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@article{201153,
        author = {B.Vikram and B.Nanda Kishore Reddy and B.Gopi and Dr.S.Anu Priya},
        title = {AI-BASED CRIME PREDICTION AND REAL-TIME SURVEILLIANCE SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3161-3171},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201153},
        abstract = {The proposed Crime Analysis and Prediction System is an AI-powered platform designed to analyze crime patterns and forecast potential criminal activities using machine learning techniques. The system utilizes a synthetic dataset of crime records across major Indian cities, incorporating temporal and geographical features for realistic modeling. It employs algorithms such as Random Forest and XGBoost for accurate crime prediction, while K-Means clustering is used to identify crime hotspots. A Flask-based web application provides an interactive dashboard for visualization, including crime trends, city-wise distribution, and real-time analytics. Additionally, a video surveillance module integrates OpenCV and YOLOv8 for live monitoring, motion detection, and anomaly alerts. The system enhances decision-making for law enforcement by offering predictive insights, risk assessment, and geographic crime mapping, ultimately contributing to improved public safety and smarter urban crime management strategies.},
        keywords = {Crime Analysis, Crime Prediction, Machine Learning, Random Forest, XGBoost, K-Means Clustering, Crime Hotspots, Data Analytics, Flask Web Application, Predictive Modeling, Geographic Information Systems (GIS), Time Series Analysis, Artificial Intelligence, Video Surveillance, OpenCV, Anomaly Detection, Mapping, Public Safety},
        month = {May},
        }