Time series classification is widely used in many fields, but it often suffers from a lack of labeled data. To address this, researchers commonly apply data augmentation techniques that generate ...

While advances in artificial intelligence have been slow to reach commercial P&C insurance, new trends in data augmentation could help pick up the pace, according to experts on a recent Insurtech ...

Solargis’ Evaluate 2.0 platform uses more granular time series data. Image: Solargis. For years, the solar industry has relied on Typical Meteorological Year (TMY) data as the standard for PV ...

1 Prairie View A&M University, Electrical and Computer Engineering, Texas A&M University System, Prairie View, TX, United States 2 Texas Juvenile Crime Prevention Center, Prairie View A&M University, ...

Manufacturing is becoming more data-driven by the day. Machines on the factory floor generate overwhelming volumes of sensor data, tracking everything from temperature and pressure to vibration and ...

A Python library for advanced and novel data augmentation, combining traditional techniques like cropping and blurring with state-of-the-art generative AI methods such as style transfer, image ...

Abstract: The recent eminent success of deep learning models has triggered a lot of interest in data augmentation studies. The main challenge of data-hungry models is that they are bound to ...

Abstract: Deep learning has become a hot research topic in the field of time series analysis and data mining. Training models often requires balanced and large data sets, but on the one hand, the ...