This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …

Data Mining (DM) is a fundamental component of the Data Science process. Over recent
years a huge library of DM algorithms has been developed to tackle a variety of problems in …

When planning bioenergy production from biomass, planners should take into account each
and every stakeholder along the biomass supply chains, eg biomass resources suppliers …

Over recent years a huge library of data mining algorithms has been developed to tackle a
variety of problems in fields such as medical imaging and network traffic analysis. Many of …

Oak forests are essential for the ecosystems of many countries, particularly when they are
used in vegetal restoration. Therefore, models for predicting the potential habitat of oaks can …

Coal mine safety is of paramount concern to mining industry. Mine accidents have various
causes and consequences including catastrophic failure of mine, substantial economic …

Traditionally, the sanitation infrastructures of most of the Urban Wastewater Systems (UWSs)
have been managed individually, without considering the many relationships among the …

This work presents advances in the design of a hybrid methodology that combines artificial
intelligence and statistical tools to induce a model of explicit knowledge in relation to the …

Here we present a new open-source software package designed to facilitate the analysis of
atmospheric data, with emphasis on data mining applications applied to single-particle mass …

This paper reports on a successful application of statistical and inductive learning methods
to determine optimal discriminating parameters and develop predictive models for the …