Zobrazeno 1 - 10
of 20 428
pro vyhledávání: '"A Shaban"'
Background: The rapid advancement of Machine Learning (ML) represents novel opportunities to enhance public health research, surveillance, and decision-making. However, there is a lack of comprehensive understanding of algorithmic bias, systematic er
Externí odkaz:
http://arxiv.org/abs/2408.13295
Autor:
Shaban, Ali, Paulheim, Heiko
Snapshot ensembles have been widely used in various fields of prediction. They allow for training an ensemble of prediction models at the cost of training a single one. They are known to yield more robust predictions by creating a set of diverse base
Externí odkaz:
http://arxiv.org/abs/2408.02707
Publikováno v:
Cancer Innovation 2024;3:e136
With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is n
Externí odkaz:
http://arxiv.org/abs/2407.12058
Autor:
Sulejman, Shaban B., Wesemann, Lukas, McCormack, Mikkaela, Meng, Jiajun, Hutchison, James A., Priscilla, Niken, McColl, Gawain, Read, Katrina, Sim, Wilson, Sukhorukov, Andrey A., Crozier, Kenneth B., Roberts, Ann
Different imaging modalities are used to extract the diverse information carried in an optical field. Two prominent modalities include bright field and phase contrast microscopy that can visualize the amplitude and phase features of a sample, respect
Externí odkaz:
http://arxiv.org/abs/2406.04576
Autor:
Kumsa, Fekede Asefa, Fowke, Jay H., Hashtarkhani, Soheil, White, Brianna M., Shrubsole, Martha J., Shaban-Nejad, Arash
Publikováno v:
Front Oncol Frontiers in Oncology, 2024 Apr 9:14:1343070
Prostate cancer is one of the leading causes of cancer-related mortality among men in the U.S. We examined the role of neighborhood obesogenic attributes on prostate cancer risk and mortality in the Southern Community Cohort Study (SCCS). From 34,166
Externí odkaz:
http://arxiv.org/abs/2405.18456
Autor:
Balduf, Paul-Hermann, Shaban, Kimia
We present efficient data-driven approaches to predict Feynman periods in $\phi^4$-theory from properties of the underlying Feynman graphs. We find that the numbers of cuts and cycles determines the period to approximately 2% accuracy. Hepp bound and
Externí odkaz:
http://arxiv.org/abs/2403.16217
Publikováno v:
JMIR Form Res JMIR Formative Research. 2024 Feb 21:8:e51727
Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities. In this stud
Externí odkaz:
http://arxiv.org/abs/2403.05575
Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer from several drawbacks, such as the inability to capture the deep sema
Externí odkaz:
http://arxiv.org/abs/2402.16194
In this paper, we propose a deep learning (DL)-based approach for efficiently computing the inverse of Hermitian matrices using truncated polynomial expansion (TPE). Our model-driven approach involves optimizing the coefficients of the TPE during an
Externí odkaz:
http://arxiv.org/abs/2402.12595
Wireless embedded edge devices are ubiquitous in our daily lives, enabling them to gather immense data via onboard sensors and mobile applications. This offers an amazing opportunity to train machine learning (ML) models in the realm of wireless devi
Externí odkaz:
http://arxiv.org/abs/2312.08577