一种基于Naïve贝叶斯算法的乘客数据分类新方法(一种实时反恐方法)

Saurabh Singh, Shashikant Verma, Akhilesh Tiwari, Aditya Tiwari
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引用次数: 3

摘要

恐怖主义数据挖掘基本上就是从海量的数据中挖掘出恐怖主义的所有数据。以一种更复杂的方式,我们都知道恐怖分子通过火车站、汽车站或机场进入任何主要场所。他们通常使用手机和网络进行交流。现在,如果这些地区配备了局域网或广域网,也就是Wi-Fi连接,这些恐怖分子肯定会利用自己。然后在恐怖分子使用的数据的帮助下,他们的存在可以很容易地被发现,他们的信息可以被收集。本文描述了一种以明确定义的方式对抗公共场所恐怖分子存在的新颖工作。
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A novel way to classify passenger data using Naïve Bayes algorithm (A real time anti-terrorism approach)
Terrorist Data Mining basically means to encounter all the data of terrorism from the huge amount of data. In a more intricate way we all know that terrorist set their foot into any predominant place through railway station, bus stands or airport. Usually to communicate they use their cell phones and network. Now if these areas are well equipped with LAN or WAN, that is the Wi-Fi connections surely these terrorist would avail themselves. Then with the help of data used by terrorists their presence can be spotted easily and their information can be collected. This paper describes a novel work to counter the presence of terrorist at public place in a well-defined manner.
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