Validation approaches for a biological model generation describing visitor behaviours in a cultural heritage scenario

Salvatore Cuomo, Pasquale De Michele, Giovanni Ponti, Maria Rosaria Posteraro

Research output: Contribution to conferencePaper

7 Citations (Scopus)

Abstract

In this paper we propose a biologically inspired mathematical model to simulate the personalized interactions of users with cultural heritage objects. The main idea is to measure the interests of a spectator w.r.t. an artwork by means of a model able to describe the behaviour dynamics. In this approach, the user is assimilated to a computational neuron, and its interests are deduced by counting potential spike trains, generated by external currents. The key idea of this paper consists in comparing a strengthened validation approach for neural networks based on classification with our novel proposal based on clustering; indeed, clustering allows to discover natural groups in the data, which are used to verify the neuronal response and to tune the computational model. Preliminary experimental results, based on a phantom database and obtained from a real world scenario, are shown. They underline the accuracy improvements achieved by the clustering-based approach in supporting the tuning of the model parameters.
Original languageEnglish
DOIs
Publication statusPublished - 2015
Event3rd International Conference on Data Management Technologies and Applications, DATA 2014 - Vienna, Austria
Duration: 1 Jan 2015 → …

Conference

Conference3rd International Conference on Data Management Technologies and Applications, DATA 2014
CountryAustria
CityVienna
Period1/1/15 → …

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

Cite this

Cuomo, S., De Michele, P., Ponti, G., & Posteraro, M. R. (2015). Validation approaches for a biological model generation describing visitor behaviours in a cultural heritage scenario. Paper presented at 3rd International Conference on Data Management Technologies and Applications, DATA 2014, Vienna, Austria. https://doi.org/10.1007/978-3-319-25936-9_10