2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)最新文献_第8页

Software reliability case development method based on software reliability characteristic model and measures of defect control 基于软件可靠性特征模型和缺陷控制措施的软件可靠性用例开发方法

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883004

L. Yuhua, Lu Min-yan, X. Biao

引用次数: 2

Research on building trustworthy software system by self-adaptation 基于自适应构建可信软件系统的研究

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883250

Shan Tang, Liping Li, Xiaolin Cao

引用次数: 0

A security risk assessment method of website based on threat analysis combined with AHP and entropy weight 基于威胁分析的AHP与熵权相结合的网站安全风险评估方法

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883113

Zhiquan Lai, Yongjun Shen, Guidong Zhang

引用次数: 7

Image retrieval using two-dimensional inverted index and semantic attributes 基于二维倒排索引和语义属性的图像检索

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883159

Wang Lei, Guoqiang Xiao

引用次数: 2

Link inference in the presense of load balancing 在负载均衡的情况下进行链路推理

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883010

Shaolei Wang, Lei Zhang, Chaojing Tang

引用次数: 0

Simultaneous Support Vector selection and parameter optimization using Support Vector Machines for sentiment classification 基于支持向量机的情感分类支持向量选择和参数优化

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883015

Ye Fei

{"title":"Simultaneous Support Vector selection and parameter optimization using Support Vector Machines for sentiment classification","authors":"Ye Fei","doi":"10.1109/ICSESS.2016.7883015","DOIUrl":"https://doi.org/10.1109/ICSESS.2016.7883015","url":null,"abstract":"Sentiment classification is widely used in some areas, such as product reviews, movie reviews, and micro-blogging reviews. Sentiment classification method is mainly bag of words model, Naive Bayes and Support Vector Machine. In recent years, the machine learning method represented by support vector machine (SVM) is widely used in the field of sentiment classification. There are more and more experiments show that support vector machine (SVM) performs better than the traditional bag of words model in the field of sentiment classification. However, more researches mainly focus on semantic analysis and feature extraction on sentiment, but also did not consider the case of sample imbalance. The purpose of this study was to test the feasibility of sentiment classification based on the genetic algorithm to optimize SVM model. Genetic algorithm is an optimization algorithm, which often used for selecting the feature subset and the optimization of the SVM parameters. This paper presents a novel optimization method, which select the optimal support vector subset by genetic algorithm and optimize SVM parameters. We construct the experiment show that the proposed method has improved significantly on sentiment classification than the traditional SVM modeling capabilities.","PeriodicalId":175933,"journal":{"name":"2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132141703","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: 17

A kind of semi-supervised classifying method research for power transformer fault diagnosis 一种用于电力变压器故障诊断的半监督分类方法研究

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883238

Siping Chen

{"title":"A kind of semi-supervised classifying method research for power transformer fault diagnosis","authors":"Siping Chen","doi":"10.1109/ICSESS.2016.7883238","DOIUrl":"https://doi.org/10.1109/ICSESS.2016.7883238","url":null,"abstract":"Dissolved gas analysis is one of the most common techniques to detect the faults in the power transformers. Most of existing diagnosis method will need large amount of labeled data sets to construct classifier, while normally ignoring without unlabeled data sets. This paper presents a power transformer fault diagnosis method which based on semi-supervised classifying. In Its learning process, the semi-supervised classifying method can simultaneously use labeled data sets and unlabeled data sets to acquire more information so that make better learning effect. A semi-supervised classifying (SSC) method adopting fuzzy nearest neighborhood label propagation (FNNLP-SSC)is adopted to diagnose the fault of power transformer, in the meantime, the proposed method, based on the similarity connections between a sample and its K nearest data, classifies the unlabeled data by making the labels propagate from the labeled data to unlabeled data. The experiments indicate that method of this paper has been proposed has higher fault diagnosis accuracy compared with C-means (FCM) algorithm and the three ratio method IEC. Also, it verifies the effectiveness and feasibility of the proposed method in the transformer fault diagnosis.","PeriodicalId":175933,"journal":{"name":"2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133140647","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

Optimization WIFI indoor positioning KNN algorithm location-based fingerprint 优化WIFI室内定位KNN算法定位指纹

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883033

Xingbin Ge, Zhiyi Qu

引用次数: 61

Requirements change management based on object-oriented software engineering with unified modeling language 基于面向对象软件工程的需求变更管理，采用统一的建模语言

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883005

Jessada Tomyim, A. Pohthong

引用次数: 27

A study on the power consumption of using cryptography algorithms in mobile devices 在移动设备中使用加密算法的功耗研究

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2016-08-01 DOI: 10.1109/ICSESS.2016.7883224

Jiehong Wu, Ilia Detchenkov, Yang Cao

引用次数: 13