Abstract Machine learning has made significant advances in the field of image processing.
The foundation of this success is supervised learning, which necessitates annotated labels …

With the high demand and sub-nanometer design for integrated circuits, surface defect
complexity and frequency for semiconductor wafers have increased; subsequently …

Deep neural networks have promoted the technology development of fault classification and
remaining useful life (RUL) prediction for mechanical equipment due to their powerful …

This paper presents a novel framework that enables the generation of unbiased estimates
for test loss using fewer labeled samples, effectively evaluating the predictive performance …

Smart homes, leveraging IoT technology to interconnect various devices and appliances to
the internet, enable remote monitoring, automation, and control. However, collecting …

Deep learning-based fire detection models are usually trained offline on static datasets. For
continuously increasing heterogeneous sensor data, incremental learning is a resolution to …

Importance Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-
and-error approach. Under this approach, many patients have to endure sequential trials of …

Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained
attention within the smart manufacturing context. To address changes within manufacturing …

Abstract Few-Shot Class Incremental Learning (FSCIL) is a recently introduced Class
Incremental Learning (CIL) setting that operates under more constrained assumptions: only …

The edge computing method and Internet of Things (IoT), which offers significantly shorter
inactivity intervals, is one of the promising network technologies in today's generation of …