2014 4th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第9页

Managing the knowledge lifecycle: A integrated knowledge management process model 管理知识生命周期:一个集成的知识管理过程模型

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993467

R. Hosseingholizadeh

引用次数: 5

Using DLBP texture descriptors and SVM for Down syndrome recognition 利用DLBP纹理描述符和支持向量机进行唐氏综合征识别

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993392

S. M. Tabatabaei, Abdollah Chalechale

{"title":"Using DLBP texture descriptors and SVM for Down syndrome recognition","authors":"S. M. Tabatabaei, Abdollah Chalechale","doi":"10.1109/ICCKE.2014.6993392","DOIUrl":"https://doi.org/10.1109/ICCKE.2014.6993392","url":null,"abstract":"Down syndrome, the most prevalent chromosome disorder in mankind, occurs approximately in one per thousand infants born per a year. Also, life expectancy of people suffering from this irregularity has increased from 25 to 59 in the last decades. Recognizing such patients in critical and high security places like security gates could assist responsible people to make proper decisions. This irregularity causes a private facial view which differentiates regular people from patients. In this study, we have proposed a novel framework, which uses first and second order directional derivative local binary pattern (LBP) histograms for texture description then applies the support vector machine for classification, in order to distinguish Down syndrome population from healthy one. We have investigated and compared two methods for texture description: one method utilizes only first order directional derivative LBP and the other benefits from both first and second order directional derivative LBPs. The histogram bins values obtained from the mentioned descriptors have been used for training the support vector machine to classify Down and not Down population. The proposed approach has been implemented using a custom database collected from free web resources. Experimental results show PPV, NPV, sensitivity and specificity factors equal to 92.35%, 96.50%, 96.66% and 92% in the best case, respectively.","PeriodicalId":152540,"journal":{"name":"2014 4th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117228855","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

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993361

Mohsen Shirpour, K. Aghajani, M. Manzuri-Shalmani

引用次数: 4

A density based clustering approach for web robot detection 基于密度的网络机器人聚类检测方法

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993362

M. Zabihi, M. V. Jahan, J. Hamidzadeh

{"title":"A density based clustering approach for web robot detection","authors":"M. Zabihi, M. V. Jahan, J. Hamidzadeh","doi":"10.1109/ICCKE.2014.6993362","DOIUrl":"https://doi.org/10.1109/ICCKE.2014.6993362","url":null,"abstract":"Distinction between humans and Web robots, in terms of computer network security, has led to the robot detection problem. An exact solution for this issue can preserve Web sites from the intrusion of malicious robots and increase the performance of Web servers by prioritizing human users. In this article, we propose a density based method called DBC_WRD (Density Based Clustering for Web Robot Detection) to discover the traffic of Web robots on two large real data sets. So, we assume the visitors as the spatial instances and introduce two new features to describe and distinguish them. These attributes are based on the behavioral patterns of Web visitors and remain invariant over time. By focusing on one of the disadvantages of DBSCAN as the density based clustering algorithm used in this paper, we just utilize 4 features to reduce the dimensions. According to the supervised evaluations, DBC_WRD can have the 96% of Jaccard metric and produce two clusters which have the entropy and purity rates of 0.0215 and 0.97, respectively. Furthermore, the comparisons show that from the standpoint of clustering quality and accuracy, DBC_WRD performs better than state-of-the-art algorithms. Finally, it can be concluded that some non-malicious popular Web robots, through imitating the human's behavior, make it difficult to be identified.","PeriodicalId":152540,"journal":{"name":"2014 4th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280478","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: 18

An entity based RDF indexing schema using Hadoop and HBase 一个使用Hadoop和HBase的基于实体的RDF索引模式

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993400

F. Abiri, M. Kahani, Fatane Zarinkalam

{"title":"An entity based RDF indexing schema using Hadoop and HBase","authors":"F. Abiri, M. Kahani, Fatane Zarinkalam","doi":"10.1109/ICCKE.2014.6993400","DOIUrl":"https://doi.org/10.1109/ICCKE.2014.6993400","url":null,"abstract":"Recent development of semantic web has opened new research to design search engines which organize and manage semantic data. The core of a search engine is the indexing system which consists of two main parts: data storage and data retrieval. With the increasing amount of semantic data, the most important goal expected from an indexing system is the ability to store large amount of data and retrieve them as fast as possible. In other words, having a scalable indexing system is one of the major challenges in semantic search engines. In this paper, a scalable method is presented to index the RDF data which utilizes HBase database, a NOSQL database management system, as its underlying data storage. HBase provides random access to massive data on the distributed framework of Hadoop, therefore, it can be a proper option for the management of the massive data. Further, due to the importance and popularity of the entity-based queries, a new schema based on a clustering algorithm is designed to effectively respond to this type of queries. The experimental evaluation shows that the proposed indexing system is effective in terms of improving scalability and retrieval of RDF data.","PeriodicalId":152540,"journal":{"name":"2014 4th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123223085","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: 4

Application of machine learning for NonHolonomic mobile robot trajectory controlling 机器学习在非完整移动机器人轨迹控制中的应用

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993354

M. Gohari, Mona Tahmasebi, A. Nozari

引用次数: 4

An effective segmentation method for dermoscopy images 一种有效的皮肤镜图像分割方法

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993380

Neda Razazzadeh, M. Khalili

引用次数: 2

Classification of liver diseases using ultrasound images based on feature combination 基于特征组合的超声图像肝脏疾病分类

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993434

A. Alivar, H. Daniali, M. Helfroush

引用次数: 14

Ensemble imbalance classification: Using data preprocessing, clustering algorithm and genetic algorithm 集成失衡分类:采用数据预处理、聚类算法和遗传算法

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993364

Niloofar Afshari Abolkarlou, A. Niknafs, M. K. Ebrahimpour

引用次数: 11

A trust evaluation model for selecting service provider in cloud environment 云环境下服务提供商选择的信任评估模型

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2014-12-22 DOI: 10.1109/ICCKE.2014.6993418

N. Agheli, Bahareh Hosseini, Aida Shojaee

引用次数: 11