The nature of manufacturing systems faces ever more complex, dynamic and at times even
chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an …

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is
described. Methods are illustrated using simulated case studies, and 4 experimental case …

Time series prediction techniques have been used in many real-world applications such as
financial market prediction, electric utility load forecasting, weather and environmental state …

Support vector machine (SVM) is a very specific type of learning algorithms characterized by
the capacity control of the decision function, the use of the kernel functions and the sparsity …

Corporate credit rating analysis has attracted lots of research interests in the literature.
Recent studies have shown that Artificial Intelligence (AI) methods achieved better …

Protein–protein interactions (PPIs) are central to most biological processes. Although efforts
have been devoted to the development of methodology for predicting PPIs and protein …

With the rapid increase in the world's population, the global electricity demand has
increased drastically. Therefore, it is required to adopt efficient energy management …

Demand prediction is playing a progressively important role in electricity management, and
is fundamental to the corresponding decision-making. Because of the high variability of the …

Online portfolio selection is a fundamental problem in computational finance, which has
been extensively studied across several research communities, including finance, statistics …

Recently, support vector machine (SVM) has become a popular tool in time series
forecasting. In developing a successful SVM forecastor, the first step is feature extraction …