Zobrazeno 1 - 10
of 3 088
pro vyhledávání: '"adversarial training"'
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-21 (2024)
Abstract Background Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved dr
Externí odkaz:
https://doaj.org/article/6f22110af321478f8be2cb5e6eae81e8
Publikováno v:
Robotic Intelligence and Automation, 2024, Vol. 44, Issue 3, pp. 351-365.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/RIA-08-2023-0109
Autor:
Lukács Kuslits, András Horváth, Viktor Wesztergom, Ciaran Beggan, Tibor Rubóczki, Ernő Prácser, Lili Czirok, István Bozsó, István Lemperger
Publikováno v:
Earth, Planets and Space, Vol 76, Iss 1, Pp 1-41 (2024)
Abstract Machine learning (ML) as a tool is rapidly emerging in various branches of contemporary geophysical research. To date, however, rarely has it been applied specifically for the study of Earth’s internal magnetic field and the geodynamo. Pre
Externí odkaz:
https://doaj.org/article/2a0ec0adb2f5479fbe9e21dafa708cc7
Autor:
Shiza Maham, Abdullah Tariq, Muhammad Usman Ghani Khan, Faten S. Alamri, Amjad Rehman, Tanzila Saba
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end fram
Externí odkaz:
https://doaj.org/article/cb4a8d6d06e44e1e81f74a28f3893933
Autor:
Nicolás Torres
Publikováno v:
Natural Language Processing Journal, Vol 8, Iss , Pp 100092- (2024)
This research contributes a comprehensive analysis of gender bias within contemporary AI language models, specifically examining iterations of the GPT series, alongside Gemini and Llama. The study offers a systematic investigation, encompassing multi
Externí odkaz:
https://doaj.org/article/1320e011bf4749c8982ecb32d76b4226
Publikováno v:
International Journal of Engineering and Technology Innovation (2024)
Current research in machine learning primarily focuses on raw coffee bean quality, hampered by limited labeled datasets for roasted beans. This study proposes a domain adaptation approach to transfer knowledge acquired from raw coffee beans to the ta
Externí odkaz:
https://doaj.org/article/717ec4afdfe84b77af262030ff34796b
Publikováno v:
Foundations of Computing and Decision Sciences, Vol 49, Iss 1, Pp 21-36 (2024)
Deep neural networks based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original input images. These crafted examples can fool sy
Externí odkaz:
https://doaj.org/article/55705007d66f480caaacbe5c6153442e
Publikováno v:
IEEE Access, Vol 12, Pp 125881-125889 (2024)
Auto-encoder has been widely used in video anomaly detection which aims to detect abnormal segments in video surveillance. However, the previous auto-encoder methods preferred to reconstruct a model of the normal event that only trains on normal samp
Externí odkaz:
https://doaj.org/article/ff223485f736472587222f9db6fe46f8
Autor:
Suchul Lee
Publikováno v:
IEEE Access, Vol 12, Pp 96051-96062 (2024)
Federated learning (FL) is a deep learning paradigm that allows clients to train deep learning models distributively, keeping raw data local rather than sending it to the cloud, thereby reducing security and privacy concerns. Although FL is designed
Externí odkaz:
https://doaj.org/article/0fb29de1c7844282bcae058074d59a7c
Autor:
Changhun Hyun, Hyeyoung Park
Publikováno v:
IEEE Access, Vol 12, Pp 83248-83259 (2024)
Despite the considerable progress made in the development of deep neural networks (DNNs), their vulnerability to adversarial attacks remains a major hindrance to their practical application. Consequently, there has been a surge of interest and invest
Externí odkaz:
https://doaj.org/article/90a281c7eef14011b00aa5ae0db9e6a9