A Comprehensive Review on AI driven Technologies and Computer Devices used for Analysing Crime Patterns
Abstract
Along with the use of sophisticated computer devices, the application of machine learning algorithms and data mining tools is changing the way crime analysis is conducted from a reactive to a proactive approach. This study aims to highlight the different approaches to crime pattern analysis with the aid of algorithms such as classification, clustering, and deep learning techniques to obtain valuable information from enormous amounts of historical data and geospatial data. These algorithms help to accurately identify the crime pattern with the aid of predictions to ensure the efficient allocation of resources to prevent crimes. Computer vision algorithms also help to monitor crimes with the aid of intelligent surveillance systems. Furthermore, the use of high-performance computers and mobile devices is helping to efficiently process crime data. However, there are certain challenges to be addressed with the aid of algorithms such as data quality issues, bias, privacy concerns, interoperability issues, and the high cost of implementation. Moreover, the complexity of cybercrime requires the implementation of robust forensic systems to effectively tackle the issue of cybercrime with the aid of algorithms such as deep learning algorithms.References
[1] David, H. and Suruliandi, A., 2017. Survey on crime analysis and prediction using data mining techniques. ICTACT Journal on Soft Computing, 7(3), pp.1496–1502.
[2] Waduge, N.D. and Ranathunga, L., 2017. Machine learning approaches for detecting crime patterns. Available at: https://www.researchgate.net
[3] Shah, N., Bhagat, N. and Shah, M., 2021. Machine learning and computer vision approach to crime prediction and prevention. Visual Computing for Industry, Biomedicine and Art, 4(1), p.9. https://doi.org/10.1186/s42492-021-00074-8
[4] Shabu, S.J. et al., 2025. Crime analysis and detection based on location using machine learning. In: AIP Conference Proceedings, 3257(1), p.020033. https://doi.org/10.1063/5.020033
[5] Rigano, C., 2019. Using artificial intelligence to address criminal justice needs. National Institute of Justice Journal, 280, pp.1–10.
[6] Sudha, T.S. and Rupa, C., 2019. Analysis and evaluation of integrated cyber crime offences. In: 2019 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, pp.1–6. https://doi.org/10.1109/I-PACT44901.2019
[7] Faqir, R.S., 2023. Digital criminal investigations in the era of artificial intelligence: A comprehensive overview. International Journal of Cyber Criminology, 17(2), pp.77–94.
[8] Naik, F. et al., 2024. Leveraging smart computing for effective crime spot alert system. In: Proceedings of the International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM). IEEE, pp.1–5.
[9] Jones, G.M. and Winster, S.G., 2021. Analysis of crime report by data analytics using Python. In: Challenges and Applications of Data Analytics in Social Perspectives. IGI Global, pp.54–79.
[10] Mishra, A. et al., 2021. Network forensics: An approach towards detecting cyber crime. In: International Conference on Advances in Power, Signal, and Information Technology (APSIT). IEEE, pp.1–6.
[11] Asor, J. et al., 2022. Building model for crime pattern analysis through machine learning using predictive analytics. International Journal of Science, Technology, Engineering and Mathematics, 2(1), pp.61–73.
[12] Lekha, K.C. and Prakasam, S., 2017. Data mining techniques in detecting and predicting cyber crimes in banking sector. In: International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, pp.1639–1643. https://doi.org/10.1109/ICECDS.2017.8389725
[13] Ogli, M.Q.D., 2024. Modern technical devices in crime detection. World Bulletin of Management and Law, 37, pp.17–20.
[14] William, P. et al., 2023. Crime analysis using computer vision approach with machine learning. In: Marriwala, N. et al. (eds.) Mobile Radio Communications and 5G Networks. Singapore: Springer, pp.XXX–XXX. https://doi.org/10.1007/978-981-19-7982-8_25
[15] Khan, A., Chandran, N. and Gangodkar, D., 2025. Geospatial analysis of crime patterns using deep learning algorithms. EDPACS, pp.1–14. https://doi.org/10.1080/07366981.2025.2545354
[16] Eissa, A., Cheng, X. and Petridis, M., 2018. Crime pattern recognition based on high-performance computing. In: Next Generation Community Policing Conference, Heraklion, Greece, pp.1–6.
[17] Gupta, J.K. et al., 2024. Predictive analysis of crime rates using machine learning algorithms. In: International Conference on Innovative Sustainable Computational Technologies (CISCT). IEEE, pp.1–6. https://doi.org/10.1109/CISCT62494.2024.11134190
[18] Nath, S.V., 2006. Crime pattern detection using data mining. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp.41–44. https://doi.org/10.1109/WI.2006.62.
[19] Yu, C.H., Ward, M.W., Morabito, M. and Ding, W., 2011. Crime forecasting using data mining techniques. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp.779–786. https://doi.org/10.1109/ICDMW.2011.108
[20] Okmi, M. et al., 2023. A systematic review of mobile phone data in crime applications. Sensors, 23(9), p.4350. https://doi.org/10.3390/s23094350
[21] Suganthi, P., Aiswarya, S.M. and Kaviyanjali, M., 2024, April. Evaluating Crime Type And Crime Pattern Analysis Using Machine Learning. In 2024, IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1-6). IEEE
[22] Pramanik, M.I., Lau, R.Y. and Chowdhury, M.K.H., 2016. Automatic crime detector: A framework for criminal pattern detection in big data era.
[23] Thongsatapornwatana, U., 2016. A survey of data mining techniques for analyzing crime patterns. In: Asian Conference on Defence Technology (ACDT). IEEE, pp.123–128.
[24] García, G. et al., 2019. CrimAnalyzer: Understanding crime patterns in São Paulo. IEEE Transactions on Visualization and Computer Graphics, 27(4), pp.2313–2328.
[25] Yadav, S. et al., 2017. Crime pattern detection, analysis and prediction. In: International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, pp.225–230.
[26] Kadar, C. and Pletikosa, I., 2018. Mining large-scale human mobility data for long-term crime prediction. EPJ Data Science, 7(1), p.26. https://doi.org/10.1140/epjds/s13688-018-0150-3
[27] Prabakaran, S. and Mitra, S., 2018. Survey of crime detection techniques using data mining and machine learning. Journal of Physics: Conference Series, 1000(1), p.012046.
[28] Chauhan, C. and Sehgal, S., 2017. Crime analysis using data mining techniques and algorithms. In: International Conference on Computing, Communication and Automation (ICCCA). IEEE, pp.21–25.
[29] Hussain, F.S. and Aljuboori, A.F., 2021. Survey on crime analysis using data mining based on mobile platforms. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(1), pp.36–44. https://doi.org/10.29304/jqcm.2021.13.1.770
[30] Ku, C.H. and Leroy, G., 2014. A decision support system: Automated crime report analysis and classification for e-government. Government Information Quarterly, 31(4), pp.534–544.
[31] Ilias, L. et al., 2023. Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: The VesselAI architecture. Frontiers in Big Data, 6, p.1220348. https://doi.org/10.3389/fdata.2023.1220348Jung Hwan Kim, Alwin Poulose, Dong Seog Han, "The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance", Sensors, vol.21, no.6, pp.2026, 2021.
[32] Anderez, D.O. et al., 2021. The rise of technology in crime prevention: Opportunities and challenges. Available at: https://arxiv.org/abs/2102.04204
[33] Kim, J.H., Poulose, A. and Han, D.S., 2021. The extensive usage of facial image processing for emotion recognition. Sensors, 21(6), p.2026.
[34] Shenoy, M.V. et al., 2021. A holistic framework for crime prevention with emphasis on women safety. IEEE Access, 9, pp.66188–66207.
[35] Reilly, D., Wren, C. and Berry, T., 2010. Cloud computing: Forensic challenges for law enforcement. In: International Conference for Internet Technology and Secured Transactions, pp.1–7.
[36] AL-Tkhayneh, K.M. et al., 2023. Crime in the metaverse: Future scenarios and legal challenges. In: International Conference on Social Networks Analysis (SNAMS). IEEE, pp.1–6.
[37] Grover, T. et al., 2023. Deep learning for crime pattern recognition. In: International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, pp.1–6.
[38] Smith, G.J.D., Moses, L.B. and Chan, J., 2017. The challenges of doing criminology in the big data era. British Journal of Criminology, 57(2), pp.259–274.
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
