Zobrazeno 1 - 10
of 20 779
pro vyhledávání: '"Ragab A. A."'
Autor:
Li, Hailin, Ramachandra, Raghavendra, Ragab, Mohamed, Mondal, Soumik, Tan, Yong Kiam, Aung, Khin Mi Mi
Smartphone-based contactless fingerphoto authentication has become a reliable alternative to traditional contact-based fingerprint biometric systems owing to rapid advances in smartphone camera technology. Despite its convenience, fingerprint authent
Externí odkaz:
http://arxiv.org/abs/2409.18636
Metasurfaces are key to the development of flat optics and nanophotonic devices, offering significant advantages in creating structural colors and high-quality factor cavities. Multi-layer metasurfaces (MLMs) further amplify these benefits by enhanci
Externí odkaz:
http://arxiv.org/abs/2409.07121
Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. However, under the realm of continually changing environments, UDA tends to
Externí odkaz:
http://arxiv.org/abs/2407.17117
Autor:
Ragab, Mohamed, Gong, Peiliang, Eldele, Emadeldeen, Zhang, Wenyu, Wu, Min, Foo, Chuan-Sheng, Zhang, Daoqiang, Li, Xiaoli, Chen, Zhenghua
Source-free domain adaptation (SFDA) aims to adapt a model pre-trained on a labeled source domain to an unlabeled target domain without access to source data, preserving the source domain's privacy. While SFDA is prevalent in computer vision, it rema
Externí odkaz:
http://arxiv.org/abs/2406.02635
Autor:
Wang, Ziyan, Ragab, Mohamed, Yang, Wenmian, Wu, Min, Pan, Sinno Jialin, Zhang, Jie, Chen, Zhenghua
Unsupervised domain adaptation (UDA) has achieved remarkable success in fault diagnosis, bringing significant benefits to diverse industrial applications. While most UDA methods focus on cross-working condition scenarios where the source and target d
Externí odkaz:
http://arxiv.org/abs/2405.17493
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications. While Transformer-based models excel at capturing long-range dependencies, they face limitations in noise sen
Externí odkaz:
http://arxiv.org/abs/2404.08472
Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain. In this work, we introduce Universal Semi-Supervised Domain Adaptation (UniSSDA),
Externí odkaz:
http://arxiv.org/abs/2403.11234
Autor:
Ragab, Mohamed, Savateev, Yury, Wang, Wenjie, Moosaei, Reza, Tiropanis, Thanassis, Poulovassilis, Alexandra, Chapman, Adriane, Oliver, Helen, Roussos, George
The DESERE Workshop, our First Workshop on Decentralised Search and Recommendation, offers a platform for researchers to explore and share innovative ideas on decentralised web services, mainly focusing on three major topics: (i) societal impact of d
Externí odkaz:
http://arxiv.org/abs/2403.07732
The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To address this, we treat Android
Externí odkaz:
http://arxiv.org/abs/2401.16982
Autor:
Martina B. William, Sharifa Hamed, Ali Shalash, Eman M. Khedr, Mohamed H. Yousef, Shaimaa El-Jaafary, Gharib Fawi, Asmaa Helmy, Eman Hamid, Mohamed Essam, Hamin Lee, Alina Jama, Mohamed Koraym, Doaa M. Mahmoud, Sara Elfarrash, Yasmin Elsaid, Asmaa S. Gabr, Nourhan Shebl, Nesreen Abdelwahhab, Tamer M. Belal, Nehal A. B. Elsayed, Mohamed El-Gamal, Shimaa Elgamal, Salma Ragab, Jaidaa Mekky, Lobna Aly, Samir Nabhan, Gaafar Ragab, Mohamed A. Hussein, Mohamed Tharwat Hegazy, Henry Houlden, Mohamed Salama, Mie Rizig
Publikováno v:
npj Parkinson's Disease, Vol 10, Iss 1, Pp 1-9 (2024)
Abstract The impact of LRRK2 variants on the risk of Parkinson’s disease in Egyptians remains unknown. We examined 1210 Egyptians (611 PD patients and 599 controls) from 16 governorates across Egypt for 12 LRRK2 pathogenic variants. The p.Gly2019Se
Externí odkaz:
https://doaj.org/article/bd18454391134934b92f176c2a4c3b3f
