International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Crime Prediction using Machine Learning and Deep Learning |
|---|---|
| ABSTRACT | Crime has become a major concern for governments and law enforcement agencies worldwide. The increasing availability of crime-related data and advancements in Artificial Intelligence (AI) have enabled the development of predictive systems capable of forecasting criminal activities. Crime prediction aims to identify potential crime hotspots, estimate crime occurrence probabilities, and support proactive policing strategies. This research presents a comprehensive study of crime prediction using Machine Learning (ML) and Deep Learning (DL) techniques. Various algorithms including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) networks are analyzed for crime forecasting. Historical crime datasets are preprocessed and transformed into meaningful features for model training. Experimental results demonstrate that deep learning models outperform conventional machine learning approaches in capturing complex temporal and spatial crime patterns. The proposed framework assists law enforcement agencies in resource allocation, crime prevention, and decision-making. The study concludes that integrating ML and DL techniques can significantly improve prediction accuracy and contribute to safer and smarter cities. |
| AUTHOR | BOLLU POOJITHA, P. RAJASEKHAR PG Student, Dept. of CSE, Siddartha Educational Academy Group of Institutions, Tirupati, India Assistant Professor, Dept. of CSE(AI&ML), Siddartha Educational Academy Group of Institutions, Tirupati, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406021 |
| pdf/21_Crime Prediction using Machine Learning and Deep Learning.pdf | |
| KEYWORDS | |
| References | 1. Wang, T., Rudin, C., Wagner, D., and Sevieri, R., "Learning to Detect Patterns of Crime," Machine Learning Journal, 2018. 2. Mohler, G., "Predictive Policing and Crime Forecasting," Annual Review of Statistics and Its Application, 2020. 3. Chainey, S., and Ratcliffe, J., GIS and Crime Mapping, Wiley Publications, 2019. 4. Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, MIT Press, 2016. 5. Breiman, L., "Random Forests," Machine Learning Journal, 2001. 6. Hochreiter, S., and Schmidhuber, J., "Long Short-Term Memory," Neural Computation, 1997. 7. LeCun, Y., Bengio, Y., and Hinton, G., "Deep Learning," Nature, 2015. 8. Aggarwal, C.C., Neural Networks and Deep Learning, Springer, 2018. |
