Anti-personnel mines continue to maim and kill even after conflicts end — and it is mainly civilians who suffer the horrific ...

Abstract: Community detection is capable of uncovering the inherent structure and functional organization within complex networks by analyzing multi-scale topological features. According to the brief ...

Introduction The substantial case detection gap in the field of child tuberculosis (TB) disease is largely driven by inadequate diagnostic tools and approaches. Chest radiographs (CXRs) remain a key ...

Background: Wearable devices can capture 1-lead ECGs but are primarily used for detecting rhythm disorders. AI deployed on these ECGs could enable scalable detection of structural heart diseases (SHDs ...

We are a group of students working on graph algorithms, and we would like to contribute by implementing the Louvain community detection algorithm in JGraphT for detecting communities in large networks ...

Abstract: Multilayer networks, as an important branch of network science, have become a powerful tool for revealing and analyzing the internal structures of complex systems. Within these networks, ...

The art of finding patterns or communities plays a central role in the analysis of structured data such as networks. Community detection in graphs has become a field on its own. Real-world networks, ...

Introduction People identified as higher risk by a machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation [FIND-AF]) are at increased risk of ...

Meta is taking a page straight out of X's playbook on fact-checking. Meta said Thursday it'll use the open-source algorithm of X for its new community notes model. Its moderation overhaul, announced ...