1. Deep learning for heterogeneous human activity recognition in complex IoT applications;Abdel-Basset;IEEE Internet of Things Journal,2022
2. Ahad, N., & Davenport, M. A. (2021). Semi-supervised Sequence Classification through Change Point Detection. In Proceedings of the AAAI conference on artificial intelligence (pp. 6574–6581).
3. Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013). A Public Domain Dataset for Human Activity Recognition Using Smartphones. In Proceedings of the 21th European symposium on artificial neural networks, computational intelligence and machine learning (pp. 437–442).
4. Baños, O., García, R., Terriza, J. A. H., Damas, M., Pomares, H., Ruiz, I. R., et al. (2014). mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications. In Ambient assisted living and daily activities - 6th international work-conference (pp. 91–98).
5. ReMixMatch: Semi-supervised learning with distribution alignment and augmentation anchoring;Berthelot,2019