Özlem Deniz Başar, E. Genç
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引用次数: 2

摘要

近年来,由于软件和硬件技术的发展,研究中使用的数据集已经扩大,并且随着人工智能技术的影响,预测中使用的模型已经能够获得具有更广泛含义的结果。在这项研究中,使用计算的犯罪指数来揭示各国每年的犯罪率,估计106个国家的安全位置。为此,采用逻辑回归分析、人工神经网络和非分类方法的多准则决策方法之一MOORA方法提供了不同的观点。研究结果表明,人工神经网络方法对国家安全状况的估计分类正确率高于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ÜLKELERİN GÜVENLİ OLMALARININ TAHMİNİNDE LOJİSTİK REGRESYON, YAPAY SİNİR AĞLARI VE MOORA YÖNTEMLERİNİN KARŞILAŞTIRILMASI
In recent years, because of the developments in software and hardware technology, the datasets used in research have expanded, and with the effects of artificial intelligence technologies, the models used in forecasts have enabled to obtain results with broader meanings. In this study, using the crime index calculated to reveal the crime rates in the countries every year, the safety positions of the 106 countries was estimated. For this purpose, logistic regression analysis, artificial neural networks and MOORA method, which is one of the multi-criteria decision making methods and also not a classification method, has been used to provide a different point of view. As a result of the study, it is determined that the correct classification rate of estimations made according to the safety of countries with artificial neural networks method is higher than other methods.
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