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 ...

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...

Graph Neural Networks (GNNs) are reshaping AI by enhancing data interpretation and improving applications. Learn how GNNs are crucial in advancing machine learning models. Graph Neural Networks (GNNs) ...

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 ...

Abstract: Auto parts inventory management is an important link of the automobiles multi-value chain, which has a certain impact on the upstream procurement, downstream production and other links.

Abstract: Graph convolutional neural networks have demonstrated promising solutions for processing non-Euclidean data in tasks such as node classification. While existing graph convolution models aim ...

Abstract: Many modern classification problems involve data that live in high-dimensional spaces but exhibit strong low-dimensional structure. Motivated by the manifold hypothesis, this talk presents a ...