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2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)最新文献

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Analysis and research on the policy performance of college students returning home to start a business based on data visualization “artificial intelligence + agriculture” 基于数据可视化“人工智能+农业”的大学生返乡创业政策绩效分析研究
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00010
Zhaoyu Peng, S. Cao
{"title":"Analysis and research on the policy performance of college students returning home to start a business based on data visualization “artificial intelligence + agriculture”","authors":"Zhaoyu Peng, S. Cao","doi":"10.1109/ISAIAM55748.2022.00010","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00010","url":null,"abstract":"As a new generation of scientific and technological forces, artificial intelligence is promoting high-quality economic development, accelerating social innovation and intelligent transformation of industries. After the issuance of the sub-“New Generation Artificial Intelligence Development Plan”, the introduction of artificial intelligence into the agricultural field can not only promote the economic growth of rural areas, but also provide new opportunities for college students to return to their hometowns to start a business. Taking “artificial intelligence + agriculture” as the research background, taking the returned entrepreneurial college students in Jingzhou City as the survey object, the policy performance is objectively analyzed by the analytic hierarchy method (AHP analysis method), and the fuzzy comprehensive method (FCE) is used to quantitatively evaluate the scores of various indicators, and constructive suggestions and optimization measures are put forward to help the returned college students better carry out entrepreneurial activities.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125202354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Comparative Study on the Evaluation Functions of Chinese and Japanese Reported Speech Based on Big Data: Taking “Poverty Alleviation of China” as An Example 基于大数据的汉语和日语口头报告评价功能比较研究——以“中国扶贫”为例
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00042
Wenyu Zhang
{"title":"A Comparative Study on the Evaluation Functions of Chinese and Japanese Reported Speech Based on Big Data: Taking “Poverty Alleviation of China” as An Example","authors":"Wenyu Zhang","doi":"10.1109/ISAIAM55748.2022.00042","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00042","url":null,"abstract":"Big data artificial intelligence technology has brought new ideas and perspectives to the study of journalism and communication and linguistics. Reported speech analysis is an important application in the intersection of linguistics and journalism and communication. This study selects the authoritative Chinese and Japanese media “People's Daily” and “Asahi Shimbun”, collects reports on “poverty alleviation of China” through big data technology, explores the similarities and differences of Chinese and Japanese reported speech by means of artificial intelligence deep learning text sentiment analysis. It is found that Chinese and Japanese news discourse have different preferences in sources, forms and settings, which are used to shape different images.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121084354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on News Push Mechanism Based on Intelligent Algorithm 基于智能算法的新闻推送机制研究
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00026
Lingjun Meng
{"title":"Research on News Push Mechanism Based on Intelligent Algorithm","authors":"Lingjun Meng","doi":"10.1109/ISAIAM55748.2022.00026","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00026","url":null,"abstract":"In order to build a reasonable news push mechanism through intelligent algorithm, relevant research will be carried out in this paper, mainly discussing the operation process of news push mechanism, and then introducing key technologies and intelligent algorithms in the construction of news push mechanism. Finally, practical research will be conducted to verify the effectiveness of the algorithm. Through the research of this paper, the intelligent algorithm can realize accurate news push and effectively develop news value.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121326569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Differential Weight Design Model of Power Grid Enterprise Asset Equipment Health Base on Fuzzy Logic and Machine Learning 基于模糊逻辑和机器学习的电网企业资产设备健康差权设计模型研究
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00018
Jia-xu Cheng, Youzi Wang, Wenxuan Li
{"title":"Research on Differential Weight Design Model of Power Grid Enterprise Asset Equipment Health Base on Fuzzy Logic and Machine Learning","authors":"Jia-xu Cheng, Youzi Wang, Wenxuan Li","doi":"10.1109/ISAIAM55748.2022.00018","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00018","url":null,"abstract":"Based on fuzzy logic and machine learning to the design and analysis of the basic weight of the asset management health evaluation model, from the subjective weight design and objective weight design two aspects, using analytic hierarchy process and entropy weight method to set the subjective and objective weight, and then through the combination of subjective and objective weight evaluation method, can achieve the effect of learning from each other. For the design of differentiation weight, based on the design of basic weight, the differentiation weight model is constructed by considering the development stage difference of asset health and the regional difference and the dynamic characteristics of evaluation indicators. The weight design model based on differentiation analysis is constructed based on hall 3D model.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129163112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Application And Exploration of Artificial Intelligence in The Whole Chain of News Communication 人工智能在新闻传播全链条中的应用与探索
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00039
Lingjun Meng
{"title":"Application And Exploration of Artificial Intelligence in The Whole Chain of News Communication","authors":"Lingjun Meng","doi":"10.1109/ISAIAM55748.2022.00039","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00039","url":null,"abstract":"With the continuous development of science and technology, especially the emergence and development of artificial intelligence technology, the media industry has undergone great changes. Artificial intelligence technology can provide powerful data information mining, content production and other services for the news communication industry. However, faced with the development of big data, cloud computing and other new era technologies, the disadvantages of the traditional whole chain operation mode of news communication, such as low efficiency, have become increasingly obvious. Based on this background, this paper combined with the artificial intelligence technology to the influence of news propagation, the analysis of artificial intelligence technology in the news spread the application of the whole chain, through the analysis of the research to the depth of the article ai technology applications for the importance and role of journalism and communication, through technical analysis to reflect the advantages of data mining, such as content generation technology.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115210420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Digitization of X-Ray Films of Aerospace Products and Defect Detection Based on Convolutional Neural Network 航空航天产品x射线胶片数字化及基于卷积神经网络的缺陷检测
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00030
Xing Wang, Zengyu Sun, Yue Gao, Tong Wu
{"title":"Digitization of X-Ray Films of Aerospace Products and Defect Detection Based on Convolutional Neural Network","authors":"Xing Wang, Zengyu Sun, Yue Gao, Tong Wu","doi":"10.1109/ISAIAM55748.2022.00030","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00030","url":null,"abstract":"The X-ray film digitization of aerospace products and the automatic detection of weld defects are of great importance for information storage and management. In this paper, we design a system for digitization and automatic detection. By automatically selecting the image with best exposure time among the images with different exposure time which are captured from an X-ray film, we can get the best digital image after homomorphic filtering of it. Then we design a network on the basis of YOLOv3 for defect detection. The digitization and detection results show that our system is of good effect.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124031512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Helmet Wearing Detection System for Non-Motor Vehicle Riders Based on k210 and YOLOv3 基于k210和YOLOv3的非机动车驾驶员头盔佩戴检测系统
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00013
Zhifei Liu, Qingsheng Xiao
{"title":"Helmet Wearing Detection System for Non-Motor Vehicle Riders Based on k210 and YOLOv3","authors":"Zhifei Liu, Qingsheng Xiao","doi":"10.1109/ISAIAM55748.2022.00013","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00013","url":null,"abstract":"Wearing helmets can effectively reduce the secondary injuries caused by traffic accidents, and it is necessary to detect the helmet wearing conditions of non-motor vehicle riders. This paper proposes a portable and low-cost rider helmet wearing detection system. The system adopts the lightweight network framework of YOLOv3, and deploys the algorithm on the K210 microcontroller. The innovation of this paper is that the calculation amount and model size of the traditional YOLOv3 algorithm model are much larger than the maximum scale supported by the K210 chip, and it is difficult to deploy on small embedded devices. We need to improve the traditional YOLOv3 algorithm. This paper adopts the method of replacing the backbone network to reduce the complexity of the model, so that the algorithm can be deployed on the K210 microcontroller. This paper conducts comparative training under two different backbone networks, and deploys the appropriate model on the k210 single-chip microcomputer, which reduces the detection cost.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124052750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-Attentional U-Net for Medical Image Segmentation 多注意力U-Net医学图像分割
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00033
Zhifang Hong, Hewen Xi, Weijie Hu, Qing Wang, Jiayi Wang, Lingli Luo, Xiying Zhan, Yuping Wang, Junxi Chen, Lingna Chen
{"title":"Multi-Attentional U-Net for Medical Image Segmentation","authors":"Zhifang Hong, Hewen Xi, Weijie Hu, Qing Wang, Jiayi Wang, Lingli Luo, Xiying Zhan, Yuping Wang, Junxi Chen, Lingna Chen","doi":"10.1109/ISAIAM55748.2022.00033","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00033","url":null,"abstract":"Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132112058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A study of aspect-level sentiment analysis based on deep learning 基于深度学习的方面级情感分析研究
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00009
Yenan Chen, Yingjia Li, Juntao Ma
{"title":"A study of aspect-level sentiment analysis based on deep learning","authors":"Yenan Chen, Yingjia Li, Juntao Ma","doi":"10.1109/ISAIAM55748.2022.00009","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00009","url":null,"abstract":"The study of aspect-level sentiment analysis using deep learning methods is one of the more important research directions in the field of natural language processing in recent years. In this paper, we address the problem of insufficient extraction of deep semantic features in existing aspect-level sentiment analysis research, design and build a sentiment analysis model based on the pre-trained language model BERT, fuse BiLSTM and GCN deep learning methods, analyze the sentiment tendency on the collected product review dataset, design relevant experiments to compare in the same application scenario, and verify the effectiveness of the proposed model.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134244299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Joint Tagging Event Extraction with Attention Mechanism 基于注意机制的联合标注事件提取
2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Pub Date : 2022-06-01 DOI: 10.1109/ISAIAM55748.2022.00020
Yang Xu, Jian Zheng, Junhui Yang, F. Tang
{"title":"A Joint Tagging Event Extraction with Attention Mechanism","authors":"Yang Xu, Jian Zheng, Junhui Yang, F. Tang","doi":"10.1109/ISAIAM55748.2022.00020","DOIUrl":"https://doi.org/10.1109/ISAIAM55748.2022.00020","url":null,"abstract":"Event extraction can extract event information from text, which is a very important information extraction task. Currently, most methods assume that there is at most one event in a sentence. However, in practice there may be one or more events in a sentence. Therefore, there may be information overlap between multiple events. To solve this problem, this paper proposes a novel joint learning framework, JointEE. First, the similarity function is used to measure the types of events present in the sentence, and then the sequence annotation model with the attention mechanism is used to jointly extract trigger and arguments. The paper is evaluated on a public event extraction dataset, FewFC. Experiments show that, compared with previous methods, JointEE achieves good results on the overlapping event extraction problem.","PeriodicalId":382895,"journal":{"name":"2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)","volume":"2 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114024826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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