Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Chunning, Wang"'
Autor:
Chunning Wang1, Fengfeng Wang2,3 wangfengfeng933@126.com, Mahmood, Muhammad Aamir4, Ali, Qamar5
Publikováno v:
Polish Journal of Environmental Studies. 2024, Vol. 33 Issue 4, p3887-3902. 16p.
Publikováno v:
Mathematics, Vol 11, Iss 14, p 3256 (2023)
The individuals of real-world networks participate in various types of connections, each forming a layer in multiplex networks. Link prediction is an important problem in multiplex network analysis owing to its wide range of practical applications, s
Externí odkaz:
https://doaj.org/article/002b293a3369403589884bc990b7cac8
Publikováno v:
IEEE Access, Vol 8, Pp 62458-62470 (2020)
Multiple-instance learning (MIL) can solve supervised learning tasks, where only a bag of multiple instances is labeled, instead of a single instance. It is considerably important to develop effective and efficient MIL algorithms, because real-world
Externí odkaz:
https://doaj.org/article/5b3fe4e2d7d049cba8f8f118fa57148b
Autor:
Chunning Wang, Bingyi Jing
Publikováno v:
Statistics and Its Interface. 15:323-334
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33a9bf6a0865f6a7a3448ab96e85c705
https://doi.org/10.4310/21-sii690
https://doi.org/10.4310/21-sii690
Publikováno v:
Communications in Statistics - Simulation and Computation. 52:68-83
Community detection is an effective exploration technique for analyzing networks. Most of the network data not only describes the connections of network nodes but also describes the properties of t...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::424627cc6e986438da3c1db9a2502f0c
https://doi.org/10.1080/03610918.2020.1847291
https://doi.org/10.1080/03610918.2020.1847291
Publikováno v:
International Journal of Modern Physics C. 33
A multilayer network is a useful representation for real-world complex systems in which multiple types of connections are formed between entities. Connections of the same type form a specific layer of the network. We propose a novel framework for pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::049fa7b0099e5c87d3706f717295683c
https://doi.org/10.1142/s0129183122500036
https://doi.org/10.1142/s0129183122500036
Publikováno v:
Computational Statistics. 35:69-94
Community detection is one of the main research topics in network analysis. Most network data reveal a certain structural relationship between nodes and provide attributes describing them. Utilizing available node attributes can help uncover latent c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d43742f120b88ec71dc3b8fe0010831
https://doi.org/10.1007/s00180-019-00909-8
https://doi.org/10.1007/s00180-019-00909-8
Publikováno v:
Statistics and Its Interface. 12:123-133
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb210ad37bc0522a1067b4fc67eb3375
https://doi.org/10.4310/sii.2019.v12.n1.a11
https://doi.org/10.4310/sii.2019.v12.n1.a11
Libraries are widely used by government, universities, research institutes, and the public since they are storing and managing intellectual assets. The library information directly stored in libraries and about the people interaction with libraries c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::753a8f7a9843a21f75ca421cfca03143
https://doi.org/10.4018/978-1-7998-8051-6.ch010
https://doi.org/10.4018/978-1-7998-8051-6.ch010
Publikováno v:
2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE).
Aiming at the vibration signals are nonlinear and time-varying since the no-load closing of a transformer, a fault feature extraction method that combines local mean decomposition (LMD) and Hilbert marginal spectrum energy entropy is proposed. Severa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd3a9bccbe8bc18640ca10bc33cb95e9
https://doi.org/10.1109/icmcce51767.2020.00091
https://doi.org/10.1109/icmcce51767.2020.00091