Recent advancements in generative approaches in AI have opened up the prospect of
synthetic tabular clinical data generation. From filling in missing values in real-world data …

Traditionally, medical research is based on randomized controlled trials (RCTs) for
interventions such as drugs and operative procedures. However, increasingly, there is a …

Recent advancements in machine learning and deep learning have significantly improved
the diagnosis, detection, prediction, and prognosis of depressive disorders. However, these …

Generative artificial intelligence (AI) may revolutionize health care, providing solutions that
range from enhancing diagnostic accuracy to personalizing treatment plans. However, its …

In healthcare, vast amounts of data are increasingly collected through sensors for smart
health applications and patient monitoring or diagnosis. However, such medical data often …

With Polycystic Kidney Disease (PKD) potentially leading to fatal complications in patients
due to the formation of cysts in the kidneys, early detection of PKD is crucial for effective …

As the demand for efficient energy management increases, the need for extensive, high-
quality energy data becomes critical. However, privacy concerns and insufficient data …

The widespread adoption of autonomous driving technology heavily relies on data
acquisition and processing, which, while providing an intelligent experience for users, also …

Originating in experiments with computer vision in the 1960s (Nikolenko, 2021), synthetic
data have recently gained traction in the data industries. The reasons for the growing …

Building on a previously developed partially synthetic data generation algorithm utilizing
data visualization techniques, this study extends the novel algorithm to generate fully …