Zobrazeno 1 - 10
of 468
pro vyhledávání: '"Graph convolution networks"'
Autor:
Wang, Xing a, b, Wang, Xiaojun a, b, Huang, Faliang c, Zou, Fumin d, Liao, Lyuchao d, Zeng, Ruihao e, ⁎
Publikováno v:
In Neurocomputing 7 March 2025 621
Publikováno v:
In Information Sciences March 2025 694
Autor:
Ben Cohen, Gil a, b, ⁎, Yaacov, Adar a, b, Ben Zvi, Yishai a, b, Loutati, Ranel a, b, Lishinsky, Natan a, b, Landau, Jakob a, b, Hope, Tom d, Popovzter, Aron c, Rosenberg, Shai a, b
Publikováno v:
In Computers in Biology and Medicine February 2025 185
Publikováno v:
Mathematics, Vol 12, Iss 23, p 3689 (2024)
Knowledge graph embedding has been identified as an effective method for node-level classification tasks in directed graphs, the objective of which is to ensure that nodes of different categories are embedded as far apart as possible in the feature s
Externí odkaz:
https://doaj.org/article/98a01b4c5fa54caeb1f9c95f54adb71b
Autor:
Huang, Aiping a, Lu, Jielong b, c, Wu, Zhihao b, c, Chen, Zhaoliang b, c, Chen, Yuhong b, c, Wang, Shiping b, c, Zhang, Hehong b, c, ⁎
Publikováno v:
In Information Sciences August 2024 677
Autor:
Aminollah Khormali, Jiann-Shiun Yuan
Publikováno v:
IEEE Access, Vol 12, Pp 58114-58127 (2024)
Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with u
Externí odkaz:
https://doaj.org/article/c4497c31ee284f70a112879294805db6
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 1198-1209 (2024)
Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementi
Externí odkaz:
https://doaj.org/article/6ac3d4f7b74e4f718fec58ae58a595b6
Publikováno v:
IEEE Access, Vol 12, Pp 39505-39516 (2024)
This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs). HAR has enjoyed increased attention due to advances in deep learning, increased data ava
Externí odkaz:
https://doaj.org/article/a08ae295387546ef8f46b8eccdce719e
Publikováno v:
Future Internet, Vol 16, Iss 9, p 318 (2024)
Real-world problems often exhibit complex relationships and dependencies, which can be effectively captured by graph learning systems. Graph attention networks (GATs) have emerged as a powerful and versatile framework in this direction, inspiring num
Externí odkaz:
https://doaj.org/article/a04281b1da2c44d1b49585437e92286d
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 1, Pp 111-128 (2023)
Abstract Analyzing highly individual-specific genomic data to understand genetic interactions in cancer development is still challenging, with significant implications for the discovery of individual biomarkers as well as personalized medicine. With
Externí odkaz:
https://doaj.org/article/6d1c5350994e4391bed26a92559b9f5b