2010 International Conference on Information Science and Applications最新文献

Interactive Visual Data Mining of a Large Fire Detector Database 大型火灾探测器数据库的交互式可视化数据挖掘

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480395

SeungJin Lim

引用次数: 1

Real-Time Simulated Traffic Generation and Verification Methods Based on the Traffic Pattern Analysis 基于流量模式分析的实时仿真流量生成与验证方法

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480357

Hyuncheol Kim, Wonhyuk Lee, Jeom-goo Kim

引用次数: 0

A New Group Key Management Protocol Using Code for Key Calculation: CKC 一种基于密钥计算代码的组密钥管理协议:CKC

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480553

Melisa Hajyvahabzadeh, Elina Eidkhani, S. A. Mortazavi, Alireza Nemaney Pour

引用次数: 17

Location Privacy against Traffic Analysis Attacks in Wireless Sensor Networks 无线传感器网络流量分析攻击下的位置隐私

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480564

Xi Luo, Xu Ji, Myong-Soon Park

{"title":"Location Privacy against Traffic Analysis Attacks in Wireless Sensor Networks","authors":"Xi Luo, Xu Ji, Myong-Soon Park","doi":"10.1109/ICISA.2010.5480564","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480564","url":null,"abstract":"Traffic analysis attacks are passive attacks that try to deduce the traffic pattern based on the eavesdropped information. Through analyzing the packet traffic, it can deduce the location of strategic nodes, and then launch an active attack to those locations, such as DoS attack. Therefore, defending against a traffic analysis attack is to prevent the adversary from tracing the location of critical sensor nodes. Due to the open wireless communication media exposing the context information to adversaries, we cannot use traditional encryption and authentication to prevent the adversaries from eavesdropping on the wireless communication. In this paper, we propose three schemes to defend against the traffic analysis attacks. Firstly, a random routing scheme (RRS) is proposed to provide path diversity. Secondly, we combine RRS with a dummy packet injection scheme (DPIS) to confuse the adversary by tracing or tracing back the forwarded packet to reach the receiver or source. Finally, an anonymous communication scheme (ACS) is proposed to hide the identities of all nodes that participate in packets transmission. Through security analysis and simulation, we can see that our proposed schemes can efficiently defend against traffic analysis attacks, take less delivery time and achieve uniform energy consumption.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128512280","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: 71

Intuitive Fuzzy C-Means Algorithm for MRI Segmentation 直观模糊c均值算法在MRI分割中的应用

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480541

Dong-Chul Park

{"title":"Intuitive Fuzzy C-Means Algorithm for MRI Segmentation","authors":"Dong-Chul Park","doi":"10.1109/ICISA.2010.5480541","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480541","url":null,"abstract":"A new model called intuitive fuzzy c-means (IFCM) model is proposed for the segmentation of magnetic resonance image in this paper. Fuzzy c-means (FCM) is one of the most widely used clustering algorithms and assigns memberships to which are inversely related to the relative distance to the point prototypes that are cluster centers in the FCM model. In order to overcome the problem of outliers in data, several models including possibilistic c-means (PCM) and possibilistic-fuzzy cmeans (PFCM) models have been proposed. In IFCM, a new measurement called intuition level is introduced so that the intuition level helps to alleviate the effect of noise. Several numerical examples are first used for experiments to compare the clustering performance of IFCM with those of FCM, PCM, and PFCM. A practical magnetic resonance image data set is then used for image segmentation experiment. Results show that IFCM compares favorably to several clustering algorithms including the SOM, FCM, CNN, PCM, and PFCM models. Since IFCM produces cluster prototypes less sensitive to outliers and to the selection of involved parameters than the other algorithms, IFCM is a good candidate for data clustering and image segmentation problems.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129898490","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 Financial Tool for Software Risk Measurement 软件风险度量的财务工具

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480537

Masood Uzzafer

引用次数: 3

Application Coordination Model for Mobile Ubiquitous Environment 面向移动泛在环境的应用协调模型

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480585

Seungkeun Lee, Daesung Lee

引用次数: 0

Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration 基于GP-GPU硬件加速的并行局部波动估计

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480362

C. Douglas, Hyoseop Lee, D. Sheen

引用次数: 0

Performance of Machine Learning Techniques in Protein Fold Recognition Problem 机器学习技术在蛋白质折叠识别问题中的性能

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480307

M. A. Khan, M. A. Khan, Z. Jan, Hamid Ali, Anwar M. Mirza

{"title":"Performance of Machine Learning Techniques in Protein Fold Recognition Problem","authors":"M. A. Khan, M. A. Khan, Z. Jan, Hamid Ali, Anwar M. Mirza","doi":"10.1109/ICISA.2010.5480307","DOIUrl":"https://doi.org/10.1109/ICISA.2010.5480307","url":null,"abstract":"In protein fold recognition problem an effort is made to assign a fold to given proteins, this is of practical importance and has diverse application in the field of bioinformatics such as the discovery of new drugs, the individual implication of amino acid in a protein and bringing improvement in a specific protein function. In this paper, we have studied various machine learning techniques for protein fold recognition problem, and compared Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and Multilayer Perceptron (MLP) on a number of measures like the recognition accuracy of protein fold, the 10-fold cross validation accuracies and Kappa statistics. These techniques are applied to the well known Structural Classification of Proteins (SCOP) dataset in extensive experimentations. In this study Multilayer Perceptron (MLP) shows better accuracy on single protein feature (C, S, H, P, V, Z) of the SCOP dataset as compared to Support Vector Machine (SVM). A plausible reason of the better performance of MLP is that it uses all the available data for classification where as the SVM model cannot exploit all the available data.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131095068","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: 6

Analyzing the Web Crawler as a Feed Forward Engine for an Efficient Solution to the Search Problem in the Minimum Amount of Time through a Distributed Framework 分析网络爬虫作为一种前馈引擎，通过分布式框架在最短时间内有效解决搜索问题

2010 International Conference on Information Science and Applications Pub Date : 2010-04-21 DOI: 10.1109/ICISA.2010.5480411

M. A. Qureshi, A. Younus, F. Rojas

引用次数: 16