Aresta, S.; Bortone, I.; Bottiglione, F.; Di Noia, T.; Di Sciascio, E.; Lofù, D.; Musci, M.; Narducci, F.; Pazienza, A.; Sardone, R.;
et al. Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support. Appl. Sci. 2022, 12, 12350.
https://doi.org/10.3390/app122312350
AMA Style
Aresta S, Bortone I, Bottiglione F, Di Noia T, Di Sciascio E, Lofù D, Musci M, Narducci F, Pazienza A, Sardone R,
et al. Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support. Applied Sciences. 2022; 12(23):12350.
https://doi.org/10.3390/app122312350
Chicago/Turabian Style
Aresta, Simona, Ilaria Bortone, Francesco Bottiglione, Tommaso Di Noia, Eugenio Di Sciascio, Domenico Lofù, Mariapia Musci, Fedelucio Narducci, Andrea Pazienza, Rodolfo Sardone,
and et al. 2022. "Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support" Applied Sciences 12, no. 23: 12350.
https://doi.org/10.3390/app122312350
APA Style
Aresta, S., Bortone, I., Bottiglione, F., Di Noia, T., Di Sciascio, E., Lofù, D., Musci, M., Narducci, F., Pazienza, A., Sardone, R., & Sorino, P.
(2022). Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support. Applied Sciences, 12(23), 12350.
https://doi.org/10.3390/app122312350
