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Investigating Drug Peddling in Nigeria Using a Machine Learning Approach

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

Abstract

The problem persists despite the heavy consequences of jail and the death penalty imposed on those found guilty of selling drugs. This situation creates an alarm among academics and managers who work in practice. Hotbeds for drug dealers are on the rise, and their financial impact on the global economy is moving forward. Drug peddlers distract society by war** peace, justice, and order and threaten the Sustainable Development Goals (SDGs). This study evaluated secondary data containing qualities and suspect drug groups to determine whether machine learning techniques could predict the suspect drug group. We developed a prediction model using nine machine learning algorithms: AdaBoost (AB), naïve Bayes (NB), logistic regression (LR), K nearest neighbour (KNN), random forest (RF), decision tree (DT), neural network (NN), CN2 and support vector machine (SVM). The study utilized Orange data mining software to obtain a more accurate perspective on the data. Predictive accuracy was determined using 5-fold stratified cross-validation. Friedman’s test was conducted, and the results showed that the performance of each algorithm was significantly different. Also, the study compared the models and compiled the results. The results reveal that the random forest has the highest accuracy compared to the others. This prediction model implies that high accuracy can help the government make informed decisions by accurately identifying and classifying suspected individuals and offenders. This prediction will help the government refer suspected individuals and offenders who meet the qualifications for specific drug law sections, like prosecution or rehabilitation, to those sections with a faster and more accurate rate. The outcome of this study should be helpful to law enforcement agents, analysts, and other drug practitioners who may find machine learning tools dependable to detect and classify drug offenders.

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Authors and Affiliations

  1. School of Computing, University of Eastern, Kuopio, Finland

    Oluwafemi Samson Balogun, Mazhar Moshin, Keijo Haataja, **ao-Zhi Gao & Pekka Toivanen

  2. School of Business, JAMK University of Applied Sciences, Rajakatu 35, 40100, Jyväskylä, Finland

    Sunday Adewale Olaleye

Authors
  1. Oluwafemi Samson Balogun
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  2. Sunday Adewale Olaleye
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  3. Mazhar Moshin
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  4. Keijo Haataja
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  5. **ao-Zhi Gao
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  6. Pekka Toivanen
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Correspondence to Oluwafemi Samson Balogun .

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Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Wei** Ding

Appendix

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Table 6. Friedman test
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Table 7. Friedman effect size
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Table 8. Pairwise comparisons using Wilcoxon rank sum test with continuity correction for the algorithms
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Balogun, O.S., Olaleye, S.A., Moshin, M., Haataja, K., Gao, XZ., Toivanen, P. (2022). Investigating Drug Peddling in Nigeria Using a Machine Learning Approach. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_10

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