IEEE

Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion

Abstract: Brain network analysis plays a crucial role in identifying distinctive patterns associated with neurological disorders. Functional magnetic resonance imaging (fMRI) enables the construction ...
GitHub

bfly123/claude_code_bridge

Introduction: Multi-model collaboration avoids model bias, cognitive blind spots, and context limits. Unlike MCP or API-based approaches, ccb gives you a WYSIWYG split-pane terminal where every ...
IEEE

Fuzzy LASSO Logistic Regression: A Regularized Framework for Interpretable Binary Classification Under Uncertainty

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
Frontiers

Disease classification via interpretable machine learning based on multi-center routine coagulation test

Data from 11 hospitals were collected. An unsupervised clustering model was used to extract classification patterns, and clinical experts assigned disease labels. Multiple machine learning models, ...