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dc.contributor.authorElkafrawy, Passent
dc.contributor.authorNasef, Mohamed
dc.contributor.authorHashim, Amal
dc.date.accessioned2024-03-04T12:21:13Z
dc.date.available2024-03-04T12:21:13Z
dc.date.issued2024-02-16
dc.identifier.citationNasef, M. M., El Kafrawy, P.,M., & Hashim, A. (2024). An improved clustering method using particle swarm optimization algorithm and mitochondrial fusion model (PSO-MFM). Journal of Intelligent & Fuzzy Systems, 46(2), 3071-3083.en_US
dc.identifier.issn1875-8967en_US
dc.identifier.doihttps://doi.org/10.3233/JIFS-223804en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1471
dc.description.abstractComputational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity.en_US
dc.language.isoenen_US
dc.publisherIOS Press BVen_US
dc.subjectParticle swarm optimization; P systems; mitochondrial fusion model; mutual dynamic rulesen_US
dc.titleAn improved clustering method using particle swarm optimization algorithm and mitochondrial fusion model (PSO-MFM)en_US
dc.source.journalJournal of Intelligent & Fuzzy Systemsen_US
dc.source.volume46en_US
dc.source.issue2en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labVirtual Reality Laben_US
dc.subject.KSAHEALTHen_US
dc.contributor.ugstudent0en_US
dc.contributor.alumnae0en_US
dc.source.indexWoSen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.pgstudent1en_US
dc.contributor.firstauthorNasef, Mohamed


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