Generating synthetic data through generative models is gaining interest in the ML
community and beyond. In the past, synthetic data was often regarded as a means to private …

Abstract Internet of Things (IoT) is likely to revolutionize healthcare, energy, education,
transportation, manufacturing, military, agriculture, and other industries. However, for the …

Anonymization techniques are widely used to make personal data broadly available for
analytics/data-mining purposes while preserving the privacy of the personal information …

Generating synthetic data through generative models is gaining interest in the ML
community and beyond, promising a future where datasets can be tailored to individual …

Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications …

Network intrusion detection systems (NIDS) are the most common tool used to detect
malicious attacks on a network. They help prevent the ever-increasing different attacks and …

Synthetic data serves as an alternative in training machine learning models, particularly
when real-world data is limited or inaccessible. However, ensuring that synthetic data …

The creation and application of data in biomedicine and healthcare often face privacy
constraints, bias, distributional shifts, underrepresentation of certain groups and data …

Simple Summary To differentiate IDH-mutant grade 4 astrocytomas from IDH-wild-type
glioblastomas, two MRI sequences (post-contrast T1 and T2-FLAIR) were acquired from 57 …

This paper proposes a novel data augmentation scheme called the conditional generative
adversarial network minority-class-augmented oversampling scheme (CTGAN-MOS) for …