Int. J. Rough Sets Data Anal.最新文献_第4页

Particle Swarm Optimization from Theory to Applications 粒子群优化从理论到应用

Int. J. Rough Sets Data Anal. Pub Date : 2018-04-01 DOI: 10.4018/IJRSDA.2018040101

M. El-Shorbagy, A. Hassanien

{"title":"Particle Swarm Optimization from Theory to Applications","authors":"M. El-Shorbagy, A. Hassanien","doi":"10.4018/IJRSDA.2018040101","DOIUrl":"https://doi.org/10.4018/IJRSDA.2018040101","url":null,"abstract":"Particle swarm optimization (PSO) is considered one of the most important methods in swarm intelligence.PSOisrelatedtothestudyofswarms;whereitisasimulationofbirdflocks.Itcanbe usedtosolveawidevarietyofoptimizationproblemssuchasunconstrainedoptimizationproblems, constrainedoptimizationproblems,nonlinearprogramming,multi-objectiveoptimization,stochastic programmingandcombinatorialoptimizationproblems.PSOhasbeenpresentedintheliterature andappliedsuccessfullyinreallifeapplications.Inthispaper,acomprehensivereviewofPSOas awell-knownpopulation-basedoptimizationtechnique.Thereviewstartsbyabriefintroductionto thebehaviorofthePSO,thenbasicconceptsanddevelopmentofPSOarediscussed,it’sfollowed bythediscussionofPSOinertiaweightandconstrictionfactoraswellasissuesrelatedtoparameter setting, selectionand tuning,dynamicenvironments, andhybridization.Also,we introduced the otherrepresentation,convergencepropertiesandtheapplicationsofPSO.Finally,conclusionsand discussionarepresented.Limitationstobeaddressedandthedirectionsofresearchinthefutureare identified,andanextensivebibliographyisalsoincluded.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129255818","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: 65

Metaheuristic Algorithms for Detect Communities in Social Networks: A Comparative Analysis Study 社会网络中社区检测的元启发式算法:比较分析研究

Int. J. Rough Sets Data Anal. Pub Date : 2018-04-01 DOI: 10.4018/IJRSDA.2018040102

A. Hassanien, Ramadan Babers

{"title":"Metaheuristic Algorithms for Detect Communities in Social Networks: A Comparative Analysis Study","authors":"A. Hassanien, Ramadan Babers","doi":"10.4018/IJRSDA.2018040102","DOIUrl":"https://doi.org/10.4018/IJRSDA.2018040102","url":null,"abstract":"ThisarticlepresentsacomparativeanalysisbetweenCuckooSearchOptimizationAlgorithm,Lion OptimizationAlgorithmandAnt-LionOptimizationAlgorithm.ZacharykarateClub,TheBottlenose DolphinNetwork,AmericanCollegeFootballNetwork,andFacebookusedasbenchmarkdatasetsfor comparison,theresultsprovedthosealgorithmscandefinethestructureanddetectcommunitiesof complexnetworkswithhighaccuracyandqualitybasedondifferentmethodthatitused.TheCuckoo SearchOptimizationAlgorithmisthebestalgorithmcomparedtoAnt-LionOptimizationAlgorithm andLionOptimizationAlgorithmasitgotgreatestnumberofcommunities,detectcommunitiesin usedbenchmarkdatasetswithaverageaccuracy%69,averagemodularity%62andaveragefitness%60. KeywoRDS Ant Lion Optimization Algorithm, Community Structure, Cuckoo Search, Lion Optimization Algorithm, Networks Community Detection, Social Networks Analysis, Social Networks","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131104561","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: 29

Nth Order Binary Encoding with Split-Protocol 分割协议的n阶二进制编码

Int. J. Rough Sets Data Anal. Pub Date : 2018-04-01 DOI: 10.4018/IJRSDA.2018040105

B. Rawal, Songjie Liang, S. Gautam, H. Kalutarage, P. Vijayakumar

{"title":"Nth Order Binary Encoding with Split-Protocol","authors":"B. Rawal, Songjie Liang, S. Gautam, H. Kalutarage, P. Vijayakumar","doi":"10.4018/IJRSDA.2018040105","DOIUrl":"https://doi.org/10.4018/IJRSDA.2018040105","url":null,"abstract":"TocopeupwiththeBigDataexplosion,theNthOrderBinaryEncoding(NOBE)algorithmwith theSplit-protocolhasbeenproposed.Intheearlierpapers,theapplicationSplit-protocolforsecurity, reliability,availability,HPChavebeendemonstratedandimplementedencoding.Thistechnologywill significantlyreducethenetworktraffic,improvethetransmissionrateandaugmentthecapacityfor datastorage.Inadditiontodatacompression,improvingtheprivacyandsecurityisaninherentbenefit oftheproposedmethod.ItispossibletoencodethedatarecursivelyuptoNtimesanduseaunique combinationofNOBE’sparameterstogenerateencryptionkeysforadditionalsecurityandprivacyfor dataontheflightoratastation.Thispaperdescribesthedesignandapreliminarydemonstrationof (NOBE)algorithm,servingasafoundationforapplicationimplementers.Italsoreportstheoutcomes ofcomputablestudiesconcerningtheperformanceoftheunderlyingimplementation. KEywORDS Adaptive Huffman Coding, Data Compression, Performance, Split-Encoding","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132154937","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: 13

Rough Set Based Ontology Matching 基于粗糙集的本体匹配

Int. J. Rough Sets Data Anal. Pub Date : 2018-04-01 DOI: 10.4018/IJRSDA.2018040103

Saruladha Krishnamurthy, A. Janardanan, B. Akoramurthy

{"title":"Rough Set Based Ontology Matching","authors":"Saruladha Krishnamurthy, A. Janardanan, B. Akoramurthy","doi":"10.4018/IJRSDA.2018040103","DOIUrl":"https://doi.org/10.4018/IJRSDA.2018040103","url":null,"abstract":"Ontologiesenrichestheknowledgeandaddmeaningtothedataresidinginsemanticweb.Ontology matchingidentifiesconceptsformatchinginsourceontologiestothetargetontologiestoeliminate heterogeneities.Despiteusing similaritymeasures for identifying similar concepts, theontology matchingsystemsfailstohandleuncertainty.Thispaperproposesaroughsetbasedontologymatching systemtohandleuncertaintywhichaims(i)tooptimizeconceptsconsideredformatchingbyusing concepttypeclassification(ii)touseroughsetconceptsusingindiscernibilityrelationsandreducts (iii)toapplycriterionofrealism–adecisionmakingunderuncertaintycriteria.Theexperiments conductedintheOAEIbenchmarkdatasets,RSOMsystemyieldedanincreaseof8%inprecision. Thecombinedapproachofusingreductandindiscenibiltyrelationsreducesthenumberofconcepts consideredformatchingamonguncertainentitiestoabout70%incomparisontotheexistingsystems andincreasestheaccuracyofresultsbyusingcriterionofrealism. KeywORDS Criterion of Realism, Ontology Matching, Reduct, Rough Sets, Uncertainty","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122489624","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

An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images 基于自适应曲波的半脆弱水印方案对数字图像进行有效智能篡改分类与恢复

Int. J. Rough Sets Data Anal. Pub Date : 2018-04-01 DOI: 10.4018/IJRSDA.2018040104

R. ChetanK., S. Nirmala

引用次数: 25

WSN Management Self-Silence Design and Data Analysis for Neural Network Based Infrastructure 基于神经网络的WSN管理自沉默设计与数据分析

Int. J. Rough Sets Data Anal. Pub Date : 2017-10-01 DOI: 10.4018/IJRSDA.2017100106

N. K. Kamila, S. Dhal

{"title":"WSN Management Self-Silence Design and Data Analysis for Neural Network Based Infrastructure","authors":"N. K. Kamila, S. Dhal","doi":"10.4018/IJRSDA.2017100106","DOIUrl":"https://doi.org/10.4018/IJRSDA.2017100106","url":null,"abstract":"In recent Wireless Sensor Network environment, battery energy conservation is one of the most important focus of research. The non-maintainable wireless sensor nodes need modern innovative ideas to save energy in order to extend the network life time. Different strategy in wireless sensor routing mechanism has been implemented to establish the energy conservation phenomenon. In earlier days, the nodes are dissipating maximum energy to communicate with each other(flooding) to establish the route to destination. In the next evolution of this research area, a clustering mechanism introduced which confirms the energy saving over the flooding mechanism. Neural Network is an advanced approach for self-clustering mechanism and when applied on wireless sensor network infrastructure, it reduces the energy consumption required for clustering. Neural network is a powerful concept with complex algorithms and capable to provide clustering solutions based on the wireless sensor network nodes properties. With the implementation of Neural Network on Wireless Sensor Network resolves the issues of high energy consumption required for network clustering. The authors propose a self-silence wireless sensor network model where sensor nodes change the sensing and transmitting mechanism by making self-silent in order to conserve the energy. This concept is simulated in neural network based wireless sensor network infrastructure of routing methodology and the authors observe that it extends the network life time. The mathematical analysis and simulation study shows the improved performance over the existing related neural network based wireless sensor routing protocols. Furthermore, the performance & related model parameters data set analysis provides the respective dependent relation information.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131105784","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: 28

A Comparison of Data Exchange Mechanisms for Real-Time Communication 实时通信数据交换机制的比较

Int. J. Rough Sets Data Anal. Pub Date : 2017-10-01 DOI: 10.4018/IJRSDA.2017100105

M. Chawla, S. Mishra, Kriti Singh, C. Kumar

引用次数: 23

Complexity Analysis of Vedic Mathematics Algorithms for Multicore Environment 多核环境下吠陀数学算法的复杂性分析

Int. J. Rough Sets Data Anal. Pub Date : 2017-10-01 DOI: 10.4018/IJRSDA.2017100103

U. Shrawankar, K. Sapkal

引用次数: 3

A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks 一种新的异常检测算法与神经网络的比较分析

Int. J. Rough Sets Data Anal. Pub Date : 2017-10-01 DOI: 10.4018/IJRSDA.2017100101

Srijan Das, Arpita Dutta, Saurav Sharma, Sangharatna Godboley

引用次数: 6

An Efficient Clustering in MANETs with Minimum Communication and Reclustering Overhead 基于最小通信和重聚类开销的manet高效聚类

Int. J. Rough Sets Data Anal. Pub Date : 2017-10-01 DOI: 10.4018/IJRSDA.2017100107

M. Mir, Satyabrata Das

{"title":"An Efficient Clustering in MANETs with Minimum Communication and Reclustering Overhead","authors":"M. Mir, Satyabrata Das","doi":"10.4018/IJRSDA.2017100107","DOIUrl":"https://doi.org/10.4018/IJRSDA.2017100107","url":null,"abstract":"AflatstructureinMANETsbasedonproactiveorreactiveroutingschemesfacescalabilityproblems withincreaseinbothmobilityandnetworksize.Clusteringoffershierarchicalorganizationofmobile nodesbyformingdisjointgroups(clusters).Soclusteringtechniquessolvethescalabilityissuein largeMANETsbut,requiresextramessageexchangeamongmobilenodesformaintenanceofcluster structure.DuetomobilityinMANETsstabilityofclusterstructureisgreatlyaffectedassuchitoften leads toRippleeffectof reclustering. In thispaper, theauthorspresentclusteringalgorithmthat eliminatestherequirementoffrozenperiodandminimizesreclusteringofentirenetworkbylocally repairingclusterstructurethatgetsaffectedduetomomentofheadnode.Theyhavereducedthe numberofmessagesexchangedintheirproposedworkbyincludingdifferentrangeoftransmission foreachnodeandoverallstabilityofentirestructureisenhanced. KEywORDS Access Point, Clusterhead, Clustering, Clustermember, Clusters, Message Overhead, Mobile Ad Hoc Networks (MANETs), Scalability","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116484087","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: 24