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@article{193431,
        author = {D Shivaram Goud and D Chinmayi and K Tarun Babu},
        title = {Crime pattern prediction and analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {335-340},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193431},
        abstract = {In recent years, crime rates have been rising globally, posing significant challenges for law enforcement agencies in identifying and preventing criminal activities. Traditional crime analysis methods often struggle to detect hidden patterns and predict future crimes effectively. This project presents a comprehensive, data-driven approach to crime analysis and prevention by integrating advanced data mining techniques with reinforcement learning(RL) and causal inference models. The system leverages historical crime data to not only identify key crime patterns and detect organized crime networks but also to learn optimal resource allocation strategies through RL, enabling dynamic and real-time crime prediction.Furthermore, the incorporation of a causal inference model based on the PC algorithm uncovers hidden causal relationships among socio-economic, demographic, and environmental factors that drive criminal behavior. The predictive models are rigorously trained and validated using performance metrics to ensure accuracy and interpretability. By leveraging these data-driven insights, the project aims to empower law enforcement agencies with proactive strategies for crime prevention, ultimately improving public safety and mitigating criminal activities.},
        keywords = {Crime prediction, crime detection, crime datasets, deep learning, machine learning, smart  policing, survey},
        month = {March},
        }