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@article{177703,
        author = {Mubaraali L and THILAGAVATHI A and MUNIYAPPAN R and NANDESHWARAN N and PRATHEEP G},
        title = {Predictive Crime Type Occurrence via Machine Learning Paradigms: A Comprehensive Spatiotemporal Analysis and Modeling Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2703-2709},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177703},
        abstract = {Crime prediction and prevention have become critical areas of research due to the increasing availability of crime-related data and advancements in machine learning (ML). This paper presents a comprehensive spatiotemporal analysis and modeling approach to predict crime-type occurrences using various ML paradigms. We explore different feature selection methods, classification techniques, and time-series forecasting models to enhance predictive accuracy. Additionally, we discuss the impact of socioeconomic and environmental factors on crime patterns. Our results demonstrate the effectiveness of ML models in identifying crime trends and aiding law enforcement agencies in proactive policing. We also analyze the ethical considerations surrounding predictive policing, ensuring that the implementation of AI-driven crime models aligns with societal and legal expectations.},
        keywords = {Crime prediction, Machine learning, Spatiotemporal analysis, Crime modeling, Predictive policing, AI ethics, Crime forecasting},
        month = {May},
        }