IEEE

Physics-Informed Uncertainty-Aware Dynamic Balancing Learning Framework for Unsupervised Universal Domain Generalization Diagnosis Scenario

Abstract: Conventional domain generalization (DG) assumes fully labeled source domains and consistent label spaces across domains. However, in industrial diagnostics, incomplete labeling is common, ...
Law

Training Alone Is Not Enough: Lessons From 'Recentive' and USPTO AI Examples on Patent Eligible Machine-Learning Claims

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...
Frontiers

The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia

Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...
Journal of Medical Internet Research

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study

Objective: This study aims to develop an explainable machine learning model, incorporating stacking techniques, to predict the occurrence of liver injury in patients with sepsis and provide decision ...
IEEE

Combining Unsupervised Domain Adaptation and Semi-Supervised Learning for Power Line and Transmission Tower Segmentation

Abstract: Fully supervised image segmentation can effectively extract power line (PL) and transmission tower (TT) from aerial images. However, its performance is constrained by the lack of ...