2017 13th International Conference on Computational Intelligence and Security (CIS)最新文献_第9页

Predicting the Change on Stock Market Index Using Emotions of Market Participants with Regularization Methods 用正则化方法预测市场参与者情绪对股票市场指数的影响

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00141

Yu Li, Rui Ma, Honghao Zhao, Shi Qiu, Ziyang Hu

{"title":"Predicting the Change on Stock Market Index Using Emotions of Market Participants with Regularization Methods","authors":"Yu Li, Rui Ma, Honghao Zhao, Shi Qiu, Ziyang Hu","doi":"10.1109/CIS.2017.00141","DOIUrl":"https://doi.org/10.1109/CIS.2017.00141","url":null,"abstract":"Stock market index as the composite of a series of representative stocks plays a very crucial role in the financial market. Predicting the change of stock market index is vital for investors and stock holders to capture the trend of stocks which they are interested. Recently research from behavioral finance suggests that emotions of market participates can influence stock market index. However, variable selection becomes a major challenge. Normally, lots of key words related to emotions can be extracted from the social media, meaning that the number of predictor variables p for the data mining methods is very large. Traditional variable selection methods require that the number of observations n is sufficient lager and regularization methods could select variables for high dimensional conditions. However, it is common that n is close to p when analyzing the emotions data within a specific time period. Under this condition, both variable selection methods are applicable, but few research has been done on it. In this paper, we compare the traditional variable selection method with the regularization method under the condition that n is close to p. Then we apply typical data mining methods to predict the SSE Composite Index in China with the selected variables. The results show that the regularization methods give much better performance compared with traditional variable infliction factor (VIF) analysis.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132643390","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: 1

Prediction of Stock Market by Principal Component Analysis 用主成分分析法预测股票市场

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00139

Muhammad Waqar, H. Dawood, Ping Guo, M. Shahnawaz, M. Ghazanfar

{"title":"Prediction of Stock Market by Principal Component Analysis","authors":"Muhammad Waqar, H. Dawood, Ping Guo, M. Shahnawaz, M. Ghazanfar","doi":"10.1109/CIS.2017.00139","DOIUrl":"https://doi.org/10.1109/CIS.2017.00139","url":null,"abstract":"The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data. Experiments are carried out on a high dimensional spectral of 3 stock exchanges such as: New York Stock Exchange, London Stock Exchange and Karachi Stock Exchange. The accuracy of linear regression classification model is compared before and after applying PCA. The experiments show that PCA can improve the performance of machine learning in general if and only if relative correlation among input features is investigated and careful selection is done while choosing principal components. Root mean square error (RMSE) is used as an evaluation metric to evaluate the classification model.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133179904","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: 25

Optimal Control Model of Pension Funds Under Continuous Time 连续时间下养老基金最优控制模型

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00082

Haiyan Zhang, Ya-juan Yang

引用次数: 1

Evaluation of Fiber Tracking Results from UKF Tractography Methods UKF纤维束造影方法纤维跟踪结果的评价

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00140

Na Wang, Wenyao Zhang, Wen Zhao

引用次数: 0

A New Algorithm for Computing All Solutions of an Absolute Value Equation 一种计算绝对值方程所有解的新算法

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00089

Jing Li, Jie Liu, Meili Wang, Fei Wei

引用次数: 0

A Method of Improved CNN Traffic Classification 一种改进的CNN流量分类方法

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00046

Huiyi Zhou, Yong Wang, Xiaochun Lei, Yuming Liu

引用次数: 27

Traffic Identification of Mobile Apps Based on Variational Autoencoder Network 基于变分自编码器网络的移动应用流量识别

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00069

Ding Li, Yuefei Zhu, Wei Lin

{"title":"Traffic Identification of Mobile Apps Based on Variational Autoencoder Network","authors":"Ding Li, Yuefei Zhu, Wei Lin","doi":"10.1109/CIS.2017.00069","DOIUrl":"https://doi.org/10.1109/CIS.2017.00069","url":null,"abstract":"Traffic identification is a fundamental issue in network security. Traditional methods, such as depth packet inspection (DPI) and flow-based classifiers, have difficulties in labeling massive samples and extracting features manually. Motivated by the achievements in computer vision, we focus on mobile app traffic, proposing a deep learning model based on variational autoencoder network (VEAN). Our contributions are two-fold. First, we propose a novel method of transforming mobile app traffic flows into vision-meaningful images, and thus enable the machine to identify the traffic in a human way. Then, based on the transformation method, we create an open dataset named IMTD17. Second, an improved network model is proposed, where variational autoencoder (VAE) algorithm is introduced into a two-stage learning. The model realizes the learning from massive unlabeled data, and the feasibility of the replacement for manual feature extraction is illustrated by the visualization analysis of the latent features. The experimental results show that the identification accuracy can reach 99.6%, which satisfies the practical requirement.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115166892","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: 26

Cost-Sharing Contract of Supply Chain Based on Carbon Emission Control 基于碳排放控制的供应链成本分担契约

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00080

Dan Wu, Yuxiang Yang

引用次数: 2

Evolutionary Multi-tasking Single-Objective Optimization Based on Cooperative Co-evolutionary Memetic Algorithm 基于协同进化模因算法的进化多任务单目标优化

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00050

Qunjian Chen, Xiaoliang Ma, Zexuan Zhu, Yiwen Sun

引用次数: 14

Scaled Context Region for Correlation Filter Tracking 缩放上下文区域的相关滤波跟踪

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI: 10.1109/CIS.2017.00077

Bo Zhao, Chao Ji, Lidan Li, Qianya Guo, Li-Yu Daisy Liu, Jing Zhou

引用次数: 0