This paper provides an overview of the recent developments in data-based techniques
focused on modern industrial applications. As one of the hottest research topics for …

Process fault detection and diagnosis (FDD) is a predominant task to ensure product quality
and process reliability in modern industrial systems. Those traditional FDD techniques are …

Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …

Identifying a small subset of informative genes from a gene expression dataset is an
important process for sample classification in the fields of bioinformatics and machine …

A novel multivariate and multiscale statistical process monitoring method is proposed with
the aim of detecting incipient failures in large slewing bearings, where subjective influence …

Abstract A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is
applied for cancer classification, that reduces the classifier's prediction errors and allows for …

The design of an optimal framework for the prediction of cancer from high-dimensional and
imbalanced microarray data is a challenging job in the fields of bioinformatics and machine …

Feature (gene) selection and classification of microarray data are the two most interesting
machine learning challenges. In the present work two existing feature selection/extraction …

In recent years, deep learning has been widely applied in process fault diagnosis due to its
powerful feature extraction ability. A predominant property of these fault diagnosis models is …

Independent component analysis (ICA) is a powerful concept for reducing the dimension of
big data in many applications. It has been used for the feature extraction of microarray gene …