Abstract
The original purpose of several of the chapters in this book was to acquaint the Metropolitan Police Service (MPS) of London, England, with the interim findings arising from the analyses of data drawn from drug trafficking crimes recorded by the MPS in 2004. Since then, the purpose has evolved into a book that would provide new and exciting guidance as to the possibilities of numerous nontrivial neural network applications to the many different fields of law enforcement. Law enforcement agencies typically collect huge quantities of data and might remain underutilized for numerous reasons, that is, insufficient time for investigators to mine the data, insufficient resources to engage in creative and exploratory methods of analytically gathering useful information from the data, or simply of not being aware of the various forms by which data can be utilized to create new insights into how mathematics, computer science, and systems analysis can be brought to bear on critical problems. These analyses were undertaken by the Semeion Research Center of Sciences of Communication (hereafter Semeion) using an array of artificial adaptive systems. As a prerequisite to the testing, training, and analytical phases, the report also discusses the importance of:
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Notes
- 1.
New Scotland Yard is the headquarters of the MPS.
References
Dzeroski, S., & Lavrac, N. (2001). Relational data mining. Berlin/New York: Springer.
Grossi, E., Mancini, A., & Buscema, M. (2007). International experience on the use of artificial neural networks in gastroenterology. Digestive and Liver Disease, 39, 278–285.
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© 2013 Springer Science+Business Media Dordrecht
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Buscema, P.M. (2013). Law Enforcement and Artificial Intelligence. In: Buscema, M., Tastle, W. (eds) Intelligent Data Mining in Law Enforcement Analytics. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4914-6_2
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DOI: https://doi.org/10.1007/978-94-007-4914-6_2
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