Mahmud, T, Sazzad Sayyed, A. Q. M., Fattah, S. A. & Kung, S.-Y. (2021). A novel multi-stage training approach for human activity recognition from multimodal wearable sensor data using deep neural network. IEEE Sensors Journal, 21 (2), 1715-1726. Zugriff am 02.03.2021 unter https://doi.org/10.1109/JSEN.2020.3015781
APA-Zitierstil (7. Ausg.)Mahmud, T., Sazzad Sayyed, A. Q. M., Fattah, S. A., & Kung, S. (2021). A novel multi-stage training approach for human activity recognition from multimodal wearable sensor data using deep neural network. IEEE Sensors Journal, 21(2), 1715-1726.
Chicago-Zitierstil (17. Ausg.)Mahmud, T., A. Q. M. Sazzad Sayyed, S. A. Fattah, und S.-Y Kung. "A Novel Multi-stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network." IEEE Sensors Journal 21, no. 2 (2021): 1715-1726.
MLA-Zitierstil (9. Ausg.)Mahmud, T., et al. "A Novel Multi-stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network." IEEE Sensors Journal, vol. 21, no. 2, 2021, pp. 1715-1726.