Optimization of fuzzy logic-based clustering for wireless sensor networks

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dc.contributor.authorHussein, Ahmed Mohammed
dc.contributor.authorKurnaz, Sefer
dc.contributor.authorSaad, Mohammed Ayad
dc.contributor.authorIbrahim, Raed Khalid
dc.contributor.authorAli, Adnan Hussein
dc.contributor.authorTaher, Anmar Yahya
dc.date.accessioned2024-08-09T06:15:33Z
dc.date.available2024-08-09T06:15:33Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractA strong framework for managing complicated and uncertain data patterns is provided by fuzzy C-Means (FCM) clustering, a potent and frequently used data analysis technique that is adaptable for data clustering in a variety of applications, including pattern recognition, picture segmentation, and data mining. Assigning data points to various clusters with different degrees of membership, in contrast to typical hard clustering approaches, makes FCM especially well-suited for situations where data show ambiguity and overlap. An overview of FCM is given in this abstract, emphasizing its fundamental ideas, iterative optimization procedure, and capacity to manage intricate data patterns. It also goes over the benefits and uses of FCM in practical situations, highlighting how it can be used to provide cluster assignments that are both flexible and nuanced. For researchers and practitioners looking for efficient ways to cluster data even in the face of ambiguity and incomplete memberships, FCM remains a useful tool.en_US
dc.identifier.citationHussein, A. M., Kurnaz, S., Saad, M. A., Ibrahim, R. K., Ali, A. H., Taher, A. Y. (2024). Optimization of fuzzy logic-based clustering for wireless sensor networks. 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023. 10.1109/EICEEAI60672.2023.10590402en_US
dc.identifier.isbn9798350373363
dc.identifier.scopus2-s2.0-85200000798
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4800
dc.indekslendigikaynakScopus
dc.institutionauthorHussein, Ahmed Mohammed
dc.institutionauthorKurnaz, Sefer
dc.institutionauthorTaher, Anmar Yahya
dc.language.isoen
dc.publisher2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023en_US
dc.relation.ispartof2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023
dc.relation.isversionof10.1109/EICEEAI60672.2023.10590402en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCluster centeren_US
dc.subjectFCM clusteringen_US
dc.subjectPattern recognitionen_US
dc.subjectWSN cluster assignmentsen_US
dc.titleOptimization of fuzzy logic-based clustering for wireless sensor networks
dc.typeConference Object

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