Zobrazeno 1 - 10
of 289
pro vyhledávání: '"Shuang Wang"'
Publikováno v:
Frontiers in Psychiatry, Vol 13 (2022)
ObjectiveAutism Spectrum Disorder (ASD) is a serious neurodevelopmental disorder that has become the leading cause of disability in children. Artificial intelligence (AI) is a potential solution to this issue. This study objectively analyzes the glob
Externí odkaz:
https://doaj.org/article/9cb6962ff3fb40b8afa24fd395b72ef4
Autor:
Shuang Wang, Amin Beheshti, Yufei Wang, Jianchao Lu, Quan Z. Sheng, Stephen Elbourn, Hamid Alinejad-Rokny
Publikováno v:
Algorithms, Vol 16, Iss 3, p 158 (2023)
Instructors face significant time and effort constraints when grading students’ assessments on a large scale. Clustering similar assessments is a unique and effective technique that has the potential to significantly reduce the workload of instruct
Externí odkaz:
https://doaj.org/article/6f3cba7e1fa2435bb281dc1d67166cb5
Publikováno v:
IEEE Robotics and Automation Letters. 8:17-24
Publikováno v:
Frontiers in Neurorobotics. 17
Speech emotion recognition is challenging due to the subjectivity and ambiguity of emotion. In recent years, multimodal methods for speech emotion recognition have achieved promising results. However, due to the heterogeneity of data from different m
Publikováno v:
IEEE Robotics and Automation Letters. 7:11609-11616
Publikováno v:
Acta Neurologica Scandinavica. 146:732-742
Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, inf
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 42:5069-5083
Slime mould algorithm (SMA) is a new metaheuristic algorithm proposed in 2020, which has attracted extensive attention from scholars. Similar to other optimization algorithms, SMA also has the drawbacks of slow convergence rate and being trapped in l
Autor:
Wenzhu Li, Shuang Wang
Publikováno v:
Neural Computing and Applications. 34:10355-10374
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, pp.5210516. ⟨10.1109/TGRS.2021.3099840⟩
IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, pp.5210516. ⟨10.1109/TGRS.2021.3099840⟩
International audience; Deep learning works normally in PolSAR image classification because the complex terrain scattering characteristic results in large intraclass differences and high interclass similarity. Deep metric learning (DML) aims to make
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, pp.5216719. ⟨10.1109/TGRS.2021.3125323⟩
IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, pp.5216719. ⟨10.1109/TGRS.2021.3125323⟩
International audience; Target decomposition features are the cornerstone of subsequent analyses for PolSAR images. Generally, adopting single or several decomposition algorithms limits the representation ability for original terrain characteristics.