Recently, there has been a significant advancement in the water quality index (WQI) models
utilizing data-driven approaches, especially those integrating machine learning and artificial …

Network surveillance, ie, the detection of anomalous behaviour in communications in a
network, has become an important issue in recent years. In this field, techniques of statistical …

Multi-mode processing is a central feature of modern industry. The application of monitoring
technology to multi-mode processing is crucial to ensure process safety and to enhance …

Leveraging machine learning applications for predictive process control signifies a decisive
advancement in manufacturing quality management, transitioning from traditional …

The major contribution of present study is to revisit how consumer obtain high adoption
intention of artificial intelligence (AI)'s production/service based on symmetric and …

In the area of multivariate process quality control, it is sometimes important to monitor the
ratio of two normal random variables denoted by RZ over time. The concept of control charts …

Abstract Machine learning (ML) models for accurately predicting heavy metals with
inconsistent outputs have improved owing to dataset outliers, which influence model …

Purpose This study aims to analyze the performance of quality indices to continuously
validate a predictive model focused on the control chart classification. Design/methodology …

Statistical techniques allow assertive and controlled studies of projects, processes and
products, aiding in management decision-making. Statistical Process Control (SPC) is one …

Control chart patterns (CCPs) are an essential diagnostic tool for process monitoring using
statistical process control (SPC). CCPs are widely used to improve production quality in …