Zobrazeno 1 - 10
of 19 218
pro vyhledávání: '"Generative adversarial network"'
Publikováno v:
Data Technologies and Applications, 2024, Vol. 58, Issue 5, pp. 787-806.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/DTA-08-2023-0523
Publikováno v:
Alexandria Engineering Journal, Vol 107, Iss , Pp 770-785 (2024)
This study addresses the challenges of data scarcity and class imbalance in structural health monitoring (SHM) of composite structures. Data-driven SHM techniques that benefit from non-destructive evaluation (NDE) are used in various composite struct
Externí odkaz:
https://doaj.org/article/7c8f7c22c3cb4017810764cb6a200d99
Publikováno v:
Heritage Science, Vol 12, Iss 1, Pp 1-20 (2024)
Abstract Archaeological illustration is a graphic recording technique that delineates the shape, structure, and ornamentation of cultural artifacts using lines, serving as vital material in archaeological work and scholarly research. Aiming at the pr
Externí odkaz:
https://doaj.org/article/eb1a6f0edd4a4b81a199ad33623afa33
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract In neuroscience and clinical diagnostics, electroencephalography (EEG) is a crucial instrument for capturing neural activity. However, this signal is polluted by different artifacts like muscle activity, eye blinks, environmental interferenc
Externí odkaz:
https://doaj.org/article/440313768bc24830b2bbb8d79de99d4c
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Explainability of convolutional neural networks (CNNs) is integral for their adoption into radiological practice. Commonly used attribution methods localize image areas important for CNN prediction but do not characterize relevant imaging fe
Externí odkaz:
https://doaj.org/article/933854f3e3f34caebec4b7f8fcd1d012
Autor:
Sebastian Johannes Müller, Eric Einspänner, Stefan Klebingat, Seraphine Zubel, Roland Schwab, Erelle Fuchs, Elie Diamandis, Eya Khadhraoui, Daniel Behme
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-9 (2024)
Abstract Objective Subtraction angiographies are calculated using a native and a contrast-enhanced 3D angiography images. This minimizes both bone and metal artifacts and results in a pure image of the vessels. However, carrying out the examination t
Externí odkaz:
https://doaj.org/article/adc1d2cb33da4b3991e91c105b4a2199
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 10, Pp 4017-4033 (2024)
Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site mea
Externí odkaz:
https://doaj.org/article/7d2a5b54706645c097c9576fbfa49450
Publikováno v:
EJNMMI Physics, Vol 11, Iss 1, Pp 1-13 (2024)
Abstract Background Deep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims
Externí odkaz:
https://doaj.org/article/2d575a2f90f64a0dba0062e5322b6bd8
Publikováno v:
Revista Română de Informatică și Automatică, Vol 34, Iss 3, Pp 149-164 (2024)
The research aimed to explore the potential of advanced machine learning (ML) algorithms in clinical and biomedical research. The significance of frameworks like generative adversarial networks (GANs), autoencoders, and autoregressive models in tackl
Externí odkaz:
https://doaj.org/article/fa0e21bba03a4a6394d19111acfe0d73
Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN
Autor:
Mozhdeh Nikpour, Ali Mohebbi
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Wall Shear Stress (WSS) is one of the most important parameters used in cardiovascular fluid mechanics, and it provides a lot of information like the risk level caused by any vascular occlusion. Since WSS cannot be measured directly and othe
Externí odkaz:
https://doaj.org/article/70f9ea306d1a4734ae0e078dff2e6445