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，利用机器学习和深度学习技术预测犯罪已引起研究人员的广泛关注，其核心在于识别犯罪事件中的模式与趋势。本综述论文审视了超过150篇文章，探究了应用于犯罪预测的各种机器学习与深度学习算法。本研究提供了研究人员用于犯罪预测的数据集访问途径，并分析了机器学习与深度学习算法中用于预测犯罪的典型方法，揭示了与犯罪活动相关的不同趋势及因素。此外，论文指出了能提升犯罪预测准确性的潜在空白与未来方向。最后，本文关于采用机器学习和深度学习方法进行犯罪预测的研究综述，为该领域研究者提供了宝贵的参考。通过加深对犯罪预测技术的理解，执法机构可以制定更有效的策略来预防和应对犯罪活动。