This study presents a deep learning-enabled framework for non-contact respiratory pattern classification using software-defined radio frequency sensing. The proposed approach builds on an SDR-based wireless channel state information acquisition system to identify three breathing patterns: normal breathing, fast breathi...
Qurat Ul Ain, Nan Zhao, R. Asif et al.· Discover Artificial Intellig...· 0 citations
This paper demonstrates the reliability of Radio Frequency (RF)-based systems for respiratory monitoring, as well as the possibility of extracting highly detailed features relevant to developing more complex, real-time healthcare solutions.
Qurat Ul Ain, R. Asif, Nan Zhao et al.· E3S Web of Conferences· 0 citations
This paper presents an Intrusion Detection System (IDS) grounded in Explainable Artificial Intelligence (XAI) to enhance transparency, reliability, and user trust in IoT security. To make detection decisions interpretable and accountable, the system employs ensemble machine learning for real-time anomaly detection and...
Ohood Alharbi, R. Shaikh, Raheel Hassan et al.· International Conference on...· 0 citations
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