In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 5000 news manually compiled for this purpose. The results reveal that both support vector machine and multinomial Naïve Bayes classifiers exhibit almost 90% accuracy. Finally, a generic method for calculating security profile of a city or a region has been developed, which is augmented by visualization and reporting components that maps this information onto maps using geographical information system.

中文翻译：

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使用机器学习方法进行智能城市安全分析的信息集成架构框架

在过去的几十年里，全世界都受到恐怖主义和其他治安局势的严重影响。这些报纸一直在报道恐怖主义和其他法律和秩序问题，并提供相关细节。然而，据我们所知，没有现有的信息系统能够积累和分析这些事件，以帮助制定策略来避免和减少未来此类事件的发生。本研究旨在提供一个通用的架构框架，通过不同的新闻门户网站半自动地积累与法律和秩序相关的新闻，并使用机器学习方法对其进行分类。提议的架构框架讨论了所有重要组件，包括数据摄取、预处理器、报告和可视化以及模式识别。已经实现了信息提取器和新闻分类器，其中分类子组件采用广泛使用的文本分类器，用于包含为此目的手动编译的近 5000 条新闻的新闻数据集。结果表明，支持向量机和多项式朴素贝叶斯分类器都表现出近 90% 的准确率。最后，开发了一种计算城市或地区安全概况的通用方法，该方法通过可视化和报告组件得到增强，这些组件使用地理信息系统将此信息映射到地图上。结果表明，支持向量机和多项式朴素贝叶斯分类器都表现出近 90% 的准确率。最后，开发了一种计算城市或地区安全概况的通用方法，该方法通过可视化和报告组件得到增强，这些组件使用地理信息系统将此信息映射到地图上。结果表明，支持向量机和多项式朴素贝叶斯分类器都表现出近 90% 的准确率。最后，开发了一种计算城市或地区安全概况的通用方法，该方法通过可视化和报告组件得到增强，这些组件使用地理信息系统将此信息映射到地图上。