Int. J. Rough Sets Data Anal.最新文献_第9页

Algebraic Properties of Rough Set on Two Universal Sets based on Multigranulation 基于多粒的两全称集上粗糙集的代数性质

Int. J. Rough Sets Data Anal. Pub Date : 2014-07-01 DOI: 10.4018/ijrsda.2014070104

M. Geetha, D. Acharjya, N. Iyengar

{"title":"Algebraic Properties of Rough Set on Two Universal Sets based on Multigranulation","authors":"M. Geetha, D. Acharjya, N. Iyengar","doi":"10.4018/ijrsda.2014070104","DOIUrl":"https://doi.org/10.4018/ijrsda.2014070104","url":null,"abstract":"The rough set philosophy is based on the concept that there is some information associated with each object of the universe. The set of all objects of the universe under consideration for particular discussion is considered as a universal set. So, there is a need to classify objects of the universe based on the indiscernibility relation (equivalence relation) among them. In the view of granular computing, rough set model is researched by single granulation. The granulation in general is carried out based on the equivalence relation defined over a universal set. It has been extended to multi-granular rough set model in which the set approximations are defined by using multiple equivalence relations on the universe simultaneously. But, in many real life scenarios, an information system establishes the relation with different universes. This gave the extension of multi-granulation rough set on single universal set to multi-granulation rough set on two universal sets. In this paper, we define multi-granulation rough set for two universal sets U and V. We study the algebraic properties that are interesting in the theory of multi-granular rough sets. This helps in describing and solving real life problems more accurately.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127546273","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: 36

Identification of Heart Valve Disease using Bijective Soft Sets Theory 用双目标软集理论识别心脏瓣膜疾病

Int. J. Rough Sets Data Anal. Pub Date : 2014-07-01 DOI: 10.4018/IJRSDA.2014070101

S. U. Kumar, H. Inbarani, A. Azar, A. Hassanien

{"title":"Identification of Heart Valve Disease using Bijective Soft Sets Theory","authors":"S. U. Kumar, H. Inbarani, A. Azar, A. Hassanien","doi":"10.4018/IJRSDA.2014070101","DOIUrl":"https://doi.org/10.4018/IJRSDA.2014070101","url":null,"abstract":"Major complication of heart valve diseases is congestive heart valve failure. The heart is of essential significance to human beings. Auscultation with a stethoscope is considered as one of the techniques used in the analysis of heart diseases. Heart auscultation is a difficult task to determine the heart condition and requires some superior training of medical doctors. Therefore, the use of computerized techniques in the diagnosis of heart sounds may help the doctors in a clinical environment. Hence, in this study computer-aided heart sound diagnosis is performed to give support to doctors in decision making. In this study, a novel hybrid Rough-Bijective soft set is developed for the classification of heart valve diseases. A rough set (Quick Reduct) based feature selection technique is applied before classification for increasing the classification accuracy. The experimental results demonstrate that the overall classification accuracy offered by the employed Improved Bijective soft set approach (IBISOCLASS) provides higher accuracy compared with other classification techniques including hybrid Rough-Bijective soft set (RBISOCLASS), Bijective soft set (BISOCLASS), Decision table (DT), Naive Bayes (NB) and J48.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114937694","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: 23

A Hybrid Approach to Diagnosis of Hepatic Tumors in Computed Tomography Images 计算机断层扫描图像中肝脏肿瘤的混合诊断方法

Int. J. Rough Sets Data Anal. Pub Date : 2014-07-01 DOI: 10.4018/ijrsda.2014070103

A. Anter, M. Elsoud, A. Azar, A. Hassanien

{"title":"A Hybrid Approach to Diagnosis of Hepatic Tumors in Computed Tomography Images","authors":"A. Anter, M. Elsoud, A. Azar, A. Hassanien","doi":"10.4018/ijrsda.2014070103","DOIUrl":"https://doi.org/10.4018/ijrsda.2014070103","url":null,"abstract":"Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year, can be reduced by early detection and diagnosis. Computer-aided liver analysis can help in the early detection and diagnosis of liver cancer. In this paper, enhancement and segmentation process is applied to increase the computation and focus on liver parenchyma. This parenchyma also segmented using Watershed and Region Growing algorithms to extract liver tumors. These tumors will be analyzed and characterized to distinguish between hemangioma (benign) and hepatocellular (malignant) tumors using Local Binary Pattern (LBP), Gray Level Co-occurrence matrix (GLCM), Fractal Dimension (FD) and feature fusion technique is applied to maximize and enhance the performance of the classifier rate. The authors review different methods for liver segmentation and abnormality classification. An attempt was made to combine the individual scores from different techniques in order to compensate their individual weaknesses and to preserve their strength. The authors present and exhaustively evaluate algorithms using computer vision techniques. The experimental results based on confusion matrix and kappa coefficient show that the higher accuracy is obtained of automatic agreement classification and suggest that the developed CAD system has great potential and promise in the automatic diagnosis of both benign and malignant tumors of liver.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123857102","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: 39

Image Segmentation Using Rough Set Theory: A Review 基于粗糙集理论的图像分割研究进展

Int. J. Rough Sets Data Anal. Pub Date : 2014-07-01 DOI: 10.4018/ijrsda.2014070105

Payel Roy, S. Goswami, Sayan Chakraborty, A. Azar, N. Dey

引用次数: 88

A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation 一种新的启发式函数的蚁群系统视网膜血管分割

Int. J. Rough Sets Data Anal. Pub Date : 2014-07-01 DOI: 10.4018/ijrsda.2014070102

Ahmed H. Asad, A. Azar, A. Hassanien

{"title":"A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation","authors":"Ahmed H. Asad, A. Azar, A. Hassanien","doi":"10.4018/ijrsda.2014070102","DOIUrl":"https://doi.org/10.4018/ijrsda.2014070102","url":null,"abstract":"The automatic segmentation of blood vessels in retinal images is the crucial stage in any retina diagnosis systems. This article discussed the impact of two improvements to the previous baseline approach for automatic segmentation of retinal blood vessels based on the ant colony system. The first improvement is in features where the length of previous features vector used in segmentation is reduced to the half since four less significant features are replaced by a new more significant feature when applying the correlation-based feature selection heuristic. The second improvement is in ant colony system where a new probability-based heuristic function is applied instead of the previous Euclidean distance based heuristic function. Experimental results showed the improved approach gives better performance than baseline approach when it is tested on DRIVE database of retinal images. Also, the statistical analysis demonstrated that was no statistically significant difference between the baseline and improved approaches in the sensitivity (0.7388a± 0.0511 vs. 0.7501a±0.0385, respectively; P = 0.4335). On the other hand, statistically significant improvements were found between the baseline and improved approaches for specificity and accuracy (P = 0.0024 and 0.0053, respectively). It was noted that the improved approach showed an increase of 1.1% in the accuracy after applying the new probability-based heuristic function.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130274035","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: 49

I-Rough Topological Spaces i -粗糙拓扑空间

Int. J. Rough Sets Data Anal. Pub Date : 2012-10-26 DOI: 10.4018/IJRSDA.2016010106

Boby P. Mathew, S. J. John

引用次数: 10

An Approach to Clustering of Text Documents Using Graph Mining Techniques 一种基于图挖掘技术的文本文档聚类方法

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/IJRSDA.2017010103

B. Rao, B. K. Mishra

引用次数: 32

A Rough Set Theory Approach for Rule Generation and Validation Using RSES 基于RSES的规则生成和验证的粗糙集理论方法

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/IJRSDA.2016010104

H. Rana, Manohar Lal

引用次数: 31

Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System Web2.0社会标签系统的混合TRS-PSO聚类方法

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/ijrsda.2015010102

H. Inbarani H., Selva Kumar S, Ahmad Taher Azar, A. Hassanien

引用次数: 41

Rough Set Based Similarity Measures for Data Analytics in Spatial Epidemiology 基于粗糙集的空间流行病学数据分析相似性度量

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/IJRSDA.2016010107

Sharmila Banu Kather, B. Tripathy

引用次数: 38