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pro vyhledávání: '"Sun, Jingzhang"'
Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion-based detection methods generally overlook the correlation between features. And mere concatenation of fe
Externí odkaz:
http://arxiv.org/abs/2404.16362
Combining Graph neural networks (GNNs) with contrastive learning for anomaly detection has drawn rising attention recently. Existing graph contrastive anomaly detection (GCAD) methods have primarily focused on improving detection capability through g
Externí odkaz:
http://arxiv.org/abs/2305.02496
Anomaly detection is defined as discovering patterns that do not conform to the expected behavior. Previously, anomaly detection was mostly conducted using traditional shallow learning techniques, but with little improvement. As the emergence of grap
Externí odkaz:
http://arxiv.org/abs/2212.05478
Autor:
Du, Yu1,2 (AUTHOR), Sun, Jingzhang1,3 (AUTHOR), Li, Chien-Ying4,5 (AUTHOR), Yang, Bang-Hung4,5 (AUTHOR), Wu, Tung-Hsin4 (AUTHOR), Mok, Greta S. P.1,2 (AUTHOR) gretamok@um.edu.mo
Publikováno v:
EJNMMI Physics. 10/2/2024, Vol. 11 Issue 1, p1-13. 13p.
Publikováno v:
In Computers & Security February 2024 137
Akademický článek
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The image noise level and resolution of SPECT images are relatively poor attributed to the limited number of detected counts and various physical degradation factors during acquisitions. This study aims to apply and evaluate the use of generative adv
Externí odkaz:
http://arxiv.org/abs/1907.11944
Publikováno v:
In Journal of Nuclear Cardiology June 2023 30(3):1022-1037
Autor:
Sun, Jingzhang, Jiang, Han, Du, Yu, Li, Chien-Ying, Wu, Tung-Hsin, Liu, Yi-Hwa, Yang, Bang-Hung **, Mok, Greta S.P. *
Publikováno v:
In Journal of Nuclear Cardiology June 2023 30(3):970-985
Publikováno v:
Mathematics (2227-7390); Oct2024, Vol. 12 Issue 19, p3093, 18p