Predicting per capita violent crimes in urban areas: an artificial intelligence approach

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@Article{castelli:2017:jaihc,
  author =       "Mauro Castelli and Raul Sormani and 
                 Leonardo Trujillo and Ales Popovic",
  title =        "Predicting per capita violent crimes in urban areas:
                 an artificial intelligence approach",
  journal =      "Journal of Ambient Intelligence and Humanized
                 Computing",
  year =         "2017",
  volume =       "8",
  number =       "1",
  pages =        "29--36",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Computation, CSGP, LSGP, SVM, ANN, RBF, Crime
                 Prediction Urban Security Semantics Local Search",
  ISSN =         "1868-5145",
  DOI =          "doi:10.1007/s12652-015-0334-3",
  size =         "8 pages",
  abstract =     "A major challenge facing all law-enforcement
                 organizations is to accurately and efficiently analyse
                 the growing volumes of crime data in order to extract
                 useful knowledge for decision makers. This is an
                 increasingly important task, considering the fast
                 growth of urban populations in most countries. In
                 particular, to reconcile urban growth with the need for
                 security, a fundamental goal is to optimize the
                 allocation of law enforcement resources. Moreover,
                 optimal allocation can only be achieved if we can
                 predict the incidence of crime within different urban
                 areas. To answer this call, in this paper we propose an
                 artificial intelligence system for predicting per
                 capita violent crimes in urban areas starting from
                 socio-economic data, law-enforcement data and other
                 crime-related data obtained from different sources. The
                 proposed framework blends a recently developed version
                 of genetic programming that uses the concept of
                 semantics during the search process with a local search
                 method. To analyze the appropriateness of the proposed
                 computational method for crime prediction, different
                 urban areas of the United States have been considered.
                 Experimental results confirm the suitability of the
                 proposed method for addressing the problem at hand. In
                 particular, the proposed method produces a lower error
                 with respect to the existing state-of-the art
                 techniques and it is particularly suitable for
                 analysing large amounts of data. This is an extremely
                 important feature in a world that is currently moving
                 towards the development of smart cities.",
  notes =        "WEKA",
}

Genetic Programming entries for Mauro Castelli Raul Sormani Leonardo Trujillo Ales Popovic

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