Abalone age prediction using machine learning

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dc.authoridAlaa Ali Hameed / 0000-0002-8514-9255en_US
dc.authorscopusidAlaa Ali Hameed / 56338374100en_US
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorGuney, Seda
dc.contributor.authorKilinc, Irem
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorJamil, Akhtar
dc.date.accessioned2022-06-11T07:48:33Z
dc.date.available2022-06-11T07:48:33Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAbalone is a marine snail found in the cold coastal regions. Age is a vital characteristic that is used to determine its worth. Currently, the only viable solution to determine the age of abalone is through very detailed steps in a laboratory. This paper exploits various machine learning models for determining its age. A comprehensive analysis of various machine learning algorithms for abalone age prediction is performed which include, backpropagation feed-forward neural network (BPFFNN), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Random Forest, Gauss Naive Bayes, and Support Vector Machine (SVM). In addition, five different optimizers were also tested with BPFFNN to evaluate their effect on its performance. Comprehensive experiments were performed using our data set. © 2022, Springer Nature Switzerland AG.en_US
dc.identifier.citationGuney, S., Kilinc, I., Hameed, A. A., & Jamil, A. (2022). Abalone age prediction using machine learning doi:10.1007/978-3-031-04112-9_25 Retrieved from www.scopus.comen_US
dc.identifier.doi10.1007/978-3-031-04112-9_25en_US
dc.identifier.endpage338en_US
dc.identifier.issn1865-0929en_US
dc.identifier.scopus2-s2.0-85128987536en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage329en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-04112-9_25
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2871
dc.identifier.volume1543en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorHameed, Alaa Ali
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofCommunications in Computer and Information Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAbaloneen_US
dc.subjectBack Propagation Neural Networksen_US
dc.subjectMachine Learningen_US
dc.subjectNeural Networksen_US
dc.titleAbalone age prediction using machine learningen_US
dc.typeConference Objecten_US

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