Advances in Intelligent Systems

This special issue is concerned with Advances in Intelligent Systems. The area of intelligent systems is rapidly developing and reaching in multiple directions. In this special issue we present seven papers that show some of the area that current intelligent systems enclose. Seven papers have been selected, after review, for this special issue. These cover two categories: the first set of papers focuses mainly on the machine learning domain and on topics belonging to the general concept of intelligent systems, while the second set of papers focuses mainly on approaches belonging to engineering applications of computational intelligent systems. In the paper by S. Alvarez et al., neural expert networks for faster combined collaborative and contentbased recommendation have been considered. The authors found that the recommendation quality achieved by a feed-forward multilayer perceptron network operating on combined collaborative and content-based information is statistically significantly better than that of a network that is provided with the collaborative data alone, assuming that dimensionality reduction is performed on the collaborative and content-based data components separately. The second paper by E.R. Hruschka Jr. et al., describes a Bayesian Imputation Method for a Clustering Genetic Algorithm. Missing values are a critical problem in data mining applications. The substitution of these values, also called imputation, can be performed by several methods. This paper describes the application of an optimized version of the Bayesian Algorithm K2 as an imputation tool for a clustering genetic algorithm. The resulting hybrid system was assessed by means of simulations in five benchmark datasets. The paper by Z He et al. is related to the clustering of categorical data streams. A new algorithm that utilises small memory footprints is presented and an empirical analysis on the performance of the algorithm in clustering both synthetic and real data streams is performed. The last paper of the first part entitled “Temporal Conflict in Workflow Schemas” by Chountas et al. presents a temporal formalism for representing indeterminacy to workflow specifications. The authors further argue that integrating workflows with temporal databases can further enhance the workflow specification. The second part of this special issue is devoted to the application of novel computational intelligent methodologies in engineering. P. Jahankhani et al., describes a novel decision support system for EEG signals based on Adaptive Fuzzy Inference Neural Networks. Although, EEG signals are used widely in biomedicine, this research study describes a new approach based on a fuzzy logic system implemented in the framework of a neural network for classification of EEG signals. Decision making