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@article{168800,
        author = {T.ANTO TEEPAK and Chandru M},
        title = {PREDICTIVE CRIME ANALYTICS USING DEEP LEARNING APPROACHES FOR CRIME FORECASTING AND PREVENTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {2212-2214},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168800},
        abstract = {This study investigates the application of deep learning techniques and the Random Forest algorithm in predictive crime analytics, aiming to enhance crime forecasting and prevention strategies. Utilizing a comprehensive dataset of historical crime incidents, we assess various deep learning architectures alongside Random Forest to evaluate their predictive capabilities. Our results demonstrate significant improvements in prediction accuracy, providing actionable insights for law enforcement agencies. The findings highlight the potential of machine learning in proactive crime prevention, paving the way for more informed decision-making in public safety.},
        keywords = {Predictive Crime Analytics, Deep Learning, Random Forest, Crime Forecasting, Crime Prevention, Machine Learning},
        month = {October},
        }