Machine learning provides a powerful approach for predicting the complex thermophysical properties of nanofluids. This study employs a suite of machine learning algorithms to forecast the viscosity, thermal conductivity, and electrical conductivity o... read more
BACKGROUND: Mammograms contain imaging biomarkers that can predict future breast cancer risk using deep learning (DL) models. We evaluated whether adding a polygenic risk score (PRS) improves performance of the image-only DL breast cancer risk model ... read more
Cardiovascular diseases (CVDs) are a significant and widespread cause of death in the world, continuing to increase mortality rates. Therefore, timely identification and diagnosis are essential for a patient's optimized recovery and longevity. In thi... read more
Journal of imaging informatics in medicine
Apr 6, 2026
Deep learning algorithms for detecting micrometastasis in breast cancer lymph nodes show promising results when used complementarily to improve the efficiency of pathologists' routines. However, in the current literature, there are critical limitatio... read more
Hydrological model accuracy is often constrained by limited in-situ data. This study develops a coupled framework integrating machine-learning-based remote sensing retrievals with the Environmental Fluid Dynamics Code (EFDC) to improve reservoir wate... read more
In August 2024, the World Health Organization declared the ongoing mpox upsurge in Africa a Public Health Emergency of International Concern, underscoring mpox as an emerging global threat. In non‑endemic countries such as Saudi Arabia, understanding... read more
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