Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Habte Tadesse Likassa"'
Publikováno v:
Stats, Vol 7, Iss 3, Pp 1066-1083 (2024)
Heart failure is a major global health concern, especially in Ethiopia. Numerous studies have analyzed heart failure data to inform decision-making, but these often struggle with limitations to accurately capture death dynamics and account for within
Externí odkaz:
https://doaj.org/article/de0b1389e34f4370bfe508e3bd21db6d
Publikováno v:
Journal of Imaging, Vol 10, Iss 7, p 151 (2024)
Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to im
Externí odkaz:
https://doaj.org/article/44f8a24970544b10b2bd248fde098ca1
Autor:
Gizachew Gobebo Mekebo, Alemayehu Siffir Argawu, Habte Tadesse Likassa, Wondimu Ayele, Senahara Korsa Wake, Dechasa Bedada, Belema Hailu, Temesgen Senbeto, Ketema Bedane, Kebede Lulu, Sagni Daraje, Reta Lemesa, Gudeta Aga, Endale Alemayehu, Bizunesh Kefale, Terefa Bechera, Getachew Tadesse, Agassa Galdassa, Jiregna Olani, Geribe Hemba, Girma Teferi, Abebe Argaw, Tariku Irana, Tsigereda Tilahun, Gezahagn Diriba
Publikováno v:
BMC Pregnancy and Childbirth, Vol 22, Iss 1, Pp 1-10 (2022)
Abstract Background World Health Organization recommends exclusive breastfeeding (EBF) for the first 6 months of life. EBF has sustainable long-term health benefits for both infants and mothers. Despite its benefits, the practice of EBF in Ethiopia i
Externí odkaz:
https://doaj.org/article/755ab394385845cea9a53a766bf6cf33
Publikováno v:
Infectious Disease Modelling, Vol 6, Iss , Pp 302-312 (2021)
In this study, predictive models are proposed to accurately estimate the confirmed cases and deaths due to of Corona virus 2019 (COVID-19) in Africa. The study proposed the predictive models to determine the spatial and temporal pattern of COVID 19 i
Externí odkaz:
https://doaj.org/article/e5ad4294b2174cc5b6376c41643cbf62
Publikováno v:
IEEE Access, Vol 7, Pp 125011-125021 (2019)
In this paper, we propose a novel robust algorithm for image recovery via affine transformations and the L2,1 norm. To be robust against miscellaneous adverse effects such as occlusions, outliers, and heavy sparse noise, the new algorithm integrates
Externí odkaz:
https://doaj.org/article/83d66d6e56e14beb89d1660572a6f078
Publikováno v:
Modelling and Simulation in Engineering, Vol 2021 (2021)
In this paper, we developed a new robust part-based model for facial landmark localization and detection via affine transformation. In contrast to the existing works, the new algorithm incorporates affine transformations with the robust regression to
Externí odkaz:
https://doaj.org/article/a270d9f7a4a2454ea8d1ffc4e1175b0d
Publikováno v:
International Journal of Mathematics and Mathematical Sciences, Vol 2021 (2021)
In this paper, we propose a novel robust algorithm for image recovery via affine transformations, the weighted nuclear, L∗,w, and the L2,1 norms. The new method considers the spatial weight matrix to account the correlated samples in the data, the
Externí odkaz:
https://doaj.org/article/4098645d522c47d08ae9f491bef8bb77
Autor:
Habte Tadesse Likassa
Publikováno v:
International Journal of Mathematics and Mathematical Sciences, Vol 2020 (2020)
This paper proposes an effective and robust method for image alignment and recovery on a set of linearly correlated data via Frobenius and L2,1 norms. The most popular and successful approach is to model the robust PCA problem as a low-rank matrix re
Externí odkaz:
https://doaj.org/article/717687107ac34cf1adbeaebe1ff012d6
Publikováno v:
International Journal of Mathematics and Mathematical Sciences, Vol 2020 (2020)
In this work, a new robust regularized shrinkage regression method is proposed to recover and align high-dimensional images via affine transformation and Tikhonov regularization. To be more resilient with occlusions and illuminations, outliers, and h
Externí odkaz:
https://doaj.org/article/b60f6c57594741f4bd68267c98e702b0
Autor:
Habte Tadesse Likassa
107
In this thesis, two robust affine transformation-assisted methods with both real-world applications and methodology development are developed to deal with out-liers and heavy sparse noise. Firstly, we present a new robust regression approach
In this thesis, two robust affine transformation-assisted methods with both real-world applications and methodology development are developed to deal with out-liers and heavy sparse noise. Firstly, we present a new robust regression approach
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/322cr5
