Ricerc@Sapienza

Computational intelligence and pattern recognition techniques are gaining more and more attention as the main computing tools in bioinformatics applications. This is due to the fact that biology by definition, deals with complex systems and that computational intelligence can be considered as an effective approach when facing the general problem of complex systems modelling. Moreover, most data available on shared databases are represented by sequences and graphs, thus demanding the definition of meaningful dissimilarity measures between patterns, which are often non-metric in nature.

In this paper we propose a novel evolutive agent-based clustering algorithm where agents act as individuals of an evolving population, each one performing a random walk on a different subset of patterns drawn from the entire dataset. Such agents are orchestrated by means of a customised genetic algorithm and are able to perform simultaneously clustering and feature selection.

Workers healthcare gained a lot of attention recently as many countries are increasingly concerning about welfare. This paper faces the problem of predicting occupational disease risks by means of computational intelligence and pattern recognition techniques. Specifically, three different machine learning approaches are compared: the first one is based on the k-means algorithm, in charge to determine a set of meaningful labelled clusters as the final model. The latter two are based on fully supervised techniques, namely Support Vector Machines and K-Nearest Neighbours.

The World energy demand is progressively growing, so that many different Renewable Energy Sources (RESs) are exploited to meet the user needs and to reduce the Global Warming. In this context, an emerging RES is the sea wave energy, because it can offer a better continuity in the energy production by taking advantage of the stationary nature of the waves. Research in the energy harvesting from waves has led to the development of Wave Energy Converters (WECs). The adoption of Computational Intelligence techniques become crucial for maximizing the WECs efficiency.