Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models
To reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2021-12-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2021.2.00064 |
| _version_ | 1797800844664504320 |
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| author | Hu Huiping Huang Xinqun Suhaim Majed Ahmad Zhang Hui |
| author_facet | Hu Huiping Huang Xinqun Suhaim Majed Ahmad Zhang Hui |
| author_sort | Hu Huiping |
| collection | DOAJ |
| description | To reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent crimes are discussed. By taking campus violence cases as examples, the major influencing factors of violent crimes are found through data analysis. The results show that campus violence cases are usually caused by the complex surrounding environments and persons. Also, campus security measures only cover a small range, and the security management is difficult. In the meantime, due to the younger ages and lack of self-protection awareness, students may easily become the targets of criminals. Therefore, the results have a positive significance for authorities to analyse the crime rates in a determinate area and take preventive measures against violent crimes. |
| first_indexed | 2024-03-13T04:41:26Z |
| format | Article |
| id | doaj.art-76f13c1ae5dd4971acd3ea56b6af54cf |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-13T04:41:26Z |
| publishDate | 2021-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-76f13c1ae5dd4971acd3ea56b6af54cf2023-06-19T05:54:32ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562021-12-017173975010.2478/amns.2021.2.00064Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive modelsHu Huiping0Huang Xinqun1Suhaim Majed Ahmad2Zhang Hui3Department of English Education, Shangrao Preschool Education College,Shangrao334000, ChinaDepartment of English Education, Shangrao Preschool Education College,Shangrao334000, ChinaDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of English Education, Shangrao Preschool Education College,Shangrao334000, ChinaTo reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent crimes are discussed. By taking campus violence cases as examples, the major influencing factors of violent crimes are found through data analysis. The results show that campus violence cases are usually caused by the complex surrounding environments and persons. Also, campus security measures only cover a small range, and the security management is difficult. In the meantime, due to the younger ages and lack of self-protection awareness, students may easily become the targets of criminals. Therefore, the results have a positive significance for authorities to analyse the crime rates in a determinate area and take preventive measures against violent crimes.https://doi.org/10.2478/amns.2021.2.00064deep learningcampus violenceautoregressionpenalty functionparameter estimation62j05 |
| spellingShingle | Hu Huiping Huang Xinqun Suhaim Majed Ahmad Zhang Hui Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models Applied Mathematics and Nonlinear Sciences deep learning campus violence autoregression penalty function parameter estimation 62j05 |
| title | Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models |
| title_full | Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models |
| title_fullStr | Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models |
| title_full_unstemmed | Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models |
| title_short | Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models |
| title_sort | comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models |
| topic | deep learning campus violence autoregression penalty function parameter estimation 62j05 |
| url | https://doi.org/10.2478/amns.2021.2.00064 |
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