Zobrazeno 1 - 10
of 350
pro vyhledávání: '"Chongjun Wang"'
Publikováno v:
IEEE Access, Vol 9, Pp 88301-88312 (2021)
Heterogeneous Information Networks (HINs) are ubiquitous in our daily life, as they can describe complex interactions among various types of objects. Heterogeneous information network embedding aims to project the network elements of HINs into low-di
Externí odkaz:
https://doaj.org/article/62f8d5243574466ab3b6bfdafccfe2bd
Autor:
Junxiong Yin, Chuanyong Yu, Hongxing Liu, Mingyang Du, Feng Sun, Cheng Yu, Lixia Wei, Chongjun Wang, Xiaoshan Wang
Publikováno v:
BMC Cardiovascular Disorders, Vol 20, Iss 1, Pp 1-9 (2020)
Abstract Background Several models have been developed to predict asymptomatic carotid stenosis (ACS), however these models did not pay much attention to people with lower level of stenosis (
Externí odkaz:
https://doaj.org/article/0f2128ffca5745ad87aa8b7824d63742
Autor:
Dongliang Ma, Zhibiao Zhang, Shunye Zhang, Zhongchao Wang, Gang Zhang, Chongjun Wang, Jicheng Xi
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 9 (2022)
ObjectiveTo analyze treatment strategies, prognosis, and related risk factors of patients with postinfarction ventricular septal rupture, as well as the impact of timing of surgical intervention.MethodsA total of 23 patients diagnosed with postinfarc
Externí odkaz:
https://doaj.org/article/c74956f712434364a9f7444f95477d7d
Autor:
Shuwei Qian, Chongjun Wang
Publikováno v:
Neural Networks. 162:443-455
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbd1c4cb953287f837de6933225d887a
https://doi.org/10.1016/j.neunet.2023.03.003
https://doi.org/10.1016/j.neunet.2023.03.003
Publikováno v:
Neural Networks. 156:58-66
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in graph-related tasks. For graph classification task, an elaborated pooling operator is vital for learning graph-level representations. Most pooling operators derived from exist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::013d2df4027fa10e9dbd6f3de4462efd
https://doi.org/10.1016/j.neunet.2022.09.004
https://doi.org/10.1016/j.neunet.2022.09.004
Publikováno v:
Results in Physics, Vol 15, Iss , Pp - (2019)
The hierarchical and porous nanomaterials usually present excellently physicochemical functions. Here, we report the successful preparation of three different ZnSnO3 nanocubes using different surfactant by one-pot hydrothermal method. The as-prepared
Externí odkaz:
https://doaj.org/article/c19b35a8a43048ccac81ad3161fc36bd
Publikováno v:
Intelligent Data Analysis. 26:1539-1555
Graph Neural Networks (GNNs) have achieved remarkable success in graph-related tasks by combining node features and graph topology elegantly. Most GNNs assume that the networks are homophilous, which is not always true in the real world, i.e., struct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::acf7c1e2e58fe90e30e09be77b12544d
https://doi.org/10.3233/ida-216253
https://doi.org/10.3233/ida-216253
Publikováno v:
Intelligent Data Analysis. 26:1229-1245
Exploiting label correlation is crucially important in multi-label learning, where each instance is associated with multiple labels simultaneously. Multi-label learning is more complex than single-label learning for that the labels tend to be correla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aee6a0458eb442d84ac29f4e638fc8b5
https://doi.org/10.3233/ida-215946
https://doi.org/10.3233/ida-215946
Autor:
Chongjun WANG
Publikováno v:
大数据, Vol 3, Pp 91-103 (2017)
People from all social circles are concerned about the big data,because all of them think that big data is valuable.However,different people have different value expectation,all of which are the goals when implementing big data project.Multiple kinds
Externí odkaz:
https://doaj.org/article/391065fed82b4854ba65d524c5996451
Publikováno v:
Neurocomputing. 495:194-204
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f4f2475804ffad2d21b9e20cf5225c0
https://doi.org/10.1016/j.neucom.2022.01.064
https://doi.org/10.1016/j.neucom.2022.01.064