Advancements in biomedical technologies have significantly facilitated the diagnosis and monitoring of diseases. Nonetheless, traditional diagnostic ...

EMBL-EBI scientists and collaborators at Heidelberg University have developed CORNETO, a new computational tool that uses ...

This article studies the problem of computing zero-forcing sets (ZFS) in graphs and provides a machine-learning solution. Zero-forcing is a vertex coloring process to color the entire vertex set from ...

Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square ...

To address this maximization problem, we propose a graph reinforcement learning-based AECNN (GRL-AECNN) method, which outperforms existing works DROO-AECNN, GRL-BottleNet++ and GRL-DeepJSCC under ...

Conclusion: By innovatively integrating multi-source data and machine learning techniques, this study systematically analyzes the relationship between campus audiovisual environments and attention ...

We then aggregated them into a single dataset by converting them into a graph structure. Furthermore, we developed medicare fraud detection models using two approaches to reflect graph information, ...

Machine learning helps improve accuracy and efficiency of small-molecule calculations Microsoft researchers used deep learning to create new DFT model by Sam Lemonick, special to C&EN June 20, 2025 ...

Scientists at Massachusetts Institute of Technology have devised a way for large language models to keep learning on the fly—a step toward building AI that continually improves itself.

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing.