Results 31 to 40 of about 13,926 (236)

Characterizing and Understanding GCNs on GPU

IEEE Computer Architecture Letters, 2020
To Appear in IEEE Computer Architecture ...
Mingyu Yan, Zhaodong Chen 0001, Lei Deng 0003, Xiaochun Ye, Zhimin Zhang 0004, Dongrui Fan, Yuan Xie 0001   +6 more
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MSFR-GCN: A Multi-Scale Feature Reconstruction Graph Convolutional Network for EEG Emotion and Cognition Recognition

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
Graph Convolutional Network (GCN) excels at EEG recognition by capturing brain connections, but previous studies neglect the important EEG feature itself.
Deng Pan, Haohao Zheng, Feifan Xu, Yu Ouyang, Zhe Jia, Chu Wang, Hong Zeng   +6 more
doaj   +1 more source

Comparative analysis of the EGFR, HER2, c-MYC, and MET variations in colorectal cancer determined by three different measures: gene copy number gain, amplification status and the 2013 ASCO/CAP guideline criterion for HER2 testing of breast cancer

Journal of Translational Medicine, 2017
Background The purpose of this study was to explore gene copy number (GCN) variation of EGFR, HER2, c-MYC, and MET in patients with primary colorectal cancer (CRC). Methods Dual-colour silver-enhanced in situ hybridization was performed in tissue samples
Yoonjin Kwak, Sumi Yun, Soo Kyung Nam, An Na Seo, Kyu Sang Lee, Eun Shin, Heung-Kwon Oh, Duck Woo Kim, Sung Bum Kang, Woo Ho Kim, Hye Seung Lee   +10 more
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Identifying Experts in Community Question Answering Website Based on Graph Convolutional Neural Network

IEEE Access, 2020
High-quality answers, usually given by experts, play an important role in community question answering (CQA) websites. Therefore, experts in these websites are defined as those who provide high-quality answers.
Chen Liu, Yuchen Hao, Wei Shan, Zhihong Dai   +3 more
doaj   +1 more source

U-Net Greenhouse Sweet Cherry Image Segmentation Method Integrating PDE Plant Temporal Image Contrastive Learning and GCN Skip Connections

智慧农业
[Objective]Within the field of plant phenotyping feature extraction, the accurate delineation of small targets boundaries and the adequate recovery of spatial details during upsampling operations have long been recognized as significant obstacles ...
HU Lingyan, GUO Ruiya, GUO Zhanjun, XU Guohui, GAI Rongli, WANG Zumin, ZHANG Yumeng, JU Bowen, NIE Xiaoyu   +8 more
doaj   +1 more source

Human Motion Prediction Based on Space-Time-Separable Graph Convolutional Network

IEEE Access, 2023
Human motion prediction is a popular method to predict future motion sequences based on past sequences, which is widely used in human-computer interaction.
Rui Li, Duo He, Shiqiang Yang, An Yan, Xin Zeng, Dexin Li   +5 more
doaj   +1 more source

Graph Neural Networks for Malware Classification: Comparing Graph-Structured and Sequence-Based Representations

Iraqi Journal for Computers and Informatics
Malware detection is one of the most important cybersecurity issues because the traditional signature-based methods cannot resist polymorphic threats and obfuscated ones.
Meer Twana Qadir, Saman Mirza Abdullah
doaj   +1 more source

Alkaline hydrogen evolution on CO-M@gCN dual-atom catalysts: insights from first-principles calculations

Tạp chí Khoa học và Công nghệ
Dual-atom catalysts (DACs) offer unique opportunities for enhancing electrocatalytic performance through synergistic metal interactions. Herein, we employ spin-polarized density functional theory to investigate Co-M DACs (M = Cr, Mn, Fe, Co, Ni, Cu, Pd ...
Tran Thi Kim Cuc, Thuy T. T. Duong, Ho Van Binh, Tien B. Tran, Thi H. Ho, Bich-Tram Truong Le, Dang Kim Hoang, Viet Q. Bui   +7 more
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DETECTION AND CLASSIFICATION OF RED BLOOD CELLS ABNORMALITY USING FASTER R-CNN AND GRAPH CONVOLUTIONAL NETWORKS

JUTI: Jurnal Ilmiah Teknologi Informasi, 2022
Research in medical imagery field such as analysis of Red Blood Cells (RBCs) abnormalities can be used to assist laboratory’s in determining further medical actions.
Amirullah Andi Bramantya, Chastine Fatichah, Nanik Suciati   +2 more
doaj   +1 more source

SVD-GCN

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022
With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-based methods rigorously stick to a common GCN learning paradigm and suffer from two limitations: (1) the limited scalability due to the high computational cost and slow ...
Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine   +2 more
openaire   +2 more sources