Zobrazeno 1 - 10
of 223
pro vyhledávání: '"Yılmaz Yasin"'
Autor:
Mumcu, Furkan, Yilmaz, Yasin
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples. While numerous successful adversarial attacks have been proposed, defenses against these attacks remain relatively understudied. Existing defense approaches either
Externí odkaz:
http://arxiv.org/abs/2410.17442
Autor:
Koutsoubis, Nikolas, Waqas, Asim, Yilmaz, Yasin, Ramachandran, Ravi P., Schabath, Matthew, Rasool, Ghulam
Artificial Intelligence (AI) has demonstrated significant potential in automating various medical imaging tasks, which could soon become routine in clinical practice for disease diagnosis, prognosis, treatment planning, and post-treatment surveillanc
Externí odkaz:
http://arxiv.org/abs/2409.16340
Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improving disease diagnoses, treatment planning, and post-treatment monitori
Externí odkaz:
http://arxiv.org/abs/2406.12815
Developing accurate machine learning models for oncology requires large-scale, high-quality multimodal datasets. However, creating such datasets remains challenging due to the complexity and heterogeneity of medical data. To address this challenge, w
Externí odkaz:
http://arxiv.org/abs/2405.07460
Autor:
Mumcu, Furkan, Yilmaz, Yasin
Adversarial machine learning attacks on video action recognition models is a growing research area and many effective attacks were introduced in recent years. These attacks show that action recognition models can be breached in many ways. Hence using
Externí odkaz:
http://arxiv.org/abs/2404.10790
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential
Externí odkaz:
http://arxiv.org/abs/2310.01438
Publikováno v:
Science and Engineering of Composite Materials, Vol 21, Iss 3, Pp 453-461 (2014)
In this study, the free vibration behavior of an annular disc made of functionally graded material (FGM) with variable geometry is investigated. The elasticity modulus, density, and thickness of the disc are assumed to vary through the radial directi
Externí odkaz:
https://doaj.org/article/a11340df3e594abe827bf6d6cb451a9c
Radio frequency (RF) fingerprinting is a tool which allows for authentication by utilizing distinct and random distortions in a received signal based on characteristics of the transmitter. We introduce a deep learning-based authentication method for
Externí odkaz:
http://arxiv.org/abs/2303.07466
Autor:
Haydari, Ammar, Yilmaz, Yasin
Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data i
Externí odkaz:
http://arxiv.org/abs/2207.10812
Anomaly detection in videos is an important computer vision problem with various applications including automated video surveillance. Although adversarial attacks on image understanding models have been heavily investigated, there is not much work on
Externí odkaz:
http://arxiv.org/abs/2204.03141