Crime Prediction and Analysis using CNN & RNN

• Author(s): Yashas B ; Srinidheesh M ; Chanackya J ; Sacheet Kumar
• Paper ID: 1707390
• Page: 175-179
• Published Date: 07-03-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 9 March-2025

Abstract

Crime prediction and analysis play a vital role in enhancing public safety and optimizing law enforcement efforts. This study explores deep learning-based approaches, integrating Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks for effective crime forecasting and analysis. The proposed framework leverages the temporal strengths of RNNs and LSTMs alongside the spatial feature extraction capabilities of CNNs to analyze large-scale crime datasets. RNNs and LSTMs handle time-series data to predict future crime trends, while CNNs perform geospatial analysis to identify crime distribution patterns across regions. The hybrid model processes both structured data (e.g., dates, times, locations) and unstructured data (e.g., crime descriptions) to enhance predictive accuracy. Experimental results demonstrate its ability to detect crime hotspots, predict crime categories, and uncover hidden trends, offering actionable insights for law enforcement and policymakers. This study highlights the potential of deep learning in tackling complex, dynamic challenges such as crime prediction, contributing to smarter and safer cities. Future work could incorporate real-time data streams and assess the ethical considerations of deploying such models in decision-making systems

Citations

IRE Journals:
Yashas B , Srinidheesh M , Chanackya J , Sacheet Kumar "Crime Prediction and Analysis using CNN & RNN" Iconic Research And Engineering Journals Volume 8 Issue 9 2025 Page 175-179

IEEE:
Yashas B , Srinidheesh M , Chanackya J , Sacheet Kumar "Crime Prediction and Analysis using CNN & RNN" Iconic Research And Engineering Journals, 8(9)