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<p>Different parts of the world have recorded an escalating number of criminal incidents, burdened the judicial system, and adversely impacted national security and economic development. Accurate prediction of crimes is crucial for law enforcement agencies to prevent proactively criminal activity and allocate resources effectively. Existing methods often address architecture design and hyperparameter tuning as separate processes. We present a combination of neural architecture search and hyperparameter tuning for improved crime prediction. The study method achieved automation of architecture discovery and fine-tuning of hyperparameters by utilizing Neural Architecture Search (NAS) to explore a wide range of neural network architectures for crime prediction and optimizing the hyperparameters of the discovered architecture for peak performance in binary crime prediction, respectively. The study used three datasets: criminal cases dataset (self-collected dataset), Vancouver crime data, and Austin crime data. The criminal cases dataset is extracted from a confidential database from certain countries, focusing on sensitive parts of those countries. The Vancouver crime and Austin crime datasets were sourced from the Kaggle website. The study considered the robust rank aggregation (RRA) feature selection method to rank and select the best features to predict crime behavior in some countries. The chosen features using robust rank aggregation included current position, age range, month, prisoner condition, and identified/unidentified (ide/unide). The hyperparameter tuning model of Architecture Search (NAS +) produced superior results across all datasets with an accuracy of 89.29% (AUC-ROC = 94.82% and recall = 64.54%) in the criminal cases dataset, 60.37% (AUC-ROC = 50.00% and recall = 100.00%) in the Vancouver dataset, and 86.68% (AUC_ROC = 65.40% and recall = 100.00%) in the findings which demonstrated that the proposed approach consistently outperforms conventional methods, making it an effective solution for the prediction of real-world crimes.</p>

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Improved Crime Prediction Using Hybrid Neural Architecture Search Together with Hyperparameter Tuning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

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  • Rami Ayied Alshahrani,
  • Tariq Jamil Saifullah Khanzada

摘要

Different parts of the world have recorded an escalating number of criminal incidents, burdened the judicial system, and adversely impacted national security and economic development. Accurate prediction of crimes is crucial for law enforcement agencies to prevent proactively criminal activity and allocate resources effectively. Existing methods often address architecture design and hyperparameter tuning as separate processes. We present a combination of neural architecture search and hyperparameter tuning for improved crime prediction. The study method achieved automation of architecture discovery and fine-tuning of hyperparameters by utilizing Neural Architecture Search (NAS) to explore a wide range of neural network architectures for crime prediction and optimizing the hyperparameters of the discovered architecture for peak performance in binary crime prediction, respectively. The study used three datasets: criminal cases dataset (self-collected dataset), Vancouver crime data, and Austin crime data. The criminal cases dataset is extracted from a confidential database from certain countries, focusing on sensitive parts of those countries. The Vancouver crime and Austin crime datasets were sourced from the Kaggle website. The study considered the robust rank aggregation (RRA) feature selection method to rank and select the best features to predict crime behavior in some countries. The chosen features using robust rank aggregation included current position, age range, month, prisoner condition, and identified/unidentified (ide/unide). The hyperparameter tuning model of Architecture Search (NAS +) produced superior results across all datasets with an accuracy of 89.29% (AUC-ROC = 94.82% and recall = 64.54%) in the criminal cases dataset, 60.37% (AUC-ROC = 50.00% and recall = 100.00%) in the Vancouver dataset, and 86.68% (AUC_ROC = 65.40% and recall = 100.00%) in the findings which demonstrated that the proposed approach consistently outperforms conventional methods, making it an effective solution for the prediction of real-world crimes.