Open Access
| Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
|---|---|---|
| Article Number | 01101 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/matecconf/202439201101 | |
| Published online | 18 March 2024 | |
- H. Dinkel et al., Text-based depression detection on sparse data, IEEE ACCESS. (2019). [Google Scholar]
- F. Cacheda et al., Early Detection of Depression: Social Network Analysis, journal of medical internet research, (2020). [Google Scholar]
- Lei Tong et al., Cost-sensitive Boosting Pruning Trees for depression detection on Twitter, IEEE (2020). [Google Scholar]
- Y. Shen et al., Automatic depression detection: an emotional audio-textual corpus and a gru/bilstm-based model, IEEE, (2022). [Google Scholar]
- Yıldırım et al., A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput. & Applic., pp.1–12, (2018). [Google Scholar]
- Stober et al., Deep feature learning for EEG recordings. arXiv preprint arXiv:1511.04306, (2015). [Google Scholar]
- Acharya et al., Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, pp. 270–278, (2018). [CrossRef] [Google Scholar]
- Krizhevsky et al., ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Inf. Proces. Syst., (2012). [Google Scholar]
- Szegedy et al., Going deeper with convolutions, In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2015). [Google Scholar]
- Simonyan et al., Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, (2014). [Google Scholar]
- LeCun et al., Gradient-based learning applied to document recognition, Proc. IEEE 86, pp. 2278–2323, (1998). [Google Scholar]
- Bashivan et al., Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448, (2015). [Google Scholar]
- Talo et al., Application of deep transfer learning for automated brain abnormality classification using MR images, Cogn. Syst. Res., (2018). [Google Scholar]
- Acharya et al., Automated EEG analysis of epilepsy: A review, Knowledge-Based Syst, Vol. 45, pp. 147–165, (2013). [CrossRef] [Google Scholar]
- U. R. Acharya et al., Characterization of focal EEG signals: A review, Futur. Gener. Comput. Syst., (2019). [Google Scholar]
- S. Stober et al., Deep feature learning for EEG recordings, arXiv preprint arXiv:1511.04306, (2015). [Google Scholar]
- K. Chidananda et al., A Robust Multi Descriptor Fusion with One-Class CNN for Detecting Anomalies in Video Surveillance, International Journal of Safety and Security Engineering, Vol. 13, pp. 1143-1151, (2023). [CrossRef] [Google Scholar]
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