Zobrazeno 1 - 10
of 395
pro vyhledávání: '"Zhexue HUANG"'
Autor:
Guangyue Zhu, Shuping Wang, Guodong Zhang, Yu Zhang, Zhexue Huang, Xiaoshun Tan, Yuhui Chen, Hui Sun, Dongsheng Xu
Publikováno v:
Trials, Vol 25, Iss 1, Pp 1-12 (2024)
Abstract Background Numerous studies have validated the clinical effectiveness of electromagnetic pairing-associated stimulation. Building upon this foundation, we have developed a novel approach involving high-frequency magnetic paired-associated st
Externí odkaz:
https://doaj.org/article/992a832ad79b44f1a3257b9a90b04707
Publikováno v:
大数据, Vol 10, Pp 93-108 (2024)
A sequential implementation of Bootstrap sampling and Bagging ensemble learning is computationally inefficient and not scalable to build large Bagging ensemble models with a large number of component models.Inspired by distributed big data computing,
Externí odkaz:
https://doaj.org/article/c006d8b77c5b43cb9aeab66bfa0e0099
Autor:
Xiaoshun Tang, Zhexue Huang, Guangyue Zhu, Haoyuan Liang, Hui Sun, Yu Zhang, Yalin Tan, Minglong Cui, Haiyan Gong, Xijin Wang, Yu-Hui Chen
Publikováno v:
Frontiers in Aging Neuroscience, Vol 16 (2024)
BackgroundNon-invasive neuroregulation techniques have been demonstrated to improve certain motor symptoms in Parkinson’s disease (PD). However, the currently employed regulatory techniques primarily concentrate on stimulating single target points,
Externí odkaz:
https://doaj.org/article/0f6aa7e00acc4434830ecfbd35ca002a
Publikováno v:
Mathematics, Vol 10, Iss 1, p 39 (2021)
To provide more external knowledge for training self-supervised learning (SSL) algorithms, this paper proposes a maximum mean discrepancy-based SSL (MMD-SSL) algorithm, which trains a well-performing classifier by iteratively refining the classifier
Externí odkaz:
https://doaj.org/article/595288040ddf40fca6d0a8d0c01ad2b1
Publikováno v:
Big Data Mining and Analytics. 6:154-169
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f30b43216d0124dd53fbc44c11522bd
https://doi.org/10.26599/bdma.2022.9020014
https://doi.org/10.26599/bdma.2022.9020014
Publikováno v:
Journal of Big Data, Vol 6, Iss 1, Pp 1-28 (2019)
Abstract Data scientists need scalable methods to explore and clean big data before applying advanced data analysis and mining algorithms. In this paper, we propose the RSP-Explore method to enable data scientists to iteratively explore big data on s
Externí odkaz:
https://doaj.org/article/5200465654254b3ba3734531c8276d83
Autor:
Yulin He, Xuan Ye, Laizhong Cui, Philippe Fournier-Viger, Chengwen Luo, Joshua Zhexue Huang, Ponnuthurai Nagaratnam Suganthan
Publikováno v:
IEEE Transactions on Network Science and Engineering. 10:1283-1296
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0934e1f078e364fb5d26366359b17808
https://doi.org/10.1109/tnse.2022.3178740
https://doi.org/10.1109/tnse.2022.3178740
Autor:
Kuanishbay Sadatdiynov, Laizhong Cui, Lei Zhang, Joshua Zhexue Huang, Salman Salloum, Mohammad Sultan Mahmud
Publikováno v:
Digital Communications and Networks. 9:450-461
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::817f84c9d35d4d1730f4c2b4e8e63b8e
https://doi.org/10.1016/j.dcan.2022.03.003
https://doi.org/10.1016/j.dcan.2022.03.003
Publikováno v:
In Pattern Recognition April 2018 76:404-415
Publikováno v:
IEEE Transactions on Artificial Intelligence. 4:182-196
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31641ef442866fe9e23dcdf180b3b53c
https://doi.org/10.1109/tai.2022.3151724
https://doi.org/10.1109/tai.2022.3151724