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pro vyhledávání: '"Farag Aly"'
Existing multimodal-based human action recognition approaches are either computationally expensive, which limits their applicability in real-time scenarios, or fail to exploit the spatial temporal information of multiple data modalities. In this work
Externí odkaz:
http://arxiv.org/abs/2408.05421
Recent achievements in machine learning (Ml) have had a significant impact on various fields, including climate science. Climate modeling is very important and plays a crucial role in shaping the decisions of governments and individuals in mitigating
Externí odkaz:
http://arxiv.org/abs/2311.09413
Given that approximately half of science, technology, engineering, and mathematics (STEM) undergraduate students in U.S. colleges and universities leave by the end of the first year [15], it is crucial to improve the quality of classroom environments
Externí odkaz:
http://arxiv.org/abs/2307.09465
In this paper, we propose a novel technique for measuring behavioral engagement through students' actions recognition. The proposed approach recognizes student actions then predicts the student behavioral engagement level. For student action recognit
Externí odkaz:
http://arxiv.org/abs/2307.09420
Autor:
Abdelkawy, Ahmed, Farag, Aly, Alkabbany, Islam, Ali, Asem, Foreman, Chris, Tretter, Thomas, Hindy, Nicholas
Publikováno v:
In Pattern Recognition Letters October 2024 186:337-344
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2005, Iss 13, p 829185 (2005)
A new algorithm for segmenting a multimodal grey-scale image is proposed. The image is described as a sample of a joint Gibbs random field of region labels and grey levels. To initialize the model, a mixed multimodal empirical grey-level distribution
Externí odkaz:
https://doaj.org/article/d16ab2ec87ac468b8645122bd48a5b07
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