Zobrazeno 1 - 10
of 246
pro vyhledávání: '"Fang Weili"'
Publikováno v:
National Science Open, Vol 2 (2022)
Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based
Externí odkaz:
https://doaj.org/article/c89caa92abac4a3f9ff3eb2195313f6b
Explainable artificial intelligence has received limited attention in construction despite its growing importance in various other industrial sectors. In this paper, we provide a narrative review of XAI to raise awareness about its potential in const
Externí odkaz:
http://arxiv.org/abs/2211.06579
Explainable artificial intelligence is an emerging and evolving concept. Its impact on construction, though yet to be realised, will be profound in the foreseeable future. Still, XAI has received limited attention in construction. As a result, no eva
Externí odkaz:
http://arxiv.org/abs/2211.06561
Publikováno v:
In International Journal of Disaster Risk Reduction 15 October 2024 113
Publikováno v:
In Advanced Engineering Informatics October 2024 62 Part A
Autor:
Wu, Dongrui, Xu, Jiaxin, Fang, Weili, Zhang, Yi, Yang, Liuqing, Xu, Xiaodong, Luo, Hanbin, Yu, Xiang
Publikováno v:
J. Xu, W. Fang, Y. Zhang, L. Yang, X. Xu, H. Luo and X. Yu, "Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review," National Science Open, 2(1):20220023, 2023
Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based
Externí odkaz:
http://arxiv.org/abs/2102.02729
Publikováno v:
In Reliability Engineering and System Safety January 2024 241
Publikováno v:
Pattern Recognition Letters, 142:11-19, 2021
Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervise
Externí odkaz:
http://arxiv.org/abs/2003.07658
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Akademický článek
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