Automated Neural Nets
Automated Target Recognition
A dynamical approach to cognition has resulted in the first composite network for
automated target recognition capable of robust distortion invariant identification
of radar targets from a single echo. The network computes with diverse attractors
and uses bifurcation between periodic and point attractors as the mechanism for
cognition. Cognition in the context of automatic target recognition is the ability
of the system, on its own, to determine that an echo belongs to a familiar (learned)
target or to a novel, previously unseen, target. Cognition has important practical
advantages for autonomous systems and for facilitating machine learning.
Use of Artificial Neural Network as a Risk Assessment Tool
Preventing Child Abuse
Every year Child Protection Services (CPS) in the US receive millions of reports of incidences of children
being abused. CPS, by law, is required to investigate these reports and if necessary to intervene to protect
the children at risk. Since resources are limited, some kind of triage is taking place. Various CPS offices
use different risk assessment tools. The purpose of this paper is to report on a preliminary effort to explore
the feasibility of utilizing artificial neural network (ANN) technology as a risk assessment tool.
Third National Incidence Study of Child Abuse and Neglect (NIS-3), a congressionally mandated, periodic
effort of National Center on Child Abuse and Neglect provides a rich set of data on child abuse was used to
explore the utility of using ANN as a risk assessment tool. While this data set is adequate for the feasibility
study, future research must rely on more targeted data.
In the current study, several different ANN designs were constructed and experimented with under a
variety of conditions. These included several designs of multi layers neural network (MLNN) using several
different training algorithms and a radial basis network (RBN.) The data was divided randomly to three
groups: a Training Set, a Validation Set, and a Test Set. The procedure was to use the Training Set to
train the network and test its veracity using the Validation Set and the Test Set.
We found that a 31-25-1 MLNN architect, after being trained, is capable of classifying the Training Set
100% accurately. However, its performance deteriorates for the Validation Set and the Test Set – new data
that was not included in the Training Set. Despite this deterioration, the results are very encouraging. The
network of Experiment VII-1 was capable to classify correctly 90 percent of abused cases for the
Validation Set and 89 percent for the Test Set. This means that it missed 10 percent of abused children for
the Validation Set (false negative) and 11 percent of the Test Set. In addition, it also misclassified 13
percent (false alarm) of those children that the survey did not find to have been abused in the Validation Set
as being abused (false positive.) For the Test Set the misclassification was the same 13 percent. A radial
basis network classified 100% accurately the Training Set and 93 percent of abused cases in the Test Set. It
also misclassified 16 percent of not abused cases as being abused (false positive.)
Although, definitive data for the performance of currently used risk assessment tools is not available,
experts tell us the performance of neural network is a clear improvement over the current practice. Much
remains to be researched.
Use of Artificial Neural Network as a Risk Assessment Tool In Preventing Child Abuse (PDF full text)