2012 Fourth International Conference on Advanced Computing (ICoAC)最新文献_第3页

Recommendation framework for Service-Oriented Grid 面向服务网格的推荐框架

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416842

N. Nagarathna, M. Indiramma

引用次数: 3

Investigation of high utility itemset mining in service oriented computing: Deployment of knowledge as a service in E-commerce 面向服务计算中的高效用项集挖掘研究:电子商务中知识即服务的部署

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416812

S. Kannimuthu, K. Premalatha, S. Shankar

引用次数: 9

Object monitoring by prediction and localisation of nodes by using Ant Colony Optimization in Sensor Networks 基于蚁群优化的传感器网络节点预测和定位目标监测

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416828

S. Niranchana, E. Dinesh

{"title":"Object monitoring by prediction and localisation of nodes by using Ant Colony Optimization in Sensor Networks","authors":"S. Niranchana, E. Dinesh","doi":"10.1109/ICOAC.2012.6416828","DOIUrl":"https://doi.org/10.1109/ICOAC.2012.6416828","url":null,"abstract":"Wireless sensor network (WSN) consists of tiny sensor nodes with sensing, computation and wireless communication capabilities. Now days, it is finding wide applicability and increasing deployment, as it enables reliable monitoring and analysis of environment. The design of routing protocols for WSN is influenced by many challenging factors like fault tolerance, energy efficiency, scalability, latency, power consumption and network topology. Mobile Sensor Networks (MSN) is networks composed of a large number of wireless devices having sensing, processing, communication, and movement capabilities. In WSN, the coverage of the large area can be improved by the moving the sensor nodes. Coverage in a wireless sensor network can be thought of as how well the wireless sensor network is able to monitor a particular field of interest. In this paper the problem of object monitoring in Mobile Sensor Networks can be identified. The proposed system consists of estimating the position of nodes and then the estimated positions are used to predict the location of nodes. Once the object is determined, the mobile node moves to cover the particular object. If the Target cannot be defined then the set of new nodes are located and each node is assigned a position to minimize the total travelled distance. The estimation and prediction of nodes are done by Interval Theory and the Relocation of Nodes is done by using Ant Colony Optimization. ACO is the Localization of Sensor Nodes which Tracks the Targets. In this proposed paper the simulation results are compared to object monitoring methods considered for networks with static nodes.","PeriodicalId":286985,"journal":{"name":"2012 Fourth International Conference on Advanced Computing (ICoAC)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121152428","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: 11

Wind profile prediction using a Meta-cognitive Fully Complex-valued neural network 基于元认知全复值神经网络的风廓线预测

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416850

E. Sathish, M. Sivachitra, R. Savitha, S. Vijayachitra

引用次数: 14

Using differential evolution in the prediction of software effort 在软件工作预测中使用差分进化

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416816

I. Thamarai, S. Murugavalli

{"title":"Using differential evolution in the prediction of software effort","authors":"I. Thamarai, S. Murugavalli","doi":"10.1109/ICOAC.2012.6416816","DOIUrl":"https://doi.org/10.1109/ICOAC.2012.6416816","url":null,"abstract":"Estimation of software is a very important and crucial task in the software development process. Due to the intangible nature of software, it is difficult to predict the effort correctly. There are number of options available to predict the software effort such as algorithmic models, non-algorithmic models etc. Estimation of Analogy has been proved to be most effective method. In this, the estimation is based on the similar projects that have been successfully completed already. If the parameters of the current project, matches well with the past project then it is easy to calculate the effort for current project. The success rate of the effort prediction largely depends on finding the most similar past projects. For finding the most relevant past project in estimation by analogy method, the computational intelligence tools have already been used. The use of Artificial Neural Networks, Genetic Algorithm has not fully solved the problem of selection of relevant projects. The main problems faced are Feature Selection and Similarity Measure between the projects. This can be achieved by using Differential Evolution. This is a population based search strategy. The Differential Evolution is used to compare the key attributes between the two projects. Thus we can get most optimal projects which can be used for the estimation of effort using analogy method.","PeriodicalId":286985,"journal":{"name":"2012 Fourth International Conference on Advanced Computing (ICoAC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127229758","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

Performance analysis of AODV, DSR, TORA and OLSR to achieve group communication in MANET 在MANET中实现群组通信的AODV、DSR、TORA和OLSR的性能分析

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416834

M. Rajeswari, P. Maheswari, S. Bhuvaneshwari, S. Gowri

引用次数: 18

Learning very simple k-Equal Matrix Grammar from positive data 从正数据中学习非常简单的k-相等矩阵语法

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416794

P. Grace, J. D. Emerald, D. G. Thomas

引用次数: 0

Region based reconstruction from the axial view of the brain for inverse EEG problem 基于脑轴位图像的区域重构求解逆EEG问题

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416820

M. Arivazhagan, N. Mira

引用次数: 0

Application of Naive Bayes dichotomizer supported with expected risk and discriminant functions in clinical decisions — Case study 期望风险和判别函数支持的朴素贝叶斯二分类器在临床决策中的应用-案例研究

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416811

A. Pratap, C. Kanimozhiselvi

{"title":"Application of Naive Bayes dichotomizer supported with expected risk and discriminant functions in clinical decisions — Case study","authors":"A. Pratap, C. Kanimozhiselvi","doi":"10.1109/ICOAC.2012.6416811","DOIUrl":"https://doi.org/10.1109/ICOAC.2012.6416811","url":null,"abstract":"In this paper, a case study on the application of Naïve Bayes dichotomizer in clinical decision supporting systems is described. The case study is about the diagnosis of the possibility of having Pervasive Developmental Disorder (PDD) in a child. The age group selected for the study is in between 2 and 3 years. Pervasive developmental Disorder is a neuro disorder that affects the social functioning, behavioural functioning and communication in a child. Conventional diagnosis is based on the scores obtained on checklists like DSM-IV Criteria. As Bayesian reasoning uses probability inferences, it is usually applied on decision making systems. Here for the study a Naive Bayes probabilistic dichotomizer was implemented. This dichotomizer calculates the most probable output depending on the inputs given to it, by applying the Bayes rule. Since the classifier is considering only two classes, the classifier is called as dichotomizer. The minimum expected risk and positive discriminant functions are also calculated, which again supports the decision of Naive Bayes dichotomizer. Implementation of Maximum A Priori Hypothesis and Maximum Likelihood Hypothesis are also discussing on the case study for a comparison. The main goal of this research work was to study the application of some probabilistic reasoning techniques in clinical decision supporting systems, where classification is more important. Based on the implementation of our case study, the findings shown that Naive Bayes dichitomizer supported with minimum expected risk and positive discriminant function, classifies correctly in clinical decision supporting systems.","PeriodicalId":286985,"journal":{"name":"2012 Fourth International Conference on Advanced Computing (ICoAC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129994809","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: 11

Surface defect detection and classification in mandarin fruits using fuzzy image thresholding, binary wavelet transform and linear classifier model 基于模糊图像阈值、二值小波变换和线性分类器模型的柑桔表面缺陷检测与分类

2012 Fourth International Conference on Advanced Computing (ICoAC) Pub Date : 2012-12-01 DOI: 10.1109/ICOAC.2012.6416829

Anandhanarayanan Kamalakannan, G. Rajamanickam

引用次数: 11