Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Manar D. Samad"'
Autor:
Sakib Abrar, Manar D. Samad
Publikováno v:
Neural Networks. 156:160-169
Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements. Additionally, in tabular data classification, DNNs are challenged by the often superior performance of traditional machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::300593e669eeedcb31558dc7991b9e43
https://doi.org/10.1016/j.neunet.2022.09.020
https://doi.org/10.1016/j.neunet.2022.09.020
Publikováno v:
2023 24th International Symposium on Quality Electronic Design (ISQED).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::599f7a0ff6c8cfe7adde93ed1014f822
https://doi.org/10.1109/isqed57927.2023.10129372
https://doi.org/10.1109/isqed57927.2023.10129372
Publikováno v:
Applications of Machine Learning 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8e4b8714dc1e1637dc4195bc5434f31
https://doi.org/10.1117/12.2641898
https://doi.org/10.1117/12.2641898
Publikováno v:
IEEE Int Conf Healthc Inform
The unpredictability and unknowns surrounding the ongoing coronavirus disease (COVID-19) pandemic have led to an unprecedented consequence taking a heavy toll on the lives and economies of all countries. There have been efforts to predict COVID-19 ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d25b38ea682f2bf5d8550c7104f555b9
https://pubmed.ncbi.nlm.nih.gov/36148026
https://pubmed.ncbi.nlm.nih.gov/36148026
Publikováno v:
Knowl Based Syst
Missing values in tabular data restrict the use and performance of machine learning, requiring the imputation of missing values. The most popular imputation algorithm is arguably multiple imputations using chains of equations (MICE), which estimates
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da762c549eba5a07c36930373a5b6ff7
http://arxiv.org/abs/2202.13734
http://arxiv.org/abs/2202.13734
Autor:
Manar D. Samad, Ali Sekmen
Publikováno v:
2021 International Conference on Engineering and Emerging Technologies (ICEET).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7a8e14f6536f530fa475622b9f9d3d9
https://doi.org/10.1109/iceet53442.2021.9659657
https://doi.org/10.1109/iceet53442.2021.9659657
Publikováno v:
ICHI
IEEE Int Conf Healthc Inform
IEEE Int Conf Healthc Inform
Deep transfer learning is a popular choice for classifying monochromatic medical images using models that are pretrained by natural images with color channels. This choice may introduce unnecessarily redundant model complexity that can limit explanat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52ebec40941a7e36eab5072f6f63e350
https://doi.org/10.1109/ichi52183.2021.00020
https://doi.org/10.1109/ichi52183.2021.00020
Publikováno v:
Proc Int Jt Conf Neural Netw
IJCNN
IJCNN
The concept of weight pruning has shown success in neural network model compression with marginal loss in classification performance. However, similar concepts have not been well recognized in improving unsupervised learning. To the best of our knowl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eeaea1350799fd236ea680830d75c1ac
https://europepmc.org/articles/PMC9493331/
https://europepmc.org/articles/PMC9493331/
Autor:
Jonna Bobzien, Norou Diawara, John W. Harrington, Khan M. Iftekharuddin, Cora Taylor, Manar D. Samad
Publikováno v:
Research in Autism Spectrum Disorders. 65:14-24
Background Individuals with autism spectrum disorders (ASD) may be differentiated from typically developing controls (TDC) based on phenotypic features in spontaneous facial expressions. Computer vision technology can automatically track subtle facia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36434ad2d0ffd47e76456a0ffcf1c345
https://doi.org/10.1016/j.rasd.2019.05.001
https://doi.org/10.1016/j.rasd.2019.05.001
Autor:
Linyuan Jing, Gregory J. Wehner, Alvaro Ulloa, Brandon K. Fornwalt, Christopher W. Good, Brent A. Williams, Christopher M. Haggerty, Manar D. Samad, Dustin N. Hartzel
Publikováno v:
JACC: Cardiovascular Imaging. 12:681-689
Objectives The goal of this study was to use machine learning to more accurately predict survival after echocardiography. Background Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7848c71dc9c2d2553c40f0c16e72169
https://doi.org/10.1016/j.jcmg.2018.04.026
https://doi.org/10.1016/j.jcmg.2018.04.026