Computer modeling based on a neural network as a tool for obtaining criminologically significant information on assessing the state of crime: based on the materials of the Republic of Kazakhstan

IF 0.1 Q4 LAW
Aleksandr Bashirov, T. Volchetskaya, B. Nurgaliyev, T. Khanov
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Abstract

The purpose of the study is to obtain the missing information related to the assessment of the degree of influence of changes in some socio-economic factors on the state of crime and its individual manifestations. The motivation for the study is related to the initiative of the President of the Republic of Kazakhstan Kassym Jomart Tokayev to increase the minimum wage. The justification for the increase in the minimum wage was a calculation that showed an increase in gross domestic product by 1.5%. The method of research is computer modeling based on the functioning of a neural network. The creation and training of the neural network was based on official statistical data of the Republic of Kazakhstan. At the initial stage of the study, the effectiveness of the chosen method was tested in comparison with other methods of information processing and analysis. The test showed a higher accuracy of calculation using a computer neural network. The dependence of changes in the minimum wage with an increase in gross domestic product and a decrease in the crime rate was confirmed. At the next, main stage of the study, there was a need to improve the neural network by optimizing the input matrix intended for training. Optimization lies in the fact that the learning matrix was formed by those values of socio-economic parameters, the impact of which on the level of crime and crime associated with manifestations of terrorism and extremism is maximal. Test comparisons of calculation results using neural network training optimization showed more accurate data compared to standard neural network training. Using a more advanced neural network, modeling of the expanded impact of changes in the minimum wage on the level of crime and criminal activity of an extremist and terrorist nature was carried out. The simulation results showed that the dependence of changes in the crime rate on changes in the minimum wage has a more complex nature of influence, in which it is important to determine its optimal value. The increase in the minimum wage has practically no effect on crimes of special gravity. They can mitigate the manifestations of peak activity of crimes of particular severity, but they do not have a significant impact on this type of crime. The research group notes the universality of the described software tools. Its application can be significantly expanded and used in various applications related to law enforcement. The authors declare no conflicts of interests.
基于神经网络的计算机建模,作为获取评估犯罪状况的犯罪学重要信息的工具:基于哈萨克斯坦共和国的材料
这项研究的目的是获得与评估某些社会经济因素的变化对犯罪状况及其个别表现的影响程度有关的缺失信息。这项研究的动机与哈萨克斯坦共和国总统卡西姆·乔马特·托卡耶夫提高最低工资的倡议有关。根据一项计算,提高最低工资的理由是国内生产总值(gdp)将增长1.5%。研究方法是基于神经网络功能的计算机建模。神经网络的创建和训练是基于哈萨克斯坦共和国的官方统计数据。在研究的初始阶段,将所选择的方法与其他信息处理和分析方法进行比较,以检验其有效性。实验表明,利用计算机神经网络进行计算具有较高的精度。最低工资的变化与国内生产总值的增加和犯罪率的下降之间的依赖关系已得到证实。在研究的下一个主要阶段,需要通过优化用于训练的输入矩阵来改进神经网络。优化在于学习矩阵是由社会经济参数的值构成的,社会经济参数对犯罪水平和与恐怖主义和极端主义表现相关的犯罪水平的影响是最大的。使用神经网络训练优化计算结果的测试对比显示,与标准神经网络训练相比,数据更加准确。使用更先进的神经网络,对最低工资变化对犯罪水平和极端主义和恐怖主义性质的犯罪活动的扩大影响进行了建模。仿真结果表明,犯罪率变化对最低工资变化的依赖性具有更为复杂的影响性质,其中确定最低工资的最优值十分重要。提高最低工资实际上对特别严重的犯罪没有任何影响。它们可以减轻特定严重程度的犯罪活动高峰的表现,但它们对这类犯罪没有显著影响。研究小组注意到所描述的软件工具的通用性。它的应用可以大大扩展,并用于与执法有关的各种应用。作者声明没有利益冲突。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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