Semantic Analysis of News Based on the Deep Convolution Neural Network

Muhammad Zeeshan Khan, Muhammad A. Hassan, S. U. Hassan, Muhammad Usman Ghanni Khan
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引用次数: 4

Abstract

Video data analysis is a fascinating field since the last few decades. It assists user to find the genre of a video without watching it. In this paper, an application for analysing Pakistani news data based on the scene classification using deep convolution neural network has been presented. For this purpose the dataset has been collected on our own, which consists of 200 videos of different news channels, and covers almost all categories which we possess in our methodology. The results have been evaluated using the 2D convolution neural network on the fine-tuned inception model. The methodology achieved the 92.2% accuracy on the proposed architecture, such a high accuracy on locally prepared dataset of the news for the video data analysis shows the novelty in literature.
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基于深度卷积神经网络的新闻语义分析
视频数据分析是近几十年来一个引人入胜的领域。它可以帮助用户在不看视频的情况下找到视频的类型。本文提出了一种基于场景分类的深度卷积神经网络在巴基斯坦新闻数据分析中的应用。为此,我们自己收集了数据集,其中包括200个不同新闻频道的视频,几乎涵盖了我们在方法中拥有的所有类别。使用二维卷积神经网络在微调初始模型上对结果进行了评估。该方法在提出的体系结构上达到了92.2%的准确率,如此高的准确率在本地准备的新闻数据集上用于视频数据分析显示了文献的新颖性。
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