultana, T., & Akter, S. (2024). Depression detection through explainable AI. arXiv:2404.13104. [Google Scholar]
  14. Abirami, S., Priyadharshini, R., Rajeswari, S., & Kumar, P. (2025). Explainable AI-driven depression detection. Frontiers in Artificial Intelligence, 5, 1627078. [Google Scholar]
  15. Adewumi, A. O., Owoeye, O., & Misra, S. (2021). A hybrid approach to detecting depression. arXiv:2106.10485. [Google Scholar]
  16. Nadeem, M., Iqbal, A., Malik, H., & Rehman, S. (2025). Early detection of depression. SES Journal, 6(1), 1–12. [Google Scholar]
  17. Goel, N., Anand, R., Amoli, A., Chuki, K., Sharma, P., & Harnal, S. (2024). Automated depression detection system. International Journal of Engineering Research & Technology (IJERT), 13(05), 245–252. [Google Scholar]
  18. Ahmed, S., Rakin, S., Ibn Waliur, M. W., Islam, N. B., Hossain, B., & Akbar, M. M. (2024). Depression detection from Bangla using RNNs. arXiv:2412.05861. [Google Scholar]
  19. De Choudhury, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. In ICWSM 2013 (pp. 128-137). [Google Scholar]
  20. Rahman, M., Chowdhury, S., Khan, T., Hossain, M., & Das, A. (2025). Depression and suicidal tendency detection. Behavioral Sciences, 15(3), 352. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.