Explainable Deep Learning for Cyber Threat in IoMT: A Synchronization-Enhanced Sparse Autoencoder Approach
- DOI
- 10.1109/jbhi.2026.3671680
- Published
- 2026
- Container
- IEEE Journal of Biomedical and Health Informatics
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/jbhi.2026.3671680,
title = {Explainable Deep Learning for Cyber Threat in IoMT: A Synchronization-Enhanced Sparse Autoencoder Approach},
author = {Yang Song and Amel Ksibi and Kadambri Agarwal and Baoze Qu},
year = {2026},
journal = {IEEE Journal of Biomedical and Health Informatics},
doi = {10.1109/jbhi.2026.3671680},
url = {https://doi.org/10.1109/jbhi.2026.3671680}
}RIS
TY - JOUR TI - Explainable Deep Learning for Cyber Threat in IoMT: A Synchronization-Enhanced Sparse Autoencoder Approach AU - Yang Song AU - Amel Ksibi AU - Kadambri Agarwal AU - Baoze Qu PY - 2026 JO - IEEE Journal of Biomedical and Health Informatics DO - 10.1109/jbhi.2026.3671680 UR - https://doi.org/10.1109/jbhi.2026.3671680 ER -
APA
Song, Y., Ksibi, A., Agarwal, K., & Qu, B. (2026). Explainable Deep Learning for Cyber Threat in IoMT: A Synchronization-Enhanced Sparse Autoencoder Approach. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2026.3671680
Source records
- crossref · retrieved 2026-11-10T11:48:25.055Z