Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Gengzhong ZHENG"'
Autor:
Muhammad Suhail Shaikh, Gengzhong Zheng, Chang Wang, Chunwu Wang, Xiaoqing Dong, Konstantinos Zervoudakis
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract Anxiety is an important issue that affects their academic performance, mental health, and overall educational journey. To address this issue, it is important to accurately assess anxiety levels and provide evidence-based techniques. However,
Externí odkaz:
https://doaj.org/article/80acfb4f9ca743899577c715d8aa970b
Autor:
Naveed Ur Rehman Junejo, Qingsheng Huang, Xiaoqing Dong, Chang Wang, Adnan Zeb, Mahammad Humayoo, Gengzhong Zheng
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract A variety of reasons have made it more difficult for educators and tutors to anticipate students’ performance. Numerous researchers have used various predictive models to identify students who may be at-risk of dropping out early. Addition
Externí odkaz:
https://doaj.org/article/c825f258363f48f38cd3374ec92c61a1
Publikováno v:
Heliyon, Vol 10, Iss 19, Pp e38555- (2024)
Transmission line (TL) parameters, particularly capacitance, are important for ensuring the efficient and reliable operation of power systems. As power networks become increasingly complex, accurately determining TL parameters faces certain challenge
Externí odkaz:
https://doaj.org/article/0c41b33db7614a19b83c532c555bb211
Publikováno v:
Axioms, Vol 13, Iss 11, p 774 (2024)
This paper introduces the Group Forward–Backward Orthogonal Matching Pursuit (Group-FoBa-OMP) algorithm, a novel approach for sparse feature selection. The core innovations of this algorithm include (1) an integrated backward elimination process to
Externí odkaz:
https://doaj.org/article/8829313e99c244e5a881f1e6793ff184
Publikováno v:
Symmetry, Vol 16, Iss 10, p 1312 (2024)
In this paper, we introduce an efficient and effective algorithm for Graph-based Semi-Supervised Learning (GSSL). Unlike other GSSL methods, our proposed algorithm achieves efficiency by constructing a bipartite graph, which connects a small number o
Externí odkaz:
https://doaj.org/article/acf8a5eec27b481db8e569a688d35e5f
Publikováno v:
Mathematics, Vol 12, Iss 17, p 2793 (2024)
This paper discusses a reduction in the optimal time due to the presence of input redundancy in time-optimal control problems. By introducing a non-idle channel to represent an active input channel, we establish the necessary and sufficient condition
Externí odkaz:
https://doaj.org/article/23ad5d4b1e9e459083e04ab5f920cc41
Publikováno v:
Mathematics, Vol 12, Iss 11, p 1620 (2024)
Nowadays, cluster analyses are widely used in mental health research to categorize student stress levels. However, conventional clustering methods experience challenges with large datasets and complex issues, such as converging to local optima and se
Externí odkaz:
https://doaj.org/article/ccd8afd09abe4eaea77300d7c2ecb0f0
Publikováno v:
IEEE Access, Vol 11, Pp 16526-16532 (2023)
Human action recognition methods based on skeleton data have been widely studied owing to their strong robustness to illumination and complex backgrounds. Existing methods have achieved good recognition results; however, they have certain challenges,
Externí odkaz:
https://doaj.org/article/d9f63bb63b1f4918883294d421aba59a
Publikováno v:
IEEE Access, Vol 8, Pp 10924-10932 (2020)
In the problem of causal discovery, conditional independence (CI) tests are generally used to detect the causal relationships among observed data. Due to the curse of dimensionality and the limitation of causal direction learning based on V-structure
Externí odkaz:
https://doaj.org/article/72dd5d23b2464f5fb53a99ecda76251b
Publikováno v:
Tongxin xuebao, Vol 40, Pp 124-135 (2019)
Aiming at the problem that it is difficult to allocate spectrum resources to secondary users efficiently in cognitive heterogeneous wireless networks with heterogeneous spectrum attributes,dynamic channel conditions and diverse service requirements,a
Externí odkaz:
https://doaj.org/article/a2ddfd219e444c079f5e057b7c2e7061