2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering最新文献_第8页

Separable reversible data hiding using Rc4 algorithm 基于Rc4算法的可分离可逆数据隐藏

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496466

V. Suresh, C. Saraswathy

引用次数: 13

Protein sequence motif patterns using adaptive Fuzzy C-Means granular computing model 基于自适应模糊c均值颗粒计算模型的蛋白质序列基序模式

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496454

M. Chitralegha, K. Thangavel

{"title":"Protein sequence motif patterns using adaptive Fuzzy C-Means granular computing model","authors":"M. Chitralegha, K. Thangavel","doi":"10.1109/ICPRIME.2013.6496454","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496454","url":null,"abstract":"Data Mining is the process to extract hidden predictive information from large databases. In Bioinformatics, data mining enables researchers to meet the challenge of mining large amount of biomolecular data to discover real knowledge. Major research efforts done in the area of bioinformatics involves sequence analysis, protein structure prediction and gene finding. Proteins are said to be prominent molecules in our cells. They involve virtually in all cell functions. The activities and functions of proteins can be determined by protein sequence motifs. These protein motifs are identified from the segments of protein sequences. All segments may not be important to produce good motif patterns. The generated sequence segments do not have classes or labels. Hence, unsupervised segment selection technique is adopted to select significant segments. Therefore Singular Value Decomposition (SVD) entropy method is adopted to select significant sequence segments. In this proposed work, weighted K-Means and Adaptive Fuzzy C-Means have been applied to the selected segments to generate granules, since large amount of segments cannot be grouped or clustered as such. Each granules generated by weighted K-Means algorithm are further clustered by using the K-Means algorithm and granules generated by Adaptive Fuzzy C-Means algorithm are clustered by using Weighted K-Means. The two proposed models are compared with K-Means granular computing model. The experimental results show that Adaptive Fuzzy C-Means with Weighted K-Means technique produces better results than K-Means and weighted K-Means granular computing methods.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114704963","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

Mobile application testing — Challenges and solution approach through automation 移动应用测试-通过自动化的挑战和解决方案

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496451

B. Kirubakaran, V. Karthikeyani

引用次数: 59

A novel approach to select significant genes of leukemia cancer data using K-Means clustering 一种基于K-Means聚类的白血病数据显著性基因选择新方法

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496455

P. Palanisamy, Perumal, K. Thangavel, R. Manavalan

引用次数: 8

A survey of routing protocols for VANET in urban scenarios 城市场景中VANET路由协议综述

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496522

P. S. Nithya Darisini, N. Kumari

引用次数: 35

An efficient image retrieval using central tendency 一种基于集中趋势的图像检索方法

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496448

R. Malini, C. Vasanthanayaki

引用次数: 0

Estimating incremental dimensional algorithm with sequence data set 序列数据集的增量维数估计算法

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496461

S. Adaekalavan

引用次数: 0

Huffman and Lempel-Ziv based data compression algorithms for wireless sensor networks 基于Huffman和Lempel-Ziv的无线传感器网络数据压缩算法

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496521

S. Renugadevi, P. S. Nithya Darisini

引用次数: 12

Optimal pattern search for database systems 数据库系统的最佳模式搜索

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496720

R. Vangipuram, P. V. Kumarz, V. Janaki

引用次数: 3

Instance and value (IVH) algorithm and dodging dependency for scheduling multiple instances in hybrid cloud computing 混合云计算中多实例调度的IVH (Instance and value)算法和回避依赖

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pub Date : 2013-04-15 DOI: 10.1109/ICPRIME.2013.6496511

B. Kumar, T. Ravichandran

{"title":"Instance and value (IVH) algorithm and dodging dependency for scheduling multiple instances in hybrid cloud computing","authors":"B. Kumar, T. Ravichandran","doi":"10.1109/ICPRIME.2013.6496511","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496511","url":null,"abstract":"Cloud computing is designed such a way to avoid over-provisioning when used with utility pricing. It also removes the need to over-provision in order to meet the demands of users. It involves multitenancy and multitasks, i.e., many customers can perform different tasks, accessing a single or multiple application instances. Sharing resources among a large pool of users assists in reducing infrastructure costs and peak load capacity. Due to the raise in convention of many applications currently, there is necessitating for high processing and storage capacity along with the consideration of cost and instance use. To provide proficient resources, Cloud computing is been pioneered. Many organizations have their own private cloud, but when there is need for extra resources they go for public cloud where they have been outlaid for their use. In such “pay-per-use”, workflow execution cost must be considered during scheduling based on users' QoS constraints. It is an algorithm that calculates an optimal value and instance for it. Using simulation, we have compared the performance of our proposed approach with the existing scheduling strategies for different type and size of workflows. The IVH algorithm comes to the decision of desiring which resource should be chartered from public providers.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129118496","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