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 | AI Powered Crime Prediction: Type and Frequency Analysis using Machine Learning |
|---|---|
| ABSTRACT | Crime prevention and public safety have become vital concerns in modern society. With the increasing complexity and frequency of crimes, traditional crime-fighting techniques are insufficient. This research paper explores how Artificial Intelligence (AI) can be leveraged for crime rate detection and prediction. We focus on three powerful machine learning algorithms: Decision Tree Classification, Random Forest, and Logistic Regression. These algorithms are applied to historical crime datasets to detect patterns and predict future crime hotspots. The study aims to develop a predictive framework that can assist law enforcement agencies in better resource allocation and proactive policing Governments and law enforcement organizations are looking for cutting-edge ways to identify, stop, and forecast crimes in light of the escalating rate of criminal activity worldwide. The study on artificial intelligence (AI)-based crime rate detection and prediction is presented in this publication. Decision Tree, Random Forest, and Logistic Regression are machine learning algorithms that are used to identify crime patterns and forecast future trends. To assess the accuracy and efficiency of the suggested models, realworld crime datasets are used for training. According to our findings, AI can greatly support resource allocation, policymaking, and proactive policing. Random Forest exhibits the best prediction accuracy and robustness out of the three models. The development of intelligent systems for improving public safety is aided by this effort. |
| AUTHOR | M SOWMIYA, VIJAY.C, SIVASAKTHI.V, TAMILARASU.S.S, THALAPATHI.G Assistant Professor, Department of Computer Science and Engineering, The Kavery Engineering College, Mecheri, Salem, India Department of Computer Science and Engineering, The Kavery Engineering College, Mecheri, Salem, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404067 |
| pdf/67_AI Powered Crime Prediction Type and Frequency Analysis using Machine Learning.pdf | |
| KEYWORDS | |
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