Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...

Background Inflammatory bowel disease (IBD) arises from complex interactions among diet, host and gut microbiome. Although diet influences intestinal inflammation, the microbial and metabolic pathways ...

Background The relationship of social determinants of health (SDOH), environmental exposures and medical history to lung function trajectories is underexplored. A better understanding of these ...

Objective Anxiety affects up to one-third of adults with asthma and is linked to poorer disease outcomes and reduced quality ...

By modeling the single-trial electroencephalogram of participants performing perceptual decisions, and building on predictions from two century-old psychological laws, we estimate the times of ...

Professor Klaus Nordhausen develops modern multivariate statistical methods to analyze high-dimensional and large datasets in different fields.

Objective Patients with atrial fibrillation (AF) frequently have multiple comorbidities that increase the risk of hospitalisation and contribute to higher mortality. However, studies examining the ...

Personally identifiable information has been found in DataComp CommonPool, one of the largest open-source data sets used to train image generation models. Millions of images of passports, credit cards ...

ABSTRACT: This research examines the interrelationships among uncertainty avoidance (UA), entrepreneurial motivations, and entrepreneurial intention (EI) within the context of Vietnamese higher ...

Recent advances in green chemistry have made multivariate experimental design popular in sample preparation development. This approach helps reduce the number of measurements and data for evaluation ...