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Prediction of juvenile crime in Bangladesh due to drug addiction using machine learning and explainable AI techniques

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Abstract

In this paper, we depict the concept of predicting juvenile crime due to drug addiction using machine learning techniques. With the exponentially increasing crime rate, it has become a daunting task to prevent them at an evenly faster rate. Therefore, we have worked with several variables that are inextricably linked with adolescent offenses. The study identifies the causes of adolescent drug addiction by pinpointing the behavioral disorders and predicts their tendency of crime engagement. Using a dataset of both drug-addicted and non-addicted teens, the proposed approach predicts the latent connections to future under-aged crimes. The dataset was collected from four drug rehabilitation centers and high school students. To achieve better prediction, we have used multiple machine learning techniques such as Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, XGBoost, and Multilayer Perceptrons (MLPs). We have evaluated several performance parameters, namely accuracy, precision, recall, and f1 score, and compared them in light of individual algorithms. The experimental results showed that MLPs algorithm outperformed others attaining 95.36% accuracy, whereas other algorithms achieved accuracies ranging from 89 to 94%. We have also extracted the best features by applying Univariate Feature Selection method and Recursive Feature Elimination (RFE) method. The proffered estimation was explained by Shapley Additive explanation (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME) techniques. The proposed method can allow counter-measures of the juvenile criminal activities induced from addiction by producing explained crime forecast.

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References

  1. Santos, R.B. (2016). Crime analysis with crime mapping. Sage publications.

  2. Wang, T., et al. (2013). Learning to detect patterns of crime. In Joint European conference on machine learning and knowledge discovery in databases. Springer.

  3. Rudin, C. (2013). Predictive policing using machine learning to detect patterns of crime. Wired Magazine.

  4. Rachuba, L., Stanton, B., & Howard, D. (1995). Violent crime in the United States: An epidemiologic profile. Archives of pediatrics & adolescent medicine, 149(9), 953–960.

    Article  Google Scholar 

  5. O'Donnell, I. (2020). Measuring Recidivism: A Research Note. The Irish Jurist, 64.

  6. Lane, J. (2018). Addressing juvenile crime: What have we learned, and how should we proceed? Criminology & Public Policy, 17(2), 283–307.

    Article  Google Scholar 

  7. Khan, M. and Islam, M. (2016). Children‘s Involvement in crime on the Rise ‘. Dhaka Tribune.

  8. Dewan, A. M., Haider, M., & Amin, M. (2014). Exploring crime statistics. Dhaka Megacity (pp. 257–282). Springer.

    Chapter  Google Scholar 

  9. Morgado, P.C. et al. (2019). Practical foundations of machine learning for addiction research. Part I. Methods and techniques.

  10. Epstein, D. H., et al. (2020). Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data. NPJ digital medicine, 3(1), 1–12.

    Article  Google Scholar 

  11. Arif, M. et al. (2021). Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population. International Journal of Electrical & Computer Engineering (2088–8708), 11(5).

  12. Lin, Y.-L., Yen, M.-F., & Yu, L.-C. (2018). Grid-based crime prediction using geographical features. ISPRS International Journal of Geo-Information, 7(8), 298.

    Article  Google Scholar 

  13. Al Boni, M. and Gerber M.S. (2016). Area-specific crime prediction models. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE.

  14. Shamsuddin, N.H.M., N.A. Ali, and Alwee. R. (2017) An overview on crime prediction methods. In 2017 6th ICT International Student Project Conference (ICT-ISPC). IEEE.

  15. Bharati, A., & Sarvanaguru, R. (2018). Crime prediction and analysis using machine learning. International Research Journal of Engineering and Technology, 5(9), 1037–1042.

    Google Scholar 

  16. Yadav, S., et al. (2017). Crime pattern detection, analysis & prediction. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE.

  17. Wang, X., M.S. Gerber, and Brown D. E. (2018). Automatic crime prediction using events extracted from twitter posts. In International conference on social computing, behavioral-cultural modeling, and prediction. Springer.

  18. El Bour, H. A., et al. (2018). A crime prediction model based on spatial and temporal data. Periodicals of Engineering and Natural Sciences, 6(2), 360–364.

    Article  Google Scholar 

  19. Brantingham, P. J., & Faust, F. L. (1976). A conceptual model of crime prevention. Crime & Delinquency, 22(3), 284–296.

    Article  Google Scholar 

  20. Safat, W., Asghar, S., & Gillani, S. A. (2021). Empirical analysis for crime prediction and forecasting using machine learning and deep learning techniques. IEEE Access, 9, 70080–70094.

    Article  Google Scholar 

  21. DhanaLakshmi, R. A. (2021). Comparative analysis of crime type prediction. Information Technology in Industry, 9(2), 963–967.

    Article  Google Scholar 

  22. Feng, M., et al. (2018). Big data analytics and mining for crime data analysis, visualization and prediction. In International conference on brain inspired cognitive systems. Springer.

  23. Kim, S., et al. (2018). Crime analysis through machine learning. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE.

  24. Agarwal, S., L. Yadav, and Thakur M.K. (2018). Crime Prediction Based on Statistical Models. In 2018 Eleventh International Conference on Contemporary Computing (IC3). IEEE

  25. Iqbal, R., et al. (2013). An experimental study of classification algorithms for crime prediction. Indian Journal of Science and Technology, 6(3), 4219–4225.

    Article  Google Scholar 

  26. Osisanwo, F., et al. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128–138.

    Article  Google Scholar 

  27. Hu, T., et al. (2021) Fully Convolutional Network Variations and Method on Small Dataset. In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE.

  28. Kosarac, A., et al. (2022). Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset. Materials, 15(3), 700.

    Article  Google Scholar 

  29. Ko, S., Choi, J., & Ahn, J. (2021). GVES: Machine learning model for identification of prognostic genes with a small dataset. Scientific Reports, 11(1), 1–8.

    Google Scholar 

  30. Han, J., J. Pei, and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.

  31. Dong, G. and Liu, H. (2018). Feature engineering for machine learning and data analytics. CRC Press.

  32. Kumar, S.V.K.R. (2014). Analysis of feature selection algorithms on classification: a survey.

  33. Demir, S., & Şahin, E. K. (2021). Assessment of feature selection for liquefaction prediction based on recursive feature elimination. Avrupa Bilim ve Teknoloji Dergisi, 28, 290–294.

    Google Scholar 

  34. Hu, L.-Y., et al. (2016). The distance function effect on k-nearest neighbor classification for medical datasets. Springerplus, 5(1), 1–9.

    Article  Google Scholar 

  35. Nayak, J., Naik, B., & Behera, H. (2015). A comprehensive survey on support vector machine in data mining tasks: Applications & challenges. International Journal of Database Theory and Application, 8(1), 169–186.

    Article  Google Scholar 

  36. Sahin, E. K. (2020). Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Applied Sciences, 2(7), 1–17.

    Article  Google Scholar 

  37. Huang, H.-C. (2012). Using artificial neural networks to predict restaurant industry service recovery. International Journal of Advancements in Computing Technology, 4(10), 315–321.

    Article  Google Scholar 

  38. Cios, K. J., Pedrycz, W., & Swiniarski, R. W. (1998). Data mining and knowledge discovery. Data mining methods for knowledge discovery (pp. 1–26). Springer.

    Chapter  Google Scholar 

  39. Lundberg, S.M. and Lee, S.-I. (2017) A unified approach to interpreting model predictions. Advances in neural information processing systems. 30.

  40. Shapley, L. (1953). Quota solutions op n-person games1. Edited by Emil Artin and Marston Morse. p. 343.

  41. Ribeiro, M.T., S. Singh, and Guestrin. C. (2016)." Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining.

  42. Gramegna, A. and Giudici, P. (2021). SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk. Frontiers in Artificial Intelligence p. 140.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalgonj, Bangladesh

    Meherun Nesa & Tumpa Rani Shaha

  2. Department of Computer Engineering, Hongik University, Seoul, Republic of Korea

    Meherun Nesa & Young Yoon

Authors
  1. Meherun Nesa
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  2. Tumpa Rani Shaha
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  3. Young Yoon
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Contributions

MN conceptualized the idea, wrote the code and drafted the manuscript. TRS supervised machine learning algorithm implementation and YY supervised the explainable artificial intelligence implementation. All authors read and approved the final manuscript.

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Correspondence to Meherun Nesa.

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The authors declare no conflicts of interest.

Data and source code availability

The dataset and source code used in this study are available on Github https://github.com/meherun-nesa/Juvenile-Crime-Prediction.git.

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Nesa, M., Shaha, T.R. & Yoon, Y. Prediction of juvenile crime in Bangladesh due to drug addiction using machine learning and explainable AI techniques. J Comput Soc Sc 5, 1467–1487 (2022). https://doi.org/10.1007/s42001-022-00175-7

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  • DOI: https://doi.org/10.1007/s42001-022-00175-7

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