Current Volume 8
For law enforcement organizations to maintain public safety and effectively deploy resources, crime prediction and forecasting are essential duties. In order to produce precise crime forecasts, this study offers a novel strategy that blends machine learning and deep learning methodologies. In order to take use of their complementing strengths in recognizing crime patterns and modeling sequential data, we suggest combining k-means clustering with Long Short-Term Memory (LSTM) networks. The first step in the procedure is gathering and preparing various crime data, such as demographic data and crime reports. The chronological, spatial, and social components of the extracted variables offer important insights into criminal incidents. Similar crime data points are organized into clusters using k-means clustering, which indicates unique crime patterns or hotspots. After these clusters are converted into sequential data, LSTM models can use historical crime sequences to forecast future crime patterns. In terms of crime prediction and forecasting, the hybrid approach shows encouraging results, giving law enforcement useful information to stop and efficiently address criminal activity. However, while using sensitive crime-related data, ethical issues and data protection laws should be strictly followed.
Crime Prediction, Machine Learning, Analysis and Forecast, Metropolitan Safety
IRE Journals:
Karthikeyan M , Naveen Kumar N , Maruthupandi M
"Hybrid Machine Learning Approach for Crime Prediction and Forecasting: Integrating K-means Clustering and LSTM Networks" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 99-105
IEEE:
Karthikeyan M , Naveen Kumar N , Maruthupandi M
"Hybrid Machine Learning Approach for Crime Prediction and Forecasting: Integrating K-means Clustering and LSTM Networks" Iconic Research And Engineering Journals, 8(10)