| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa |
New York, : Springer, 2013
|
| Descrizione fisica |
1 online resource (521 p.)
|
| Disciplina |
363.2/30285
|
| Altri autori (Persone) |
BuscemaMassimo
TastleWilliam J
|
| Soggetto topico |
Data mining in law enforcement
Neural networks (Computer science)
|
| ISBN |
1-283-86558-0
94-007-4914-7
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
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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| Record Nr. | UNINA-9910438347603321 |
Info
Data mining applications using artificial adaptive systems / / William J. Tastle, editor