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The value of human data annotation for machine learning based anomaly detection in environmental systems
Russo, S., Besmer, M. D., Blumensaat, F., Bouffard, D., Disch, A., Hammes, F., … Villez, K. (2021). The value of human data annotation for machine learning based anomaly detection in environmental systems. Water Research, 206, 117695 (10 pp.). https://doi.org/10.1016/j.watres.2021.117695
Active learning for anomaly detection in environmental data
Russo, S., Lürig, M., Hao, W., Matthews, B., & Villez, K. (2020). Active learning for anomaly detection in environmental data. Environmental Modelling and Software, 134, 104869 (11 pp.). https://doi.org/10.1016/j.envsoft.2020.104869
Automated model selection in principal component analysis: a new approach based on the cross-validated ignorance score
Russo, S., Li, G., & Villez, K. (2019). Automated model selection in principal component analysis: a new approach based on the cross-validated ignorance score. Industrial and Engineering Chemistry Research, 58(30), 13448-13468. https://doi.org/10.1021/acs.iecr.9b00642
EIT-based tactile sensing patches for rehabilitation and human machine interaction
Russo, S., Carbonaro, N., & Tognetti, A. (2019). EIT-based tactile sensing patches for rehabilitation and human machine interaction. In M. C. Carrozza, S. Micera, & J. L. Pons (Eds.), Biosystems & biorobotics: Vol. 22. Wearable robotics: challenges and trends (pp. 13-17). https://doi.org/10.1007/978-3-030-01887-0_3