Outputs (2)

A Hybrid Wasserstein GAN and Autoencoder Model for Robust Intrusion Detection in IoT (2025)
Journal Article
Alshehri, M. S., Saidani, O., Al Malwi, W., Asiri, F., Latif, S., Khattak, A. A., & Ahmad, J. (online). A Hybrid Wasserstein GAN and Autoencoder Model for Robust Intrusion Detection in IoT. Computer Modeling in Engineering & Sciences, https://doi.org/10.32604/cmes.2025.064874

The emergence of Generative Adversarial Network (GAN) techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems (IDS). However, conventional GAN-based IDS models face several challeng... Read More about A Hybrid Wasserstein GAN and Autoencoder Model for Robust Intrusion Detection in IoT.

Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning (2025)
Journal Article
Umair, M., Ahmad, J., Alasbali, N., Saidani, O., Hanif, M., Khattak, A. A., & Khan, M. S. (2025). Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning. Frontiers in Computational Neuroscience, 19, Article 1569828. https://doi.org/10.3389/fncom.2025.1569828

Introduction: Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disord... Read More about Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning.