The present paper brings together openly available datasets and simulators for testing of
process monitoring and fault diagnosis techniques. Some general characteristics of these …

Dimensionality reduction methods, also known as projections, are frequently used in
multidimensional data exploration in machine learning, data science, and information …

Thanks to the increasingly high standard of electronics, the semiconductor material science
and semiconductor manufacturing have been booming in the last few decades, with massive …

Many search strategies have been exploited for the task of feature selection (FS), in an effort
to identify more compact and better quality subsets. Such work typically involves the use of …

The most critical issue in time series data is predicting future data values. Recently, an
ensemble model combining multiple models with superior predictive performance has …

The big data samples are important source for analytics. However, its relevant/irrelevant
information, unspecified variables/scales, noise/null, and so forth impose huge challenges …

High-dimensional problem poses significant challenges for dictionary learning based
classification architecture. Joint Dimension Reduction and Dictionary Learning (JDRDL) …

Semiconductor manufacturing is one of the most technologically and highly complicated
manufacturing processes. Traditional machine learning algorithms such as uni-variate and …

Industries are going through the fourth industrial revolution (Industry 4.0), where
technologies like the Industrial Internet of things, big data analytics, and machine learning …

In the fields of machine learning and data mining, feature selection methods are used to
identify the most cost-effective predictors and to give a deeper understanding of pattern …