graph neural networks

Heatwave-Gated Adaptive Spatiotemporal Learning for Short-Term Load Forecasting of Light-Industrial Multiusers

Findings indicate that incorporating extreme meteorological information into spatiotemporal forecasting frameworks is an effective way to enhance accuracy and robustness in high-temperature, strongly nonstationary conditions.

Graph-radiomic learning (GrRAiL) descriptor to characterize imaging heterogeneity in confounding tumor pathologies

Abstract Distinguishing benign confounding pathologies from malignant neoplasms on routine imaging remains a significant challenge in solid tumors. Radiomics approaches attempt to capture lesion heterogeneity on clinical imaging (CT,MRI), but typically aggregate feature values across the region of interest (ROI), losin...

Meta-learning enhanced human–robot collaboration in visual communication design instruction

Visual icon-based human–robot interaction faces challenges of low efficiency and asynchronous responses in educational settings. This study proposes the Adaptive Graph-based Visual Teaching and Robot Interaction (AGVTRI) model for optimizing robot teaching networks. Integrating a Vision Transformer, graph neural networ...

Code and data for HDSTAR-GNN: A heterogeneous dynamic spatiotemporal graph neural network model for quantifying spatially cascading impacts of extreme precipitation on economic growth

Dynamic spatiotemporal autoregressive models explicitly represent spatial dependency and temporal dynamics underlying an evolving spatial system, providing a natural framework for investigating how climate shocks propagate, persist, and amplify. Yet their application to fine-resolution climate-impact assessment is crit...

A Public Canvas Course - Cybertraining for Chemical Data scientists

We present a free public Canvas course connects machine learning concepts with chemical applications, including Python examples and quizzes. The course is available at https://notredamecatalog.instructure.com/courses/21946~1257. The course also includes three animation videos that introduce neural networks, random fore...

Deep Learning of EEG Signals for Brain Function and Injury: A Systematic Review

While deep learning has advanced EEG signal analysis, further research is essential to build larger datasets and improve multimodal models for broader applications in Brain Function and Injury assessment.

Feature representation learning and combinatorial optimization method of visual design elements

Aiming at the challenge of automatic combinatorial optimization of visual design elements, this paper proposes a hybrid framework that combines multi-modal feature representation learning and hierarchical combinatorial optimization. By constructing a dual-channel graph neural network (GNN) encoder, the visual content f...

Design of multimodal collaborative representation learning algorithm in English translation tasks

Experiments show that MCRA-Net outperforms baseline models on BLEU, METEOR, Culture-BLEU, and TERM-F1, especially in translating culture-specific and technical terms, especially in translating culture-specific and technical terms.

Analysis of nonlinear Schrödinger equations with anticubic nonlinearity in optical fiber systems via physics informed neural network (PINN)

The higher order nonlinear phenomena such as the anticubic nonlinearity have a major influence on the evolution, stability and localization of optical pulses and play a significant role in the evolution of nonlinear optical waves in dispersive media. Therefore, effective numerical approaches for solving these nonlinear...

SpaMet-GNN: a donor-aware graph neural network framework for identifying reproducible spatial expression programs in gastrointestinal cancers

Graph Theory Applications In Smart Transportation Network Optimization

With the growing trend in urban development along with advanced intelligent transportation systems, there is a demand for advanced solutions for the management of the transport network. Since transportation networks can easily be modeled using graphs, graph theory is a useful mathematical tool for optimizing complex ne...

Construction of multimodal oracle bone semantic reasoning and knowledge graphs in archaeology under artificial intelligence technology

To address the difficulty of establishing continuous associations among oracle bone script images, transcriptions, and archaeological knowledge, this study developed the Oracle-Artifact Multimodal Reasoning (OAMR) multimodal semantic reasoning framework. Vision Transformer (ViT)-B/16 and Bidirectional Encoder Represent...