Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of
medicine, understanding the potential uses of generative AI in health care becomes …

Synthetic health data have the potential to mitigate privacy concerns in supporting
biomedical research and healthcare applications. Modern approaches for data generation …

Deep learning models have demonstrated superior performance in several real-world
application problems such as image classification and speech processing. However …

Load forecasting is one of the critical tasks for enhancing the energy efficiency of smart
grids. Even though recent deep learning-based load forecasting models have shown …

Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …

Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving
offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However …

Abstract Generative Adversarial Networks (GANs) are an important tool to generate synthetic
medical data, in order to combat the limited and difficult access to the real data sets and …