As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Semi-Supervised Learning of Multi-Scale Graph Neural Networks for Industrial Process Fault Diagnosis
Abstract: The scarcity of labeled data is a critical challenge in industrial process multi-scale modeling, as learning reliable models from limited labeled data and large-scale unlabeled data is ...
ABSTRACT: Foot-and-Mouth Disease (FMD) remains a critical threat to global livestock industries, causing severe economic losses and trade restrictions. This paper proposes a novel application of ...
ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...
Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...
Abstract: Multi-view learning is a promising research field that aims to enhance learning performance by integrating information from diverse data perspectives. Due to the increasing interest in graph ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results