Results 51 to 60 of about 24,295 (253)

Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease Using EEG Data

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer’s disease (AD), remains a relatively unexplored ...
Dominik Klepl, Fei He, Min Wu, Daniel J. Blackburn, Ptolemaios Sarrigiannis   +4 more
doaj   +1 more source

Session-based Recommendation with Graph Neural Networks

, 2019
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations.
Tan, Tieniu, Tang, Yuyuan, Wang, Liang, Wu, Shu, Xie, Xing, Zhu, Yanqiao   +5 more
core   +1 more source

Dual GNNs: Graph Neural Network Learning with Limited Supervision

CoRR, 2021
Graph Neural Networks (GNNs) require a relatively large number of labeled nodes and a reliable/uncorrupted graph connectivity structure in order to obtain good performance on the semi-supervised node classification task. The performance of GNNs can degrade significantly as the number of labeled nodes decreases or the graph connectivity structure is ...
Abdullah Alchihabi, Yuhong Guo
openaire   +2 more sources

Engineering Strategies for Stable and Long‐Life Alkaline Zinc‐Based Flow Batteries

Advanced Functional Materials, EarlyView.
Alkaline zinc‐based flow batteries face persistent challenges from unstable zinc deposition, including dendrite growth, passivation, corrosion, and hydrogen evolution, which severely limit cycling stability. Current research addresses these issues through coordinated electrode structuring, electrolyte regulation, and membrane design to control zinc ...
Yuran Bai, Xiaoyu Huo, Xin Long, Enkang Fu, Xusong Gong, Lei Wei, Liang An   +6 more
wiley   +1 more source

A GNN routing module is all you need for LSTM Rainfall–Runoff models [PDF]

Hydrology and Earth System Sciences
Rainfall–Runoff (R–R) modeling is crucial for hydrological forecasting and water resource management, yet traditional deep learning approaches, such as Long Short-Term Memory (LSTM) networks, often overlook explicit runoff routing, leading to ...
H. Mosaffa, H. Mosaffa, F. Pappenberger, C. Prudhomme, M. Chantry, C. Rüdiger, H. Cloke, H. Cloke   +7 more
doaj   +1 more source

Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials

npj Computational Materials, 2021
Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining ...
Minyi Dai, Mehmet F. Demirel, Yingyu Liang, Jia-Mian Hu   +3 more
doaj   +1 more source

Prototipat d'una Graph Neural Network [PDF]

, 2019
Aquest TFG consisteix en la implementació de dos optimitzadors de "Routing Matrix" disenyats per a minimitzar la làtencia d'una xarxa mitjançant la Graph Neural Network (GNN) de RouteNet, desenvolupada per un grup de recerca de la FIB.This Final degree ...
Canyelles Ruiz, Albert
core  

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Research on Intermittent Hypoxia Training in Sports Based on Graph Neural Network

Applied Artificial Intelligence, 2023
To enhance the efficacy of intermittent hypoxia training in sports, this study presents an intelligent training model that utilizes a graph neural network.
Guolong Li, Haixia Li, Jiyong Lv
doaj   +1 more source