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Intelligent Data Mining in Law Enforcement Analytics New Neural Networks Applied to Real Problems /

This book provides a thorough summary of the means currently available to the investigators of Artificial Intelligence for making criminal behavior (both individual and collective) foreseeable, and for assisting their investigative capacities.� The volume provides chapters on the introduction of art...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Buscema, Massimo. (Editor), Tastle, William J. (Editor)
Format: Electronic
Language:English
Published: Dordrecht : Springer Netherlands : Imprint: Springer, 2013.
Subjects:
Social sciences.
Artificial intelligence.
Social sciences > Methodology.
Social Sciences.
Methodology of the Social Sciences.
Artificial Intelligence (incl. Robotics).
Mathematical Models of Cognitive Processes and Neural Networks.
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-94-007-4914-6
Table of Contents:
  • Dedication
  • Preface.-�Chapter 1. Introduction to Artificial Networks and Law Enforcement Analytics; William J. Tastle
  • Chapter 2. Law Enforcement and Artificial Intelligence; Massimo Buscema
  • Chapter 3. The General Philosophy of Artificial Adaptive Systems; Massimo Buscema
  • Chapter 4. A Brief Introduction to Evolutionary Algorithms and the Genetic Doping Algorithm; M. Buscema, M. Capriotti
  • Chapter 5. Artificial Adaptive Systems in Data Visualization: Pro-Active data; Massimo Buscema
  • Chapter 6. The Metropolitan Police Service Central Drug Trafficking Database: Evidence of Need; Geoffrey Monaghan and Stefano Terzi
  • Chapter 7. Supervised Artificial neural Networks: Back Propagation Neural Networks; Massimo Buscema
  • Chapter 8. Pre-Processing Tools for Non-Linear Data Sets; Massimo Buscema, Alessandra Mancini and Marco Breda
  • Chapter 9. Metaclassifiers; Massimo Buscema, Stefano Terzi
  • Chapter 10. Auto Identification of a Drug Seller Utilizing a Specialized Supervised Neural Network; Massimo Buscema and Marco Intraligi
  • Chapter 11. Visualization and Clustering of Self-Organizing Maps; Giulia Massini
  • Chapter 12. Self-Organizing Maps: Identifying Non-Linear Relationships in Massive Drug Enforcement Databases; Guila Massini
  • Chapter 13. Theory of Constraint Satisfaction Neural Networks; Massimo Buscema
  • Chapter 14. Application of the Constraint Satisfaction Network; Marco Intraligi and Massimo Buscema
  • Chapter 15. Auto-Contractive Maps, h Function and the Maximally regular Graph: A new methodology for data mining; Massimo Buscema
  • Chapter 16. Analysis of a Complex Dataset Using the Combined MST and Auto Contractive Map; Giovanni Pieri
  • Chapter 17. Auto Contractive Mapsand Minimal Spanning tree: Organization of Complex datasets on criminal behavior to aid in the deduction of network connectivity; Giula Massini and Massimo Buscema
  • Chapter 18. Data Mining Using Non-linear Auto Associative Artificial Neural Networks: The Arrestee Dataset; Massimo Buscema
  • Chapter 19. Artificial Adaptive System for Parallel Querying of Multiple Databases; Massimo Buscema.-.

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