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The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability. This paper proposes a novel approach to address these challenges, introducing a Bayesi
Externí odkaz:
http://arxiv.org/abs/2404.10483
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications. While transformer-based deep neural networks (DNNs) like BERT have demonstrated promise in legal tasks, accurately assessing their prediction conf
Externí odkaz:
http://arxiv.org/abs/2404.10481
Akademický článek
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Publikováno v:
AIMS Mathematics; 2024, Vol. 9 Issue 11, p1-52, 52p
Publikováno v:
AIMS Mathematics; 2024, Vol. 9 Issue 11, p1-38, 38p
Publikováno v:
AIMS Mathematics; 2024, Vol. 9 Issue 10, p1-37, 37p
Publikováno v:
Punjab University Journal of Mathematics; 2024, Vol. 56 Issue 5, p175-207, 33p
Autor:
Alghazzawi, Dilshad, Abbas, Sajida, Alolaiyan, Hanan, Khalifa, Hamiden Abd El-Wahed, Alburaikan, Alhanouf, Xin, Qin, Razaq, Abdul
Publikováno v:
AIMS Mathematics; 2024, Vol. 9 Issue 3, p1-24, 24p
This five-volume set LNCS 15436 -15440 constitutes the proceedings of the 25th International Conference on Web Information Systems Engineering, WISE 2024, held in Doha, Qatar, in December 2024. The 110 full papers and 55 short papers were presented