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Proceedings of the 2018 1st International Conference on Mathematics and Statistics最新文献

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Data Mining and Opinion Mining: A Tool in Educational Context 数据挖掘和意见挖掘:教育背景下的工具
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 2018-07-15 DOI: 10.1145/3274250.3274263
Myriam Peñafiel, Stefanie Vásquez, Diego Vásquez, Juan Zaldumbide, S. Luján-Mora
{"title":"Data Mining and Opinion Mining: A Tool in Educational Context","authors":"Myriam Peñafiel, Stefanie Vásquez, Diego Vásquez, Juan Zaldumbide, S. Luján-Mora","doi":"10.1145/3274250.3274263","DOIUrl":"https://doi.org/10.1145/3274250.3274263","url":null,"abstract":"The use of the web as a universal communication platform generates large volumes of data (Big data), which in many cases, need to be processed so that they can become useful knowledge in face of the sceptics who have doubts about the credibility of such information. The use of web data that comes from educational contexts needs to be addressed, since that large amount of unstructured information is not being valued, losing valuable information that can be used. To solve this problem, we propose the use of data mining techniques such as sentiment analysis to validate the information that comes from the educational platforms. The objective of this research is to propose a methodology that allows the user to apply sentiment analysis in a simple way, because although some researchers have done it, very few do with data in the educational context. The results obtained prove that the proposal can be used in similar cases.","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133743898","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: 5
Feature Selection Using Simulated Kalman Filter (SKF) for Prediction of Body Fat Percentage 基于模拟卡尔曼滤波(SKF)的体脂率预测特征选择
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 2018-07-15 DOI: 10.1145/3274250.3274264
N. Zamri, T. Bhuvaneswari, N. Aziz, Nor Hidayati Abdul Aziz
{"title":"Feature Selection Using Simulated Kalman Filter (SKF) for Prediction of Body Fat Percentage","authors":"N. Zamri, T. Bhuvaneswari, N. Aziz, Nor Hidayati Abdul Aziz","doi":"10.1145/3274250.3274264","DOIUrl":"https://doi.org/10.1145/3274250.3274264","url":null,"abstract":"Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. SKF is driven by the estimation capability of a well-known Kalman Filter. Since it is first introduced, it has been applied to various applications. Further studies also have been made to adapt SKF towards diverse area of optimization problems. Based on previous works, SKF algorithm has shown promising results. In this paper, SKF is proposed to do a feature selection for the prediction of body fat percentage. The prevalence of overweight and obesity has increased on a global scale. Thus, various methods have been introduced to evaluate obesity. SKF provides the ability to select features that resembles the percentage of body fat in an individual. The experimental results have shown that the proposed SKF feature selector is able to find the best combination of features and performs better than Particle Swarm Optimisation (PSO) which is a state of the art metaheuristic.","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134573868","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: 2
Spectral Graph Analysis with Apache Spark 光谱图分析与Apache Spark
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 2018-07-15 DOI: 10.1145/3274250.3275111
D. Sutic, E. Varga
{"title":"Spectral Graph Analysis with Apache Spark","authors":"D. Sutic, E. Varga","doi":"10.1145/3274250.3275111","DOIUrl":"https://doi.org/10.1145/3274250.3275111","url":null,"abstract":"Graphs are the cornerstone of many algorithms pertaining to various network analyses. When the problem's dimensionality is relatively small, expressed in the number of vertices and edges of a graph, then most methods perform adequately well. As the problem size increases, more compute power is required. Distributed computing is a one viable option to address this issue, but it cannot scale indefinitely. At one point, it is necessary to turn to heuristic approaches. Spectral graph theory is an example of such approximate scheme. In this paper, we combine spectral analysis with distributed computing using Apache Spark. The paper is accompanied with a publicly available proof of concept implementation. The system was extensively performance tested, and the results show a superb fit of Apache Spark to the purpose of spectral graph analysis. Furthermore, the resulting code is straightforward thankfully to Spark's intuitive distributed programming model, and well-designed APIs.","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124104881","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
Big Data Service Delivery Network 大数据服务交付网络
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 2018-07-15 DOI: 10.1145/3274250.3275113
E. Xinhua, Binjie Zhu
{"title":"Big Data Service Delivery Network","authors":"E. Xinhua, Binjie Zhu","doi":"10.1145/3274250.3275113","DOIUrl":"https://doi.org/10.1145/3274250.3275113","url":null,"abstract":"Big data service is a promising technology in Internet. Quality of service of big data services is a very important indicator. A service delivery network was presented in this paper to reduce service delays. The web services were distribution to the edge of the network to making it closer to users, so the network delay is small. A services distribution method with QoS guarantee was presented in this paper. Friendly degrees were measured in this method between the servers. According to the friendly degree determine the coverage areas of a copy. It takes up less resource under the premise of QoS guaranteeing.","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127494040","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
New and Old Banknote Recognition Based on Convolutional Neural Network 基于卷积神经网络的新旧钞票识别
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 2018-07-15 DOI: 10.1145/3274250.3275114
Yongjiao Liu, Jianbiao He, Min Li
{"title":"New and Old Banknote Recognition Based on Convolutional Neural Network","authors":"Yongjiao Liu, Jianbiao He, Min Li","doi":"10.1145/3274250.3275114","DOIUrl":"https://doi.org/10.1145/3274250.3275114","url":null,"abstract":"Recognition for new and old currency is a key function of the paper currency sorter. How to discriminate unfitness banknotes which became rough and fuzzy, even be damaged is an important task in financial institution. Different from traditional fitness banknote recognition based on extracting feature manually, a method based on convolutional neural network was proposed to identify fitness banknotes in this paper. Firstly, we preprocess Ukrainian banknote image and train letnet-5 model to identify fitness and unfitness currency. Secondly, after optimizing the network structure from the network layer and convolutional kernel size, we determine the best structure and performance parameters. Finally, compared with the traditional fitness banknotes recognition methods, optimized structure achieves higher recognition rate. It owes better result to combining multiple features such as holes, stains and so on. In a word, the method proposed has considerable advantages in accuracy.","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122175072","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: 3
Inference for the Evolution in Series of Studies 一系列研究的演化推论
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 2018-07-15 DOI: 10.1145/3274250.3274259
Aníbal Areia, J. Mexia, Manuela M. Oliveira
{"title":"Inference for the Evolution in Series of Studies","authors":"Aníbal Areia, J. Mexia, Manuela M. Oliveira","doi":"10.1145/3274250.3274259","DOIUrl":"https://doi.org/10.1145/3274250.3274259","url":null,"abstract":"Studies will be matrix triplets (X,Dp,Dn), where the matrix X has a row per object and a column per variable, while Dp and Dn are weight matrices for objects and variables, respectively. Given a series of studies (Xi,Dp,Dn),i=1,...,k, we condense the matrix triplets into the Ai = XiDpXtiDn, and use spectral analysis of matrix S = [Sij],i,j = 1,...,k, with Sij = tr(AiAjt) to study the series evolution. When we have a series of studies for each treatment of a basis design we carry out an ANOVA-like inference to study the action of the factors in the base design on the evolution of the series associated to the differents treatments.","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125677185","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
Estimation of Three-Parameter Weibull Distribution Based on Artificial Fish-Swarm Algorithm 基于人工鱼群算法的三参数威布尔分布估计
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 2018-07-15 DOI: 10.1145/3274250.3274252
Xiangpo Zhang
{"title":"Estimation of Three-Parameter Weibull Distribution Based on Artificial Fish-Swarm Algorithm","authors":"Xiangpo Zhang","doi":"10.1145/3274250.3274252","DOIUrl":"https://doi.org/10.1145/3274250.3274252","url":null,"abstract":"Three-parameter Weibull distribution (TPWD) plays an important role and is widely used in failure distribution modeling in reliability studies, which makes the estimation of its parameters very important and a hot study topic. In this paper, a new method of TPWD parameters estimation is proposed by integrating the artificial fish-swarm algorithm (AFSA) with the maximum likelihood estimation (MLE) method. In contrast to the existing methods, where the maximum log-likelihood value is obtained by solving the maximum likelihood equations set, the log-likelihood maximization is achieved directly using AFSA in the proposed method. And then the parameters of TPWD can be obtained according to the maximum likelihood value. The case study shows that the new method proposed in this paper is easy to be processed and has a good precision. It provides a new and highly efficient way to estimate the parameters of TPWD, and therefore provides a new way to evaluate the reliability and life distribution of products whose life distributions are considered as typical TPWD.","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122601164","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
Nonparametric Classification of Satellite Images 卫星图像的非参数分类
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 2018-07-15 DOI: 10.1145/3274250.3274260
R. Dinuls, I. Mednieks
{"title":"Nonparametric Classification of Satellite Images","authors":"R. Dinuls, I. Mednieks","doi":"10.1145/3274250.3274260","DOIUrl":"https://doi.org/10.1145/3274250.3274260","url":null,"abstract":"The task of classifying the objects on a satellite image into predefined categories is the topic of the article. The problems arising while designing a practicable classifier are discussed. The general conditions for robustness of a classifier are provided. To solve the problems mentioned, a robust classification approach is proposed aiming at completely nonparametric unsupervised clustering with consequent association of the clusters with target categories using multiple sources of the testing and training data. The nonparametric clustering used is primarily based on ranking and grouping. Completely nonparametric cluster union and cleaning procedures are presented; theoretical basics for other parts of the approach are provided. The software implementation and complexity of the methodology are discussed. The approach aims at getting the highest possible classification accuracy under real conditions for images with more than 100 million pixels.","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124542679","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: 3
Proceedings of the 2018 1st International Conference on Mathematics and Statistics 2018年第一届国际数学与统计会议论文集
Proceedings of the 2018 1st International Conference on Mathematics and Statistics Pub Date : 1900-01-01 DOI: 10.1145/3274250
{"title":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","authors":"","doi":"10.1145/3274250","DOIUrl":"https://doi.org/10.1145/3274250","url":null,"abstract":"","PeriodicalId":410500,"journal":{"name":"Proceedings of the 2018 1st International Conference on Mathematics and Statistics","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133928349","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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