title: Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.
abstract: The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.
authors.affiliation: 1Semeion Research Centre of Sciences of Communication Via Sersale 117 , Rome, 00128 , Italy.
authors.forename: Paolo Massimo
authors.initials: PM
authors.lastname: Buscema
authors.forename: Giulia
authors.initials: G
authors.lastname: Massini
authors.forename: Guido
authors.initials: G
authors.lastname: Maurelli
mesh_terms: D000428:Alcohol Drinking / Q000453:epidemiology / Q000517:prevention & control*; D005060:Europe / Q000453:epidemiology; D006291:Health Policy; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer
keywords: artificial adaptive systems; artificial neural networks; supervised and unsupervised ANNs