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 | An Advanced Crime Rate Prediction Framework Using Data Mining and Deep Learning Techniques |
|---|---|
| ABSTRACT | Rapid urban growth and increasing population density have resulted in a noticeable rise in crime incidents, creating significant challenges for public safety and law enforcement agencies. Accurate prediction of crime rates plays a vital role in enabling proactive policing, efficient resource allocation, and effective crime prevention strategies. Conventional statistical and rule-based approaches are often inadequate for analyzing large, complex, and heterogeneous crime datasets. This paper introduces an advanced crime rate prediction framework that integrates data mining techniques with deep learning models to enhance prediction accuracy and reliability. The proposed methodology incorporates data preprocessing and feature engineering to identify meaningful crime patterns, followed by the application of machine learning classifiers and Long Short-Term Memory (LSTM) networks for temporal crime forecasting. Experimental evaluation conducted on real-world crime datasets demonstrates that the proposed hybrid framework outperforms traditional machine learning models in terms of accuracy and robustness. The findings confirm that deep learning-based crime prediction systems can significantly support smart policing and smart city initiatives. |
| AUTHOR | PROF. VISHAL PARANJAPE Department of Computer Science and Engineering, Baderia Global Institute of Engineering & Management, Jabalpur, Madhya Pradesh, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401039 |
| pdf/39_An Advanced Crime Rate Prediction Framework Using Data Mining and Deep Learning Techniques.pdf | |
| KEYWORDS | |
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