Researchers at Stevens Institute of Technology used machine learning tools and social network theory — the study of how ...
Abstract: Modern grid codes require wind power plants to provide sufficient reactive power capability at the point of common coupling (PCC) to maintain voltage security. Meeting these provisions is ...
RESEARCHERS reported that new transcranial magnetic stimulation (TMS) biomarkers, combined with machine learning, accurately distinguished individuals with major depressive disorder (MDD) from healthy ...
This capstone project develops a machine learning pipeline to classify coding genetic variants as pathogenic versus benign using pretrained protein language model embeddings (ESM2). The work supports ...
Connecting the dots: By applying machine learning techniques to satellite imagery, researchers have built an unprecedented database of man-made structures across the globe. The data could reshape ...
aDepartment of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for ...
The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
Abstract: Using machine learning applied to multimodal physiological data allows the classification of cognitive workload (low, moderate, or high load) during task performance. However, current ...
Introduction: Peripheral Artery Disease (PAD) is a progressive vascular disorder impairing mobility, raising fall risk, and reducing quality of life. Early detection is key to preventing amputations ...
Alterations in brain structure have been suggested to be associated with bulimia nervosa (BN). This study aimed to employ machine learning (ML) methods based on diffusion tensor imaging (DTI) to ...
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