International Conference on Images, Signals, and Computing最新文献

A trajectory simplification algorithm based on motion trend and variable speed characteristics 一种基于运动趋势和变速特性的轨迹简化算法

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691797

Wei Li, Liang Zhou

引用次数: 0

Wireless and sensors network security threats and countermeasures 无线和传感器网络安全威胁及对策

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692300

Maha A Abdullah, Mazen A Abdullah, Omar H. Alhazmi

引用次数: 0

Merging public opinion information and stock numerical data for stock trend prediction based on deep learning 融合舆情信息和股票数值数据进行基于深度学习的股票走势预测

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691661

geng Lv, Jianjiang Cui

{"title":"Merging public opinion information and stock numerical data for stock trend prediction based on deep learning","authors":"geng Lv, Jianjiang Cui","doi":"10.1117/12.2691661","DOIUrl":"https://doi.org/10.1117/12.2691661","url":null,"abstract":"Unlike other stock markets participants, the participants in China mainland are composed of individual investors, which account for 82% of the trading volume of the stock market. The decision-making basis of individual investors is mainly public opinion and recent stock prices. Therefore, the public opinion on professional stock social sites has an important impact on the decision of individual investors, which in turn affects the trend of the stock market. However, the previous stock market forecasting methods mostly ignored the influence of public opinion information on the market. For this reason, this paper proposes a novel framework to predict the stock trend by using both public opinion and stock numerical data. The original contributions of this paper include stock commentary word embedding model based on the stock comment text data crawled from https://xueqiu.com through two-stage training and LSTM-CNN layered model based on the improved self-attention mechanism. Two main experiments are conducted: the first experiment extract stock commentary word embedding, and the second experiment forecasts the stock price trends of Shanghai and Shenzhen A-share market. Results show that: 1)LSTM-CNN layered model is better than previous methods; 2)The combination of public opinion information and numerical data can improve the performance of the model; 3)Stock commentary word embedding model is better than pre-training word embedding model; 4) The longer the data span, the better the stock forecasting model will perform","PeriodicalId":361127,"journal":{"name":"International Conference on Images, Signals, and Computing","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124825270","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

Adolescent dysmorphic disorder model research based on machine learning 基于机器学习的青少年畸形障碍模型研究

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691926

Leyao Bi

引用次数: 0

Multi-sensor fusion for the security surveillance of public areas 多传感器融合用于公共场所安全监控

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692294

M. Litzenberger, Michael Hubner, B. Kohn, Kilian Wohlleben

引用次数: 0

Reducing ringing artefact in fresnel digital holography using compressed sensing 利用压缩传感技术减少菲涅耳数字全息中的环形伪影

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691819

Yue Wang, J. Healy

引用次数: 0

Research on a personalized classifier of health status based on pulse signal 基于脉冲信号的健康状态个性化分类器研究

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691708

Xiaoping Jiang, Leli Sun, Shuyao Feng, Zhuojing Li, Ying Chen, Xingzhuo Chen, C. Wang, Aolai He

引用次数: 0

DOA estimation in a distributed optimization framework: a sparse approach based on consensus ADMM implementation 分布式优化框架中的DOA估计:基于共识ADMM实现的稀疏方法

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691755

Xiaoyuan Jia, Xiaohuan Wu, Weiping Zhu

引用次数: 0

Face mask recognition during KYC generation from a live photo detection methodology 面罩识别在KYC生成从现场照片检测方法

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.3002128

Debalina Barua, Mumtahina Ahsan, Moumita Khandaker, H. M. Z. Amin, Md Humaion Kabir Mehedi, Annajiat Alim Rasel

引用次数: 0

Application specific convolutional neural networks for brain tumor detection 特定卷积神经网络在脑肿瘤检测中的应用

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691696

Yanming Sun, Juncheng Tong, Yunlong Ma, Chunyan Wang

{"title":"Application specific convolutional neural networks for brain tumor detection","authors":"Yanming Sun, Juncheng Tong, Yunlong Ma, Chunyan Wang","doi":"10.1117/12.2691696","DOIUrl":"https://doi.org/10.1117/12.2691696","url":null,"abstract":"The research on CNN applications for medical image processing has been progressing rapidly. In the process of the development, hurdles appear and are to be overcome. The limitation in training samples is one of them, and restriction in computation resources can be another. In this paper, we present a design approach of application specific CNN (ASCNN), allowing to minimize the computational complexity of CNN systems without lowering the performance. This approach is to full-custom design CNNs for specific applications, such as brain tumor detection, so that each part of a CNN can be optimized to suit the input data and the task assigned to it. The convolution kernels and layers are made just-sufficient, nothing excessive. In this way, the randomness and the redundancy in computation can be minimized, the dependency on training samples decreased, the information density in data flow increased, the computation efficiency/quality and performance reliability improved. Three ASCNN systems for brain tumor detection are also presented as design examples. The results of the performance evaluation demonstrate that each of them delivers a high-quality detection with a computation volume of one-digit percentage, or less, of that needed by other CNN systems recently reported in reputed journals in the research area. Hence, ASCNN approach is effective to achieve high process quality at low computation cost. It can also lower the barrier of resource requirement of CNN systems to make them more implementable and applicable for general public.","PeriodicalId":361127,"journal":{"name":"International Conference on Images, Signals, and Computing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121556459","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