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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63613
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING TECHNOLOGY

MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING TECHNOLOGY
Crime prevention and ensuring security is one of the most important problems that modern societies are facing today. Conventional systems of crime investigation and analysis depend mostly on past data analysis, which can prove to be inefficient. This paper presents an Artificial Intelligence (AI) based crime prediction and prevention system that make use of machine learning and data analytics to recognize trends in criminal activity and forecast potential crime hotspots. The proposed system uses historical crime statistics in conjunction with data combined with demographic and temporal factors to train predictive models capable of estimating crime probability in specific locations and time frames. By applying supervised learning algorithms such as Random Forest, Decision Trees, and Neural Networks, the system aims to assist law enforcement agencies in proactive policing and efficient resource allocation. Experimental results demonstrate that AI-based crime prediction can significantly improve accuracy and support timely preventive actions.
KEYWORDS
The AI-based crime prediction and prevention system described in this research makes use of machine learning techniques to predict crime trends and support proactive police. When compared to conventional crime analysis techniques, the suggested method shows increased accuracy and provides useful assistance for law enforcement decision-making. Future enhancements include: • Integration of real-time data streams • Deployment as a web or mobile application • Incorporation of deep learning and spatial-temporal models • Addressing ethical, legal, and privacy challenges
[1] Chen, J., Zhang, S., & Li, Q. (2020). Predictive Policing Using Machine Learning: A Review of Data-Driven Crime Analysis Techniques. IEEE Access, 8, 185907–185923. [2] Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P., & Tita, G. E. (2015). Randomized Controlled Field Trials of Predictive Policing. Journal of the American Statistical Association, 110(512), 1399–1411. [3] Wang, T., & Brown, D. E. (2018). The Spatio-Temporal Modeling for Crime Prediction Using Deep Learning. IEEE Transactions on Systems, Man, and Cybernetics, 48(9), 1–12. [4] Lin, Y., & Hsu, W. (2019). Deep Neural Network Framework for Predicting Urban Crime Patterns. Expert Systems with Applications, 136, 250–261. [5] Chainey, S., & Ratcliffe, J. (2013). GIS and Crime Mapping. John Wiley & Sons. [6] Gerber, M. S. (2014). Predicting Crime Using Twitter and Machine Learning. Decision Support Systems, 61, 115–125. [7] Abdallah, M., Mohammed, F., & Khan, S. (2021). Artificial Intelligence Applications in Predictive Policing and Public Safety Management. International Journal of Computer Applications, 183(41),
Publication Date
5/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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