Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm
Crime type identification is crucial for improving public safety through more accurate prevention and efficient responses. However, practical applications often suffer from a significant lack of effective samples features, making it difficult to focus on the most informative aspects during identific...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2428552 |
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| _version_ | 1849220179115900928 |
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| author | Dawei Qiu Chang Liu Yuangfeng Shang Zixu Zhao Jinlin Shi |
| author_facet | Dawei Qiu Chang Liu Yuangfeng Shang Zixu Zhao Jinlin Shi |
| author_sort | Dawei Qiu |
| collection | DOAJ |
| description | Crime type identification is crucial for improving public safety through more accurate prevention and efficient responses. However, practical applications often suffer from a significant lack of effective samples features, making it difficult to focus on the most informative aspects during identification. This study addresses these challenges by proposing a novel crime type identification method that leverages a deep neural network enhanced with multiple attention mechanisms. The approach includes a tailored data processing method involving target encoding to convert categorical data into numerical form, L2 normalizer to standardize data and ensure balanced feature contribution, and variance threshold feature selection to remove low-variance features. Additionally, a High-Order Deep Residual Network with Multiple Attention (HO-ResNet-MA) is developed, featuring an optimized Huta68 block (Huta-6(8)-MA ResBlock) with an enhanced Contextual Transformer (CoT) unit for local attention and a queue-and-exclusion layer for global attention. To validate the effectiveness of the proposed method, homicide reports data and Chicago crimes data are processed and fed into the crime type identification model, resulting in accuracies of over 84.1% and 99.5%, respectively. This study makes contributions to the field of crime analysis by validating the practical applicability of these approaches, and enhancing the efficiency of public safety workers. |
| format | Article |
| id | doaj-art-15bbdec7516e4bbfb7e8282eb97d9da7 |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-15bbdec7516e4bbfb7e8282eb97d9da72024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2428552Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention AlgorithmDawei Qiu0Chang Liu1Yuangfeng Shang2Zixu Zhao3Jinlin Shi4State Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, ChinaState Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, ChinaState Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, ChinaState Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, ChinaState Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, ChinaCrime type identification is crucial for improving public safety through more accurate prevention and efficient responses. However, practical applications often suffer from a significant lack of effective samples features, making it difficult to focus on the most informative aspects during identification. This study addresses these challenges by proposing a novel crime type identification method that leverages a deep neural network enhanced with multiple attention mechanisms. The approach includes a tailored data processing method involving target encoding to convert categorical data into numerical form, L2 normalizer to standardize data and ensure balanced feature contribution, and variance threshold feature selection to remove low-variance features. Additionally, a High-Order Deep Residual Network with Multiple Attention (HO-ResNet-MA) is developed, featuring an optimized Huta68 block (Huta-6(8)-MA ResBlock) with an enhanced Contextual Transformer (CoT) unit for local attention and a queue-and-exclusion layer for global attention. To validate the effectiveness of the proposed method, homicide reports data and Chicago crimes data are processed and fed into the crime type identification model, resulting in accuracies of over 84.1% and 99.5%, respectively. This study makes contributions to the field of crime analysis by validating the practical applicability of these approaches, and enhancing the efficiency of public safety workers.https://www.tandfonline.com/doi/10.1080/08839514.2024.2428552 |
| spellingShingle | Dawei Qiu Chang Liu Yuangfeng Shang Zixu Zhao Jinlin Shi Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm Applied Artificial Intelligence |
| title | Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm |
| title_full | Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm |
| title_fullStr | Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm |
| title_full_unstemmed | Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm |
| title_short | Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm |
| title_sort | crime type identification using high order deep residual network with multiple attention algorithm |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2428552 |
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