Results 111 to 120 of about 5,438,478 (196)

STr-GCN: Dual Spatial Graph Convolutional Network and Transformer Graph Encoder for 3D Hand Gesture Recognition

, 2023
International audienceSkeleton-based hand gesture recognition is a challenging task that sparked a lot of attention in recent years, especially with the rise of Graph Neural Networks.
Wannous, Hazem, Rabeh, Wael, Rabah, Wael, Slama, Rim   +3 more
core   +1 more source

AI‐enabled engineering of hesperidin/ursodeoxycholic acid nanomedicine for synergistic treatment of drug‐induced liver injury

Smart Molecules, EarlyView.
An AI‐driven framework integrating disease‐network analysis, direction‐aware molecular screening, and carrier‐free co‐assembly engineering enables nanomedicine design. The dual‐constraint deep learning model predicts API–target affinity and regulatory direction to prioritize complementary APIs.
Kai Xiao, Meng Fan, Haoyu Zheng, Boyuan Gu, Shaowen Wang, Ziping Wu, Shiming Zhang, Xin Zhao, Wensheng Zhang, Quanxin Ning, Sigen A, Chao Yang, Huayi Sun, Ren Mo, Dan Shao   +14 more
wiley   +1 more source

C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading [PDF]

Graph-based learning approaches, due to their ability to encode tissue/organ structure information, are increasingly favored for grading colorectal cancer histology images.
Paul, Sudipta, Lund, Amanda W., Yener, Bulent   +2 more
core  

Graph neural network‐based attack prediction for communication‐based train control systems

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract The Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions.
Junyi Zhao, Tao Tang, Bing Bu, Qichang Li   +3 more
wiley   +1 more source

Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics.

PLoS Computational Biology
Drug-induced liver injury is a leading cause of high attrition rates for both candidate drugs and marketed medications. Previous in silico models may not effectively utilize biological drug property information and often lack robust model validation.
Tong Xiao, Ying Liu, Kaimiao Hu, Kaimin Guo, Mengying Zhang, TingTing Wang, Weihua Lei, Wenjia Wang, Shuiping Zhou, Yunhui Hu, Ran Su   +10 more
doaj   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

TFC-GCN: Lightweight Temporal Feature Cross-Extraction Graph Convolutional Network for Skeleton-Based Action Recognition

, 2023
For skeleton-based action recognition, graph convolutional networks (GCN) have absolute advantages. Existing state-of-the-art (SOTA) methods tended to focus on extracting and identifying features from all bones and joints.
Hongmin Deng, Kaixuan Wang
core   +1 more source

Short‐Term Multi‐Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention‐GCN‐LSTM

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng   +4 more
wiley   +1 more source

ANPGT: Towards Adaptive Node Property Extraction and Integration

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Graph transformers (GTs) with elaborate positional/structural encodings (PEs/SEs) have excelled in graph representation learning, especially in graph‐level tasks. However, their potential in large‐scale node classification remains untapped for several reasons: (i) Current PEs/SEs are insufficient in modelling large‐scale real‐world graphs ...
Qin Chen, Yuanyi Ren, Guojie Song, Liang Wang, Bo Zheng   +4 more
wiley   +1 more source

A simple and effective convolutional operator for node classification without features by graph convolutional networks.

PLoS ONE
Graph neural networks (GNNs), with their ability to incorporate node features into graph learning, have achieved impressive performance in many graph analysis tasks.
Qingju Jiao, Han Zhang, Jingwen Wu, Nan Wang, Guoying Liu, Yongge Liu   +5 more
doaj   +1 more source