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A Fault Prognostic System for the Turbine Guide Bearings of a Hydropower Plant Using Long-Short Term Memory (LSTM)

Autor: Afridi, Yasir Saleem, Shah, Mian Ibad Ali, Khan, Adnan, Kareem, Atia, Hasan, Laiq

Hydroelectricity, being a renewable source of energy, globally fulfills the electricity demand. Hence, Hydropower Plants (HPPs) have always been in the limelight of research. The fast-paced technological advancement is enabling us to develop state-of

Externí odkaz: http://arxiv.org/abs/2407.19040

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MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis

Autor: Shaaban, Mai A., Khan, Adnan, Yaqub, Mohammad

Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to incomplete electronic health records (EHR). This paper introduces Med

Externí odkaz: http://arxiv.org/abs/2403.15585

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Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text

Autor: Shaaban, Mai A., Akkasi, Abbas, Khan, Adnan, Komeili, Majid, Yaqub, Mohammad

The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing. These entities serve as essential building blocks for clinical information extraction, enablin

Externí odkaz: http://arxiv.org/abs/2401.15780

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Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization

Autor: Khan, Adnan, Shaaban, Mai A., Khan, Muhammad Haris

Beyond attaining domain generalization (DG), visual recognition models should also be data-efficient during learning by leveraging limited labels. We study the problem of Semi-Supervised Domain Generalization (SSDG) which is crucial for real-world ap

Externí odkaz: http://arxiv.org/abs/2401.13965

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