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Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models

Huiping Hu

Huiping Hu

Department of English Education, Shangrao Preschool Education CollegeShangrao, China
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Hu, Huiping
, 
Xinqun Huang

Xinqun Huang

Department of English Education, Shangrao Preschool Education CollegeShangrao, China
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Huang, Xinqun
, 
Majed Ahmad Suhaim

Majed Ahmad Suhaim

Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz UniversityJeddah, Saudi Arabia
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Suhaim, Majed Ahmad
 et 
Hui Zhang

Hui Zhang

Department of English Education, Shangrao Preschool Education CollegeShangrao, China
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Zhang, Hui
  
13 déc. 2021
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Publié en ligne: 13 déc. 2021
Pages: 739 - 750
Reçu: 17 juin 2021
Accepté: 24 sept. 2021
DOI: https://doi.org/10.2478/amns.2021.2.00064
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© 2021 Huiping Hu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Model structure of BPNN. BPNN, backpropagation neural network.
Model structure of BPNN. BPNN, backpropagation neural network.

Fig. 2

Structure of DBM. RBM, restricted Boltzmann machine.
Structure of DBM. RBM, restricted Boltzmann machine.

Fig. 3

Statistics of campus crime in a determinate area.
Statistics of campus crime in a determinate area.

Fig. 4

Simulation results when N = 200, R = 10 and ρ = 0.1. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.
Simulation results when N = 200, R = 10 and ρ = 0.1. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.

Fig. 5

Simulation results when N = 200, R = 10 and ρ = 0.5. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.
Simulation results when N = 200, R = 10 and ρ = 0.5. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.

Fig. 6

Simulation results when N = 400, R = 30 and ρ = 0.1. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.
Simulation results when N = 400, R = 30 and ρ = 0.1. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.

Fig. 7

Simulation results when N = 400, R = 30 and ρ = 0.5. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.
Simulation results when N = 400, R = 30 and ρ = 0.5. (a) The result of ALASSO penalty function; (b) the result of LASSO penalty function; and (c) the result of SCAD penalty function. ALASSO, adaptive least absolute shrinkage and selection operator; LASSO, least absolute shrinkage and selection operator; SCAD, smoothly clipped absolute deviation.

Data parameter estimation of crime influencing factors

Influencing factors @@QMLE LASSO ALASSO SCAD
X1 −0.2174235 −0.1453376 −0.0861383 −0.2133456
X2 0.2461965 0.2118965 0.2431985 0.2265178
X3 0.0051896 0 0 0
X4 −0.1041869 0 0 0
X5 0.1472457 0.00112187 0 0
X6 0.14773986 0.16729365 0.1051986 0.18729353
X7 −0.2786496 −0.31298654 0 −0.2825
X8 0.14085674 0.05428654 0 0
X9 0.1751896 0.18532187 0.1507659 0
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