Zobrazeno 1 - 10
of 16 617
pro vyhledávání: '"Synthetic data"'
Autor:
Kiana Farhadyar, Federico Bonofiglio, Maren Hackenberg, Max Behrens, Daniela Zöller, Harald Binder
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-17 (2024)
Abstract In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such he
Externí odkaz:
https://doaj.org/article/d25e58a81cc94eca9b10f4d9990b625e
Publikováno v:
Visual Intelligence, Vol 2, Iss 1, Pp 1-22 (2024)
Abstract The Metaverse’s emergence is redefining digital interaction, enabling seamless engagement in immersive virtual realms. This trend’s integration with AI and virtual reality (VR) is gaining momentum, albeit with challenges in acquiring ext
Externí odkaz:
https://doaj.org/article/d7ef7d0b02ff440e8e9eb66ae465f588
Autor:
Divas Karimanzira
Publikováno v:
Stats, Vol 7, Iss 3, Pp 808-826 (2024)
The lack of data on flood events poses challenges in flood management. In this paper, we propose a novel approach to enhance flood-forecasting models by utilizing the capabilities of Generative Adversarial Networks (GANs) to generate synthetic flood
Externí odkaz:
https://doaj.org/article/8f62aa436bff4e3fa93bc3fccbf35506
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract This study addresses challenges related to privacy issues in utilizing medical data, particularly the protection of personal information. To overcome this obstacle, the research focuses on data synthesis using real-world time-series generati
Externí odkaz:
https://doaj.org/article/8f43a7f4e2b041709f200ec4820c628c
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-14 (2024)
Abstract Background Synthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly evaluate synthetically
Externí odkaz:
https://doaj.org/article/3a8b76e7aafb44debb6b26488488c5bb
Autor:
Vasileios C. Pezoulas, Dimitrios I. Zaridis, Eugenia Mylona, Christos Androutsos, Kosmas Apostolidis, Nikolaos S. Tachos, Dimitrios I. Fotiadis
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 2892-2910 (2024)
Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with
Externí odkaz:
https://doaj.org/article/1595553cc13b4b048a4661b05bb4ccd8
Publikováno v:
SICE Journal of Control, Measurement, and System Integration, Vol 17, Iss 1, Pp 87-105 (2024)
Efficiently recognizing Activities of Daily Living (ADLs) requires overcoming challenges in collecting datasets through innovative approaches. Simultaneously, it involves adapting to the demand for interpreting human activities amidst temporal sequen
Externí odkaz:
https://doaj.org/article/268b97df65cf448bb69bd6e75c4befcd
Publikováno v:
Commodities, Vol 3, Iss 3, Pp 254-280 (2024)
The dynamic structure of electricity markets, where uncertainties abound due to, e.g., demand variations and renewable energy intermittency, poses challenges for market participants. We propose generative adversarial networks (GANs) to generate synth
Externí odkaz:
https://doaj.org/article/9af5f9b153794fc99f0f46805e97cc97
Publikováno v:
Orphanet Journal of Rare Diseases, Vol 19, Iss 1, Pp 1-8 (2024)
Abstract Background Globally, researchers are working on projects aiming to enhance the availability of data for rare disease research. While data sharing remains critical, developing suitable methods is challenging due to the specific sensitivity an
Externí odkaz:
https://doaj.org/article/fa589ac1d392488da873d187ea3f33f4
Autor:
Nicolas Alexander Schulz, Jasmin Carus, Alexander Johannes Wiederhold, Ole Johanns, Frederik Peters, Natalie Rath, Katharina Rausch, Bernd Holleczek, Alexander Katalinic, the AI-CARE Working Group, Christopher Gundler
Publikováno v:
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables s
Externí odkaz:
https://doaj.org/article/dec1b1d848d74098abd187748b4ee6cc