Artificial Neural Networks for Drug Vulnerability Recognition and Dynamic Scenarios Simulation

Abstract

Semeion researchers have developed and used different kinds of Artificial Neural Networks (ANN) in order to process selected, “standard” data coming from drug users and from people who never used drugs before. In the first step a collection of 112 general variables, not traditionally connected to drug user's behavior, were collected from a sample of 545 people (223 heroin addicted and 322 non-users). Different types of ANNs were used to test the capability of the system to classify the drug users and the non-drug users correctly. A special ANN tool, created by Semeion, was also used to prune the number of the independent variables. The ANN selected for this first experiment was a Supervised Feed Forward Network, whose equations were enhanced by Semeion researchers. For the validation of the capability of generalization of the ANN, the Training-Testing protocol was used. This ANN was able, in the Testing phase, to classify approximately 95% of the sample with accuracy. A special sensitivity tool selected only 47 among the 112 independent variables as necessary to train the ANN. In the second step, different types of ANN were tested on the new 47 variables to decide which kind of ANN was better able to classify the sample. This benchmark included the following ANNs: a) Back Propagation with Soft Max; b) Learning Vector Quantization; c) Logicon Projection; d) Radial Basis Function; e) Squash (Semeion Network); f) Fuzzy Art Map; g) Modular Neural Network. In the third step a Constraint Satisfation Network, specifically created by Semeion, was used to simulate a dynamic fuzzy map of the drug user's world; that is, which fuzzy, or approximate, variables are critical to decide the fuzzy membership of a subject from the fuzzy membership of the drug users to the fuzzy membership of non-users and vice versa.