Zobrazeno 1 - 10
of 17 427
pro vyhledávání: '"A. A. Ragab"'
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:
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
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
In real-time Industrial Internet of Things (IIoT), e.g., monitoring and control scenarios, the freshness of data is crucial to maintain the system functionality and stability. In this paper, we propose an AoI-Aware Deep Learning (AA-DL) approach to m
Externí odkaz:
http://arxiv.org/abs/2311.13325
DroidDissector is an extraction tool for both static and dynamic features. The aim is to provide Android malware researchers and analysts with an integrated tool that can extract all of the most widely used features in Android malware detection from
Externí odkaz:
http://arxiv.org/abs/2308.04170
Autor:
Azza Mahmoud El Sheashaey, Marium Nagah Al Zafrany Al Agha, Amr ragab Ibrahim shalaby, Salah Mohammed El Kousy, Gamalate Abd Ellatef Elgedawy
Publikováno v:
Egyptian Liver Journal, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Background Metabolomics is an emerging field that quantifies numerous metabolites systematically aiming to determine the metabolites corresponding to each biological phenotype and then provide an analysis of the mechanisms involved. Bile aci
Externí odkaz:
https://doaj.org/article/50ce4b4ebf84436f8200f2d2c37d75c3