Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM)
The results indicate that the SFOA-Bi-LSTM model performs well in predicting MA. The SFOA-Bi-LSTM model offers several unique advantages, including efficient hyperparameter tuning via the SFOA, enhanced feature representation through an attention mechanism, and comprehensive temporal analysis using Bi-LSTM. It demonstrates superior performance compared to conventional models while being robust to noisy data due to effective preprocessing.
Authors
- Ahmed Aljabri (ORCID: https://orcid.org/0000-0002-7049-9826)
- Palanisamy Amirthalingam (ORCID: https://orcid.org/0000-0002-5803-1227)
- Vasudevan Mani (ORCID: https://orcid.org/0000-0002-8440-8366)
- Muralikrishnan Dhanasekaran (ORCID: https://orcid.org/0000-0001-8986-3440)
- Yasser Alatawi (ORCID: https://orcid.org/0000-0001-8167-6931)
- S. Manimurugan
- Saleh Fahad Alqifari
- Mostafa A. Sayed Ali (ORCID: https://orcid.org/0000-0002-5851-5088)
- Narmatha Chellamani
- Abdulelah Saeed Alqahtani
Institutions
- Qassim University (SA)
- King Abdulaziz University (SA)
- University of Tabuk (SA)
- Auburn University (US)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251349692
- Citations
- 2
- Primary Topic
- Mobile Health and mHealth Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.63
Funders
- University of Tabuk