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 …

Traditional methods of forecasting and analyzing property trends using statistical analysis
and questionnaires are limited; in particular, they are too slow to provide insights based on …

Real-time safety assessment of dynamic systems is of paramount importance in industrial
processes since it provides continuous monitoring and evaluation to prevent potential harm …

Graph-based multi-view clustering has gained increasing attention due to its ability to
effectively unveil complex nonlinear structures among data points from various views …

The potential of text analytics is revealed by Machine Learning (ML) and Natural Language
Processing (NLP) techniques. In this paper, we propose an NLP framework that is applied to …

To mitigate the shortage of labeled data, Few-Shot Classification (FSC) methods train deep
neural networks (DNNs) on a base dataset with sufficient labeled data, and then adapt them …

Multiview clustering has gained attention for its ability to incorporate complementary
information from multiple sources of data, leading to better clustering results. However, these …

The Broad Learning System (BLS) has demonstrated strong performance across a variety of
problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is …

Ensemble clustering, which combines the information from multiple base clusterings to
obtain a better partition result, has received extensive attention due to its effectiveness and …

Broad learning systems (BLSs) are becoming increasingly popular due to their fast and
superior learning capabilities. However, their performances are susceptible to outliers and …