Title: DrugApp: a simulation of drug suspects and offenders classification

Authors: Donald Douglas Atsa'am; Terlumun Gbaden; Ruth Wario

Addresses: Department of Computer Science, College of Physical Sciences, Joseph Sarwuan Tarka University, Makurdi 970001, Nigeria ' Department of Computer Science, College of Physical Sciences, Joseph Sarwuan Tarka University, Makurdi 970001, Nigeria ' Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, QwaQwa Campus, South Africa

Abstract: A prototype web application, named DrugApp, was developed in this study to simulate the classification of illicit drug suspects. The application logic implements an existing artificial neural network (ANN) model that uses four attributes namely, type of exhibit, age of suspect, weight of exhibit, and gender of suspect, to predict the class of a drug suspect as either a drug peddler or non-drug peddler. System design was carried out using unified modelling language (UML) tools, and development of the system followed the object-oriented programming paradigm. The app consists of graphical user interfaces (GUIs) to facilitate suspects classification in an easy and efficient manner. DrugApp could be a valuable tool to aid the police, immigration, and other law enforcement agents at airports, seaports, and land borders for classifying drug-related suspects while in transit.

Keywords: DrugApp; drug suspects classification; drug peddler; non-drug peddler; security agents.

DOI: 10.1504/IJSPM.2024.143843

International Journal of Simulation and Process Modelling, 2024 Vol.21 No.3, pp.147 - 154

Received: 27 Feb 2024
Accepted: 20 Jul 2024
Published online: 10 Jan 2025 *

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