Results 1 to 10 of about 2,406,425 (137)

Synthetic data production for biomedical research [PDF]

Osong Public Health and Research Perspectives
Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information. Although synthetic data does not consist of actual data points, it is
Yun Gyeong Lee, Mi-Sook Kwak, Jeong Eun Kim, Min Sun Kim, Dong Un No, Hee Youl Chai   +5 more
doaj   +2 more sources

The Role of Synthetic Data and Generative AI in Breast Imaging: Promise, Pitfalls, and Pathways Forward [PDF]

Diagnostics
Artificial intelligence is reshaping breast imaging, yet progress is constrained by data scarcity, privacy restrictions, and uneven representation.
Filippo Pesapane, Lucrezia D’Amelio, Luca Nicosia, Carmen Mallardi, Anna Bozzini, Lorenza Meneghetti, Gianpaolo Carrafiello, Enrico Cassano, Sonia Santicchia   +8 more
doaj   +2 more sources

Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation

Applied Sciences, 2021
Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of healthcare data for secondary purposes. It is considered a safe approach for the sharing of sensitive data as it generates an artificial dataset that contains no ...
Fida K. Dankar, Mahmoud Ibrahim
doaj   +1 more source

Synthetic Data Generation for Data Envelopment Analysis

Data, 2023
The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods.
Andrey V. Lychev
doaj   +1 more source

Synthetic Aperture Imaging With Intensity-Only Data. [PDF]

, 2020
We consider imaging the reflectivity of scatterers from intensity-only data recorded by a single moving transducer that both emits and receives signals, forming a synthetic aperture.
Moscoso, Miguel, Novikov, Alexei, Papanicolaou, George, Tsogka, Chrysoula   +3 more
core   +2 more sources

An Adversary Model of Fraudsters’ Behavior to Improve Oversampling in Credit Card Fraud Detection

IEEE Access, 2023
Imbalanced learning jeopardizes the accuracy of traditional classification models, particularly for what concerns the minority class, which is often the class of interest.
Daniele Lunghi, Gian Marco Paldino, Olivier Caelen, Gianluca Bontempi   +3 more
doaj   +1 more source

Predictive design of sigma factor-specific promoters

Nature Communications, 2020
Automated design tools and tailored subunits are beneficial in fine-tuning all components of a complex genetic circuit. Here the authors create E. coli and B.
Maarten Van Brempt, Jim Clauwaert, Friederike Mey, Michiel Stock, Jo Maertens, Willem Waegeman, Marjan De Mey   +6 more
doaj   +1 more source

A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome

iMeta, 2022
The identification of microbial markers adequate to delineate the disease‐related microbiome alterations from the complex human gut microbiota is of great interest.
Shun Guo, Haoran Zhang, Yunmeng Chu, Qingshan Jiang, Yingfei Ma   +4 more
doaj   +1 more source

An Adaptive Large Neighborhood Search for the Larger-Scale Instances of Green Vehicle Routing Problem with Time Windows

Complexity, 2020
Due to huge amount of greenhouse gases emission (such as CO2), freight has been adversely affecting the global environment in facilitating the global economy.
Zixuan Yu, Ping Zhang, Yang Yu, Wei Sun, Min Huang   +4 more
doaj   +1 more source

Predicting the frequencies of drug side effects

Nature Communications, 2020
Currently, the frequencies of drug side effects are determined in randomised controlled clinical trials. Here the authors develop an interpretable machine learning approach to predict the frequencies of unknown side effects for drugs with a small number ...
Diego Galeano, Shantao Li, Mark Gerstein, Alberto Paccanaro   +3 more
doaj   +1 more source