W.J. Tastle
Introduction to Artificial Networks and Law Enforcement Analytics
in Intelligent Data Mining in Law Enforcement Analytics, Ch.1, pages 1-9, Tastle W.J (Eds) Springer, January 2013.
P.M. Buscema
Law Enforcement and Artificial Intelligence
in Intelligent Data Mining in Law Enforcement Analytics, Ch.2, pages 11-16, Tastle W.J (Eds) Springer, January 2013.
P.M. Buscema
The General Philosophy of Artificial Adaptive Systems
in Intelligent Data Mining in Law Enforcement Analytics, Ch.3, pages 17-30, Tastle W.J (Eds), Springer, January 2013.
P.M. Buscema (et al.)
A Brief Introduction to Evolutionary Algorithms and the Genetic Doping Algorithm
in Intelligent Data Mining in Law Enforcement Analytics, Ch.4, pages 31-49, Tastle W.J (Eds), Springer, January 2013.
P.M. Buscema
Artificial Adaptive Systems in Data Visualization: Proactive Data
in Intelligent Data Mining in Law Enforcement Analytics, Ch.5, pages 51-88, Tastle W.J (Eds), Springer, January, 2013.
G.Monaghan, (et al.)
The Metropolitan Police Service Central Drug-Trafficking Database: Evidence of Need
Ch.6, pages 89-117, Tastle W.J (Eds), Springer, January, 2013.
P.M. Buscema
Supervised Artificial neural Networks: Backpropagation Neural Networks
in Intelligent Data Mining in Law Enforcement Analytics,Ch.7, pages 119-135, Tastle W.J (Eds), Springer, January 2013.
P.M. Buscema, A.Mancini, M.Breda
Preprocessing Tools for Nonlinear Dataset
in Intelligent Data Mining in Law Enforcement Analytics, Ch.8, pages 137-155, Tastle W.J (Eds), Springer, January 2013.
P.M. Buscema, S. Terzi
Metaclassifiers
in Intelligent Data Mining in Law Enforcement Analytics, Ch.9, pages 157-165, Tastle W.J (Eds), Springer, January 2013.
P.M.Buscema (et al.)
Auto-Identification of a Drug Seller Utilizing a Specialized Supervised Neural Network
in Intelligent Data Mining in Law Enforcement Analytics, Ch.10, pages 167-175, Tastle W.J (Eds), Springer, January 2013.
G.Massini
Visualization and Clustering of Self-Organizing Maps
in Intelligent Data Mining in Law Enforcement Analytics, Ch. 11, pages 177-192, Tastle W.J (Eds), Springer, January 2013.
G.Massini
Self-Organizing Maps: Identifying Nonlinear Relationships in Massive Drug Enforcement Databases
in Intelligent Data Mining in Law Enforcement Analytics, Ch. 12, pages 193-214, Tastle W.J (Eds), Springer, January 2013.
P.M. Buscema
Theory of Constraint Satisfaction Neural Networks
in Intelligent Data Mining in Law Enforcement Analytics, Ch.13, pages 215-229, Tastle W.J (Eds), Springer, January 2013.
M.Intraligi, P.M. Buscema
Application of the Constraint Satisfaction Network
in Intelligent Data Mining in Law Enforcement Analytics, Ch.14, pages 231-313, Tastle W.J (Eds), Springer, January 2013.
P.M. Buscema
Auto-Contractive Maps, H Function and the Maximally Regular Graph: A New Methodology for Data Mining
in Intelligent Data Mining in Law Enforcement Analytics, Ch.15, pages 315-381, Tastle W.J (Eds), Springer, January 2013.
P. Giovanni
Analysis of a Complex Dataset Using the Combined MST and Auto-Contractive Map
in Intelligent Data Mining in Law Enforcement Analytics, Ch.16, pages 383-398, Tastle W.J (Eds), Springer, January 2013.
G.Massini, P.M. Buscema
Auto Contractive Map and Minimal Spanning Tree: Organization of Complex Datasets on Criminal Behavior to Aid in the Deduction of Network Connectivity
in Intelligent Data Mining in Law Enforcement Analytics, Ch.17, pages 399-413, Tastle W.J (Eds), Springer, January 2013.
P.M. Buscema
Data Mining Using Nonlinear Auto-Associative Artificial Neural Networks: The Arrestee Dataset
in Intelligent Data Mining in Law Enforcement Analytics, Ch.18, pages 415-479, Tastle W.J (Eds), Springer, January 2013.
P.M. Buscema
Artificial Adaptive System for Parallel Querying of Multiple Databases
in Intelligent Data Mining in Law Enforcement Analytics, Ch.19,pages 481-511, Tastle W.J, (Eds), Springer, January 2013.
P.M. Buscema, F. Newman, G. Massini, E. Grossi, W.J. Tastle, A.K. Liu
Assessing Post-Radiotherapy Treatment involving Brain Volume Differences in Children: An Application of Adaptive Systems Methodology
in Data Mining Applications Using Artificial Adaptive Systems, Ch.1, pp 1-23, Tastle W.J (Ed), Springer, New York 2013.
P.M.Buscema, R. Passariello, E.Grossi, G. Massini , F. Fraioli, G. Serra, C. Catalano
J-Net: An Adaptive System for Computer-Aided Diagnosis in Lung Nodule Characterization
in Data Mining Applications Using Artificial Adaptive Systems, Ch.2, pp 25-61, Tastle W.J (Ed), Springer, New York 2013.
G.Massini, S. Terzi, P.M. Buscema
Population Algorithm: A New Method of Multi Dimensional Scaling
in Data Mining Applications Using Artificial Adaptive Systems, Ch.3, pp 63-74, Tastle W.J (Ed), Springer, New York 2013.
P.M. Buscema, M. Breda, E. Grossi, L. Catzola, P.L. Sacco
Semantics of Point Spaces Through the Topological Weighted Centroid and Other Mathematical Quantities – Theory and Applications
in Data Mining Applications Using Artificial Adaptive Systems, Ch.4, pp 75-139, Tastle W.J (Ed), Springer, New York 2013.
P.M. Buscema, W.J. Tastle, S. Terzi
Meta Net: A New Meta-Classifier Family
in Data Mining Applications Using Artificial Adaptive Systems, Ch. 5, pp 141-182, Tastle W.J (Ed), Springer, New York 2013.
P.M. Buscema, P.L. Sacco
Optimal Informational Sorting: The ACS-ULA Approach
in Data Mining Applications Using Artificial Adaptive Systems, Ch.6, pp 183-209, Tastle W.J (Ed), Springer, New York 2013.
P.M. Buscema, P.L. Sacco
GUACAMOLE: A New Paradigm for Unsupervised Competitive Learning
in Data Mining Applications Using Artificial Adaptive Systems, Ch.7, pp 211-230, Tastle W.J (Ed), Springer, New York 2013.
P.M. Buscema, P.L. Sacco, E. Grossi, L. A. Lodwick
Spatiotemporal Mining: A Systematic Approach to Discrete Diffusion Models for Time and Space Extrapolation
in Data Mining Applications Using Artificial Adaptive Systems, Ch. 8, pp 231-275, Tastle W.J (Ed), Springer, New York 2013.