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 …

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 …

Graphs are powerful structures able to capture topological and semantic information from
data, hence suitable for modelling a plethora of real-world (complex) systems. For this …

Topological Data Analysis is a novel approach, useful whenever data can be described by
topological structures such as graphs. The aim of this paper is to investigate whether such …

The possibility of clustering objects represented by structured data with possibly non-trivial
geometry certainly is an interesting task in pattern recognition. Moreover, in the Big Data era …

This paper investigates a novel graph embedding procedure based on simplicial
complexes. Inherited from algebraic topology, simplicial complexes are collections of …

Pattern recognition in the graphs domain gained a lot of attention in the last two decades,
since graphs are able to describe relationships (edges) between atomic entities (nodes) …

Calibrating a classification system consists in transforming the output scores, which
somehow state the confidence of the classifier regarding the predicted output, into proper …

Dissimilarity spaces, along with feature reduction/selection techniques, are among the
mainstream approaches when dealing with pattern recognition problems in structured (and …

Embedding spaces are one of the mainstream approaches when dealing with structured
data. Granular Computing, in the last decade, emerged as a powerful paradigm for the …