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@article{172850,
        author = {V. Sri Veda and S.Sunanda and P.Kausar and M.Shohaib and Dr J.Sirisha},
        title = {Intelligent Crime Anomaly Detection In Smart Cities Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {1769-1775},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172850},
        abstract = {With the increasing trend of urbanization, the importance of public safety in the smart city has gained more focus. Conventional systems for video surveillance cannot detect and respond to crime in real time. This paper describes an intelligent crime anomaly detection system based on deep learning techniques for analyzing and understanding large-stacked surveillance data. The proposed approach combines CNNs and RNN networks for the detection of anomalous patterns showing criminal activities. The model is trained using video surveillance datasets to get more accurate detection. Experimental results demonstrate minimal false positives and high precision for the proposed model in the detection of crime anomalies. The research lays the foundation for integrating AI-ready solutions into urban normative infrastructures for future crime-free scenarios due to proactive crime prevention and improved public safety initiatives.},
        keywords = {CNN, RNN, HDL, smart surveillance, Neural networks, IoT, Deep Learning, Artificial Intelligence},
        month = {February},
        }