Virtual sample generation (VSG) technology aims to generate virtual samples based on real
samples, in order to expand the size of the datasets and improve model performance …

Virtual sample generation (VSG), as a cutting-edge technique, has been successfully
applied in machine learning-assisted materials design and discovery. A virtual sample …

Carbon monoxide (CO) is a toxic gas emitted during municipal solid waste incineration
(MSWI). Its emission prediction is conducive to pollutant reduction and optimized control of …

Abstract Machine learning (ML) approaches have been developed to predict materials'
corrosion inhibition efficiency, particularly pyrimidine compounds. Notably, the virtual …

This study explores the significant elements in driving visitors' mind while their willingness to
visit (WTV) is formed and affected by the dual-pricing policy announced by a retail market …

In this paper, a method for detecting surface roughness in machining processes is proposed
to solve the problem of low detection accuracy caused by a small sample size in machine …

The development of material informatics has led to an increasingly deep intersection
between material science and machine learning (ML). However, limited data volume …

Real time detection of production quality and environmental protection indices is the basis
for realizing operational optimization of industrial processes. Insufficient samples are one of …

The limited information and knowledge inherent in small sample sizes often result in poor
generalization performance of constructed models. To address this issue, we propose a …

In process industry, data-driven soft sensor often faces the problem of data shortage in
modeling due to factors such as high cost of label samples acquisition and high data …