Zobrazeno 1 - 10
of 168 649
pro vyhledávání: '"detection accuracy"'
The rapid progress in machine learning models has significantly boosted the potential for real-world applications such as autonomous vehicles, disease diagnoses, and recognition of emergencies. The performance of many machine learning models depends
Externí odkaz:
http://arxiv.org/abs/2411.15602
Autor:
Abdelwahab, Abdelrahman, Vishnubhatla, Akshaj, Vaswani, Ayaan, Bharathulwar, Advait, Kommaraju, Arnav
Inaccuracies in polygraph tests often lead to wrongful convictions, false information, and bias, all of which have significant consequences for both legal and political systems. Recently, analyzing facial micro-expressions has emerged as a method for
Externí odkaz:
http://arxiv.org/abs/2411.08885
Autor:
Fan, Qianyu
With the increase of computing power, machine learning models in medical imaging have been introduced to help in rending medical diagnosis and inspection, like hemophilia, a rare disorder in which blood cannot clot normally. Often, one of the bottlen
Externí odkaz:
http://arxiv.org/abs/2409.05225
Autor:
Liu, Zuoyu1,2 (AUTHOR) liuzuoyu20@mails.ucas.ac.cn, Gao, Shijie1 (AUTHOR) gaoshijie@ciomp.ac.cn, Wu, Jiabin1 (AUTHOR) gaoshijie@ciomp.ac.cn, Chen, Yunshan1 (AUTHOR) yuxichang20@mails.ucas.ac.cn, Ma, Lie1 (AUTHOR) wangximing@ciomp.ac.cn, Yu, Xichang1,2 (AUTHOR) liruipeng22@mails.ucas.ac.cn, Wang, Ximing1 (AUTHOR), Li, Ruipeng1,2 (AUTHOR)
Publikováno v:
Sensors (14248220). Oct2024, Vol. 24 Issue 20, p6684. 19p.
Autor:
Hong, Zong-Wei, Lin, Yu-Chen
The domain of computer vision has experienced significant advancements in facial-landmark detection, becoming increasingly essential across various applications such as augmented reality, facial recognition, and emotion analysis. Unlike object detect
Externí odkaz:
http://arxiv.org/abs/2404.06029
Akademický článek
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Autor:
Kubina Jr., Richard M.1 (AUTHOR) RMK11@psu.edu, Halkowski, Madeline1 (AUTHOR), Yurich, Kirsten K. L.2 (AUTHOR), Ghorm, Kimberly3 (AUTHOR), Healy, Nora M.3 (AUTHOR)
Publikováno v:
Journal of Behavioral Education. Mar2024, Vol. 33 Issue 1, p142-162. 21p.
Autor:
Lee, Jemyoung1,2 (AUTHOR) jaymlee0407@snu.ac.kr, Park, Heejun3 (AUTHOR) eirbadmin@kumc.or.kr, Yang, Zepa3 (AUTHOR) yangzepa@gmail.com, Woo, Ok Hee3 (AUTHOR) wokhee@korea.ac.kr, Kang, Woo Young3 (AUTHOR) quartet0@hanmail.net, Kim, Jong Hyo1,2,4,5,6 (AUTHOR) quartet0@hanmail.net
Publikováno v:
Diagnostics (2075-4418). Nov2024, Vol. 14 Issue 22, p2477. 14p.
Autor:
Tinkham, Wade T.1 (AUTHOR) wade.tinkham@usda.gov, Woolsey, George A.2 (AUTHOR)
Publikováno v:
Remote Sensing. Oct2024, Vol. 16 Issue 20, p3844. 21p.
Autor:
Rubio-Martín, Sergio, García-Ordás, María Teresa, Bayón-Gutiérrez, Martín, Prieto-Fernández, Natalia, Benítez-Andrades, José Alberto
Publikováno v:
Health Inf Sci Syst 12, 20 (2024)
Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD). It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in tradit
Externí odkaz:
http://arxiv.org/abs/2403.03581