Authors : Alok Maurya; Aman Jaiswal; Aman Kumar; Abhishek Kumar; Sanjeev Pippal

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/y5hpd4p9

Scribd : https://tinyurl.com/fetw4jez

DOI : https://doi.org/10.38124/ijisrt/25apr1315

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

Abstract : Due to the increasing crime rate all over the world, the new methods are required for the prediction as well as prevention. Introduction of Artificial Intelligence (AI) Crime Detection tool is a strong way to find patterns of prediction of malfeasance by ML model and real-time analytics. Hence, this paper dissect the existing AI-induced structures, which aid in the detection/prediction of crimes along with their architecture, methodology, difficulties, and future direction. Demographic, temporal, and spatial data are obtained, and thus help to improve predictive performance. It also deals with ethical issues like bias reduction, transparency, and data privacy.info The research ends with a list of recommendations suggesting crucial areas for this kind of interdisciplinary cooperation, which is needed to improve the reliability and fairness of computer systems that are deployed in the prevention of crime.

Keywords : AI in Crime detection, Predictive Policing, Machine learning, Ethical AI, Real-Time Analytics.

References :

1. G. A. A. Firdaus, Muhammad Alkaff, “Prediction of Crime Rate in Banjarmasin City Using RNN- GRU Model,” Int J Intell Syst Appl Eng, vol. 10, no. 3, pp. 01–09, 2022.
2. W. Safat, S. Asghar and S. A. Gillani, “Crime Prediction and Forecasting: Empirical Analysis using Machine Learning and Deep Learning Techniques”, IEEE Access, vol. 9.
3. Gaurav Hajela “A Clustering-++Based Hotspot Identification Approach for Crime Prediction”, Procedia Computer Science, vol. 167, 2020.
4. Fatehkia, D. O’Brien and I. Weber, “Correlated Impulses: Using Facebook Interests to Improve Predictions of Crime Rates in Urban Areas”, PLOS ONE, vol. 14, 2019.
5. Fatima Dakalbab , Manar Abu Talib , “A Comprehensive Survey on Crime Forecasting Models Using Artificial Intelligence,”.
6. Yu, L, Ding, R, Sun, K (2020) Deep Learning Approaches for Real-Time Crime Pattern Recognition in Urban Environments. ACM Transactions on Intelligent Systems and Technology (TIST) 11(4): 52. 13, no. 3, pp. 1–19, 2022.
7. Rashid Ahmad 1, Asif Nawaz 1, Ghulam Mustaf a 1, "Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning”.
8. Application of Machine Learning in Forensic Investigations: A Crime Analysis Perspective, vol. 193, pp. 1-14, 2022.
9. Xu Wang, Sen Wang, “Deep Reinforcement Learning: A Survey” IEEE 2022
10. Varun Mandalapu, Lavanya Elluri, “Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions” IEEE 2013
11. M. Anton Permana, Muhammad Ikhsan Thohir, “Crime Rate Detection Based on Text Mining on Social Media Using Logistic Regression Algorithm” IEEE 2022.
12. Somayyeh Aghabaei, Masoud Makrehchi, “Mining Social Media Content for Crime Prediction” IEEE 2017
13. Iqbal Singh Saini, Navneet Kaur, “The Power of Predictive Analytics: Forecasting Crime Trends in High-Risk Areas for Crime Prevention using Machine Learning” IEEE 2023
14. Ankit Sharaff, Pradeep Kumar Kushwaha, Shashi Prakash Dwivedi, Om Krishna, Shivam Singh, Dinesh Thakur, “Crime Rate Prediction Using Machine Learning” IEEE 2024
15. Pradeep Soren Goyal, J. A. Bloom, I. J. Cox and M. Miller, Detection of statistical arbitrage using machine learning techniques in Indian Stock market| IIT Kanpur, April 2013.
16. L’ Heureux, K. Grolinger, H. F. Elyamany and M.A. M. Capretz, "Machine learning with big data: Challenges and approaches", IEEE Access, vol. 5,pp. 7776-7797, 2017.
17. G. O. Mohler, M. B. Short, P. J. Brantingham, F. P. Schoenberg and G. E. Tita, "Self-exciting point process modeling of crime", J. Amer. Stat. Assoc., vol. 106, no. 493, pp. 100-108, Mar. 2011.
18. M. S. Gerber, "Predicting crime using Twitter and kernel density estimation", Decis. Support Syst., vol. 61, pp. 115-125, 2014.
19. P. Kump, D. H. Alonso, Y. Yang, J. Candella, J. Lewin and M.N. Wernick, "Measurement of repeat effects in Chicago's criminalsocial network", Appl. Comput. Informat., vol. 12, no. 2, pp. 154-160, Jul. 2016.
20. P. K. Kushwaha, V. Bibhu, B. P. Lohani and D. Singh, "Review on information security laws and ethical issues with online financial system", 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH), pp. 49-53, 2016.
21. G. Gulati, B. P. Lohani and P. K. Kushwaha, "A Novel Application Of IoT In Empowering Women Safety Using GPS Tracking Module", 2020 Research Innovation Knowledge Management and Technology Application for Business Sustainability (INBUSH) Greater Noida India, pp. 131-137, 2020.