Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Cheng-Te Li"'
Publikováno v:
Frontiers in Big Data, Vol 7 (2024)
Time series forecasting is an essential tool across numerous domains, yet traditional models often falter when faced with unilateral boundary conditions, where data is systematically overestimated or underestimated. This paper introduces a novel appr
Externí odkaz:
https://doaj.org/article/9182265dace44e298c1e6d37d71173c3
Autor:
Ching-Chi Lee, Yuan-Pin Hung, Chih-Chia Hsieh, Ching-Yu Ho, Chiao-Ya Hsu, Cheng-Te Li, Wen-Chien Ko
Publikováno v:
BMC Infectious Diseases, Vol 23, Iss 1, Pp 1-12 (2023)
Abstract Background The development of scoring systems to predict the short-term mortality and the length of hospital stay (LOS) in patients with bacteraemia is essential to improve the quality of care and reduce the occupancy variance in the hospita
Externí odkaz:
https://doaj.org/article/16a53ab53cc34d1a9bcd5f648187093a
Publikováno v:
Frontiers in Big Data, Vol 6 (2023)
Externí odkaz:
https://doaj.org/article/7d91fe107a5c461ea8d00b102b61c3cd
Autor:
I-Chung Hsieh, Cheng-Te Li
Publikováno v:
IEEE Access, Vol 10, Pp 37506-37514 (2022)
The random walk process on network data is a widely-used approach for network representation learning. However, we argue that the sampling of node sequences and the subsampling for the Skip-gram’s contexts have two drawbacks. One is less possible t
Externí odkaz:
https://doaj.org/article/ea71e7bda40740e49b02db459e409ee4
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021)
Abstract Background Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, ap
Externí odkaz:
https://doaj.org/article/2773c540a21d4946ab826c421417c48e
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 22, Iss 1, Pp 1-1 (2022)
Externí odkaz:
https://doaj.org/article/98cbb718ffc24128b5d488a961fd24f1
Publikováno v:
Sensors, Vol 21, Iss 3, p 709 (2021)
Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has
Externí odkaz:
https://doaj.org/article/ad85eb1044fa4a77947d943d5ef08eb2
Autor:
Yi-Chun Chen, Cheng-Te Li
Publikováno v:
Applied Sciences, Vol 10, Iss 22, p 8003 (2020)
In the scenarios of location-based social networks (LBSN), the goal of location promotion is to find information propagators to promote a specific point-of-interest (POI). While existing studies mainly focus on accurately recommending POIs for users,
Externí odkaz:
https://doaj.org/article/3e32a2f56675477297097fd3d45a04f0
Autor:
Cheng-Te Li, Hong-Yu Lin
Publikováno v:
Applied Sciences, Vol 10, Iss 20, p 7214 (2020)
Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP). However, existing NRL methods neither properly identify neighbor nodes that should
Externí odkaz:
https://doaj.org/article/6f366aaa283f48bfab5b2e4307a00269
Autor:
Cheng-Te Li, Zi-Yun Zeng
Publikováno v:
Applied Sciences, Vol 10, Iss 14, p 4835 (2020)
Users pay increasing attention to their data privacy in online social networks, resulting in hiding personal information, such as profile attributes and social connections. While network representation learning (NRL) is widely effective in social net
Externí odkaz:
https://doaj.org/article/e8bf3862cc0a45a8bb24f49ee4a1812d