Results 31 to 40 of about 22,751 (263)
, 2019 The official implementation of PU-GCN https://sites.google.com/kaust.edu.sa ...
Li, Guohao +4 more
core +2 more sources
Heliyon, 2023 The extensive consumption of fossil fuels increases CO2 concentration in the atmosphere, resulting in serious global warming problems. Meanwhile, the problem of water contamination by organic substances is another significant global challenge.
Chiing-Chang Chen +7 more
doaj +1 more source
Development of photocatalysis‐membrane separation reactor systems for aqueous pollutant removal
PhotoMat, EarlyView., 2023 Abstract Background In our rapidly expanding society, the demand for clean water has steadily emerged as one of the most critical issues, promoting the development of numerous water treatment strategies. Aims Coupling photocatalysis and membrane separation technology provides an energy saving and environment‐friendly as well as sustainable method for ...
Junyang Zhang +3 more
wiley +1 more source
Spammer detection technology of social network based on graph convolution network
网络与信息安全学报, 2018 In social networks,Spammer send advertisements that are useless to recipients without the recipient's permission,seriously threatening the information security of normal users and the credit system of social networking sites.In order to solve problems of
Qiang QU,Hongtao YU,Ruiyang HUANG
doaj +1 more source
Frontiers in Sensors, 2022 In this study a nanocomposite of graphitic carbon nitride-silver polyvinylpyrrolidone (gCN-AgPVP) was fabricated for the electrochemical detection of paracetamol (PAR).
N. Mekgoe, N. Mabuba, K. Pillay
doaj +1 more source
A Simple Yet Effective SVD-GCN for Directed Graphs [PDF]
, 2022 In this paper, we propose a simple yet effective graph neural network for directed graphs (digraph) based on the classic Singular Value Decomposition (SVD), named SVD-GCN.
Lin, Lequan +3 more
core +1 more source
Characterizing and Understanding GCNs on GPU
IEEE Computer Architecture Letters, 2020 To Appear in IEEE Computer Architecture ...
Mingyu Yan +6 more
openaire +5 more sources
IEEE Access, 2022 Graph Convolutional Network (GCN) has achieved significant success in many graph representation learning tasks. GCN usually learns graph representations by performing Neighbor Aggregation (NA) and Feature Transformation (FT) operations.
Dezhi Sun, Man Hu, Zhenyu Li
doaj +1 more source
Bridging the Gap: Elusive Explosions in the Local Universe [PDF]
, 2011 For centuries, we have known that our dynamic universe is adorned by cosmic fireworks: energetic and ephemeral beacons of light from a single star that are a million (nova) to a billion (supernova) times brighter than our sun. However, it had been an age-
Kasliwal, Mansi Manoj
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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 +6 more
doaj +1 more source