Data Augmentation (DA) has become a critical approach in Time Series Classification
(TSC), primarily for its capacity to expand training datasets, enhance model robustness …

Limited availability of representative time-to-failure (TTF) trajectories either limits the
performance of deep learning (DL)-based approaches on remaining useful life (RUL) …

Image semantic segmentation is a dense prediction task in computer vision that is
dominated by deep learning techniques in recent years. UNet, which is a symmetric encoder …

Time series generation (TSG) studies have mainly focused on the use of Generative
Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants …

This paper presents a method for identifying fraudulent fund transfers using real bank data,
analyzing customer information, transactional activities, and customer relationships. The …

Wearable systems measuring human physiological indicators with integrated sensors and
supervised learning-based medical image analysis (eg ECG, X-ray, CT or ultrasound …

Generative AI has received much attention in the image and language domains, with the
transformer neural network continuing to dominate the state of the art. Application of these …

Conditional generation of time-dependent data is a task that has much interest, whether for
data augmentation, scenario simulation, completing missing data, or other purposes. Recent …

Machine learning tasks involving biomedical signals frequently grapple with issues such as
limited data availability, imbalanced datasets, labeling complexities, and the interference of …

This letter proposes a generative neural network architecture for spatially consistent air-to-
ground channel modeling. The approach considers the trajectories of uncrewed aerial …