Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm

doi:10.1080/08839514.2024.2428552

	Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm
	Qiu, Dawei 1,2,3; Liu, Chang 1,2,3; Shang, Yuangfeng 1,2,3; Zhao, Zixu 1,2,3,4; Shi, Jinlin 1,2,3
	2024-12-31
发表期刊	APPLIED ARTIFICIAL INTELLIGENCE
ISSN	0883-9514
卷号	38 期号:1 页码:31
摘要	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.
DOI	10.1080/08839514.2024.2428552
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2022YFC3320800]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001357714600001
出版者	TAYLOR & FRANCIS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41185
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shang, Yuangfeng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, 6 Kexueyuan Nanlu, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Qiu, Dawei,Liu, Chang,Shang, Yuangfeng,et al. Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm[J]. APPLIED ARTIFICIAL INTELLIGENCE,2024,38(1):31.
APA	Qiu, Dawei,Liu, Chang,Shang, Yuangfeng,Zhao, Zixu,&Shi, Jinlin.(2024).Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm.APPLIED ARTIFICIAL INTELLIGENCE,38(1),31.
MLA	Qiu, Dawei,et al."Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm".APPLIED ARTIFICIAL INTELLIGENCE 38.1(2024):31.