Data that represent complex and multivariate processes are well known to be multiscale due
to the variety of changes that could occur in a process with different localizations in time and …

Decision tree algorithms are extensively used in machine learning field to classify
biomedical signals. De-noising and feature extraction methods are also utilized to get higher …

Many recent studies propose wavelet-based hydrological and water resources forecasting
models that have been incorrectly developed and that cannot properly be used for real …

The last 15 years have seen an explosion of interest in wavelets with applications in fields
such as image compression, turbulence, human vision, radar and earthquake prediction …

Abstract Process Systems Engineering (PSE) is now a mature field with a well-established
body of knowledge, computational-oriented frameworks and methodologies designed and …

Use of wavelets in the areas of hydrologic forecasting is increasing in appeal on account of
its multi resolution capabilities in addition to its ability to deal with non-stationarities. For …

Although this book is focused on the process industries, the methodologies discussed in the
following chapters are generic and can in many instances be applied with little modification …

Recently there has been significant development in the use of wavelet methods in various
data mining processes. However, there has been written no comprehensive survey …

In this paper the O‐PLS method [1] has been modified to further improve its interpretational
functionality to give (a) estimates of the pure constituent profiles in X as well as model (b) the …

Chemometrics was started around 30 years ago to cope with the rapidly increasing volumes
of data produced in chemical laboratories. A multivariate approach based on projections …