Muhammad Zeeshan Khan, Muhammad A. Hassan, S. U. Hassan, Muhammad Usman Ghanni Khan
{"title":"基于深度卷积神经网络的新闻语义分析","authors":"Muhammad Zeeshan Khan, Muhammad A. Hassan, S. U. Hassan, Muhammad Usman Ghanni Khan","doi":"10.1109/ICET.2018.8603653","DOIUrl":null,"url":null,"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.","PeriodicalId":443353,"journal":{"name":"2018 14th International Conference on Emerging Technologies (ICET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Semantic Analysis of News Based on the Deep Convolution Neural Network\",\"authors\":\"Muhammad Zeeshan Khan, Muhammad A. Hassan, S. U. Hassan, Muhammad Usman Ghanni Khan\",\"doi\":\"10.1109/ICET.2018.8603653\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":443353,\"journal\":{\"name\":\"2018 14th International Conference on Emerging Technologies (ICET)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 14th International Conference on Emerging Technologies (ICET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICET.2018.8603653\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 14th International Conference on Emerging Technologies (ICET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICET.2018.8603653","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Semantic Analysis of News Based on the Deep Convolution Neural Network
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.