Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions
Прогнозирование преступности с использованием машинного обучения и глубокого обучения: систематический обзор и перспективные направления
2023-01-01
SCID: 54.1/xm3763rn
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Abstract (AI)
Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.
Key Findings
1
The paper highlights research gaps and future directions aimed at improving crime-prediction accuracy.
2
The review catalogs datasets used in crime-prediction research and summarizes prominent algorithmic approaches.
3
The review provides a consolidated reference that may support more effective law-enforcement prevention and response strategies.
4
The surveyed literature identifies patterns, trends, and factors associated with criminal activity that inform predictive modeling.
5
The systematic review examines more than 150 studies applying machine learning and deep learning to crime prediction.
Research Object
crime occurrences and criminal activities
Research Subject
patterns, trends, and predictive factors in crime occurrence, including the machine-learning and deep-learning approaches used to identify them
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Publication Date
2023-01-01
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