RT Article
T1 A Model for Predicting the Class of Illicit Drug Suspects and Offenders
JF Journal of drug issues
VO 52
IS 2
SP 168
OP 181
A1 Atsa'am, Donald D.
A2 Balogun, Oluwafemi S.
A2 Agjei, Richard O.
A2 Devine, Samuel N. O.
A2 Akingbade, Toluwalase J.
A2 Omotehinwa, Temidayo O.
LA English
YR 2022
UL https://krimdok.uni-tuebingen.de/Record/1795543809
AB In this study, the artificial neural network was deployed to develop a classification model for predicting the class of a drug-related suspect into either the drug peddler or non-drug peddler class. A dataset consisting of 262 observations on drug suspects and offenders in central Nigeria was used to train the model which uses parameters such as exhibit type, suspect’s age, exhibit weight, and suspect’s gender to predict the class of a suspect, with a predictive accuracy of 83%. The model sets the pace for the implementation of a full system for use at airports, seaports, police stations, and by security agents concerned with drug-related matters. The accurate classification of suspects and offenders will ensure a faster and correct reference to the sections of the drug law that correspond to a particular offence for appropriate actions such as prosecution or rehabilitation.
K1 Artificial neural network
K1 Classification model
K1 Suspect classification
K1 Drug Trafficking
K1 Drug use
DO 10.1177/00220426211049358