论文标题
Twitter驱动的深度学习机制，用于确定城市的车辆劫持位置
A Twitter-Driven Deep Learning Mechanism for the Determination of Vehicle Hijacking Spots in Cities
论文作者
论文摘要
车辆劫持是许多城市的主要犯罪之一。例如，在南非，驾驶员必须在道路上保持警惕，以确保他们不会成为劫机受害者。这项工作旨在通过使用Twitter数据来开发描绘城市中劫持景点的地图。在这项工作中，在开普敦指定城市获得了包括关键字“劫持”的推文。为了提取相关的推文，通过使用以下机器学习技术来分析这些推文：1）多层馈送前馈神经网络（MLFNN）； 2）卷积神经网络；来自变形金刚（BERT）的双向编码器表示。通过培训和测试，CNN的准确度为99.66％，而MLFNN和BERT的准确率分别达到98.99％和73.99％。在召回，精度和F1得分方面，CNN也取得了最佳结果。因此，CNN用于识别相关推文。相关的报道说，它在开普敦市的点地图上进行了视觉介绍。这项工作使用了426个推文的小数据集。将来，将探索进化计算的使用，以优化深度学习模型。正在开发移动应用程序，以使该信息可由公众使用。
Vehicle hijacking is one of the leading crimes in many cities. For instance, in South Africa, drivers must constantly remain vigilant on the road in order to ensure that they do not become hijacking victims. This work is aimed at developing a map depicting hijacking spots in a city by using Twitter data. Tweets, which include the keyword "hijacking", are obtained in a designated city of Cape Town, in this work. In order to extract relevant tweets, these tweets are analyzed by using the following machine learning techniques: 1) a Multi-layer Feed-forward Neural Network (MLFNN); 2) Convolutional Neural Network; and Bidirectional Encoder Representations from Transformers (BERT). Through training and testing, CNN achieved an accuracy of 99.66%, while MLFNN and BERT achieve accuracies of 98.99% and 73.99% respectively. In terms of Recall, Precision and F1-score, CNN also achieved the best results. Therefore, CNN was used for the identification of relevant tweets. The relevant reports that it generates are visually presented on a points map of the City of Cape Town. This work used a small dataset of 426 tweets. In future, the use of evolutionary computation will be explored for purposes of optimizing the deep learning models. A mobile application is under development to make this information usable by the general public.
