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.
翻译:近年来,运用机器学习和深度学习方法预测犯罪行为引起了广泛的关注,旨在识别犯罪案件中的模式与趋势。本综述分析了150多篇相关文章,探讨了各种机器学习和深度学习算法应用于犯罪预测的研究进展。该研究提供了犯罪预测研究所使用的数据集,并分析了机器学习和深度学习算法中应用广泛的方法,揭示了与犯罪活动相关的不同趋势和因素。此外,本文还概述了可能存在的研究空缺和未来的方向,以提高犯罪预测的准确性。最后,本文全面综述了犯罪预测应用机器学习和深度学习方法的相关研究,为该领域的研究者提供了有价值的参考。通过深入了解犯罪预测技术,执法机构可以制定更有效的预防和应对犯罪活动的策略。