This investigation employs a quantum neural network (QNN) synergistically integrated with a
quantitative structure-property relationship (QSPR) model for the comprehensive evaluation …

This paper proposes a quantitative structure–property relationship model (QSPR) based on
machine learning (ML) for a pyrimidine-pyrazole hybrid as a corrosion inhibitor. Based on …

This paper presents a quantitative structure–property relationship (QSPR)-based machine
learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values …

This paper endeavors to present an in-depth investigation into the corrosion inhibition
efficiency (CIE) of novel triazole derivatives serving as corrosion inhibitors. Among the array …

This work used a variational quantum circuit (VQC) in conjunction with a quantitative
structure-property relationship (QSPR) model to completely investigate the corrosion …

This scientific paper aims to investigate the best machine learning (ML) for predicting the
corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a …

Industrial process plants are instrumented with a large number of redundant sensors and the
measured variables are often contaminated by random noises. Thus, it is significant to …

Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of
nonlinear processes. However, traditional JITL approaches mainly focus on equal sample …

This study explores the efficacy of using a simplified molecular input line entry system
(SMILES) as the sole feature, replacing quantum chemical properties (QCP), in predicting …

Nonlinear and multimode characteristics commonly appear in modern industrial process
data with increasing complexity and dynamics, which have brought challenges to soft sensor …