Imbalanced data poses a substantial challenge to conventional classification methods,
which often disproportionately favor samples from the majority class. To mitigate this issue …

Multi-view learning is an emerging field that aims to enhance learning performance by
leveraging multiple views or sources of data across various domains. By integrating …

Time series anomaly detection is the process of identifying anomalies within time series
data. The primary challenge of this task lies in the necessity for the model to comprehend the …

Mechanical activities near energy pipelines pose a significant threat to energy transportation
safety and energy system supply. The distributed acoustic sensing (DAS) system, which is …

Due to the advantages of fast training speed and competitive performance, Broad Learning
System (BLS) has been widely used for classification tasks across various domains …

Abstract Machine Learning (ML) is transforming manufacturing by adeptly managing large
and complex dataset, holding immense potential to improve various machining processes …

One of the fundamental issues in predicting firm performance from the perspective of
complex systems is how to accurately construct stock networks. Most stock network-based …

The industrial fault prediction based on time series data inference is a very meaningful work.
Generally, this task has two key difficulties including the limited amount of fault data and …

RGB-Thermal semantic segmentation is important in widespread applications in adverse
illumination conditions, such as autonomous driving and robotic sensing. However, most …

Human mobility prediction is the fundamental problem in studying human social behaviors.
However, current approaches overlook the dynamic physics processes inherent in the …