2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)最新文献_第4页

Prediction and analysis of Rheumatic heart disease using kNN classification with ACO 基于蚁群算法的kNN分类对风湿性心脏病的预测分析

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684132

S.Rajathi Dr, G.Radhamani, Dr.G.R.Damodaran

引用次数: 35

A blind watermarking method for fingerprinting digital images 一种用于指纹数字图像的盲水印方法

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684124

J. Abraham, V. Paul

引用次数: 3

Facial emotion recognition for Human-Computer Interactions using hybrid feature extraction technique 基于混合特征提取技术的人机交互面部情感识别

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684129

Shoaib Kamal, Farrukh Sayeed, Mohammed Rafeeq

引用次数: 10

Comparative study of diverse zero-knowledge argument systems 不同零知识论证体系的比较研究

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684134

Jeril Kuriakose, Pushpendra Singh Sisodia, A. V, Dhvani K. Shah, Shraddha S. More, St John

引用次数: 2

A novel aggregated statistical feature based accurate classification for internet traffic 基于聚合统计特征的互联网流量精确分类

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684123

R. Raveendran, Raghi R. Menon

{"title":"A novel aggregated statistical feature based accurate classification for internet traffic","authors":"R. Raveendran, Raghi R. Menon","doi":"10.1109/SAPIENCE.2016.7684123","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684123","url":null,"abstract":"Traffic Classification plays a vital role and is the premise for the modern era of network security and management. This technology categorizes network traffic into several traffic classes based on some fusion of parameters. A number of restrictions have been revealed by the older methods like port based, payload based, and heuristics based classification. Due to inadequate classifier performance in each aspect, the overall classification accuracy is affected while small training samples are used. Hence statistical feature based approach incorporating supervised machine learning techniques are used here to analyze the network applications. This paper proposes a novel approach which combines Hidden Naive Bayes (HNB) and KStar (K*) lazy classifier for accurate classification. Correlation based feature selection (CFS) and Entropy based Minimum Description Length (ENT-MDL) discretization method is also used as a pre-processing task. The proposed system is analyzed and compared with other Bayesian models and lazy classifiers and the experimental results shows better outcomes compared with the state of the art methods.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121263609","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: 10

Feature selection for mining SNP from Leukaemia cancer using Genetic Algorithm with BCO 基于BCO的遗传算法挖掘白血病SNP特征选择

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684136

P. H. Prathibha, C. P. Chandran

{"title":"Feature selection for mining SNP from Leukaemia cancer using Genetic Algorithm with BCO","authors":"P. H. Prathibha, C. P. Chandran","doi":"10.1109/SAPIENCE.2016.7684136","DOIUrl":"https://doi.org/10.1109/SAPIENCE.2016.7684136","url":null,"abstract":"Single Nucleotide Polymorphisms (SNPs) are the most common form of genetic variation in humans comprising nearly 1/1,000th of the average human genome. The intelligent analysis of databases may be affected by the presence of unimportant features, which motivates the application of feature selection. In this work, we have proposed a genetic based feature selection. Genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic is routinely used to generate useful solutions to optimization and search problems. Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Bee Colony optimization (BCO) algorithm is a population-based search algorithm. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. In this paper the feature selection approach Genetic clustering with BCO was successfully applied to Leukamia cancer data sets. The feature selection approach has resulted in 80% reduction in number of features. The accuracy and specificity for the significant gene/SNP set was 70% and 82%, respectively. The number of features has been considerably reduced while the quality of knowledge was enhanced.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132494402","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

Application of Genetic Algorithm for optimization of fuel management in nuclear reactors 遗传算法在核反应堆燃料管理优化中的应用

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684164

M. Jayalal, M. Sai Baba, S. Satyamurty

引用次数: 0

A survey of diversification techniques in Recommendation Systems 推荐系统多样化技术综述

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684161

Jayeeta Chakraborty, V. Verma

引用次数: 7

An intelligent system for segmenting lung image using parallel programming 基于并行编程的智能肺图像分割系统

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684117

T. M. Shiju, S. Babu

引用次数: 1

Modified k-means algorithm and genetic approach for cluster optimization 改进的k-均值算法和遗传算法用于聚类优化

2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) Pub Date : 2016-03-01 DOI: 10.1109/SAPIENCE.2016.7684130

N. Kurinjivendhan, K. Thangadurai

引用次数: 7