Design of optimal deep learning based human activity recognition on sensor enabled internet of things environment
| dc.contributor.author | Al-Wesabi, Fahd N. | |
| dc.contributor.author | Albraikan, Amani Abdulrahman | |
| dc.contributor.author | Hilal, Anwer Mustafa | |
| dc.contributor.author | Al-Shargabi, Asma Abdulghani | |
| dc.contributor.author | Alhazbi, Saleh | |
| dc.contributor.author | Al Duhayyim, Mesfer | |
| dc.contributor.author | Rizwanullah, Mohammed | |
| dc.contributor.author | Hamza, Manar Ahmed | |
| dc.date.accessioned | 2026-06-14T22:56:33Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Human activity recognition has become an important area in smart healthcare and Internet of Things environments. This study designs an optimal deep learning based human activity recognition model for sensor-enabled IoT environments. The model involves preprocessing, feature extraction, and classification using deep learning, with parameter optimization to improve recognition performance on sensor activity data. | en_US |
| dc.identifier | 10.1109/ACCESS.2021.3112973 | |
| dc.identifier.citation | Al-Wesabi, F. N., Albraikan, A. A., Hilal, A. M., Al-Shargabi, A. A., Alhazbi, S., Al Duhayyim, M., Rizwanullah, M., & Hamza, M. A. (2021). Design of optimal deep learning based human activity recognition on sensor enabled internet of things environment. IEEE Access, 9, 143988-143996. https://doi.org/10.1109/ACCESS.2021.3112973 | en_US |
| dc.identifier.uri | https://repository.ust.edu.ye/handle/123456789/335 | |
| dc.identifier.uri | https://ieeexplore.ieee.org/document/9539181 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.title | Design of optimal deep learning based human activity recognition on sensor enabled internet of things environment | en_US |
| dc.type | Article | en_US |