Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites

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dc.authoridBOZKURT, ALPER/0000-0002-3725-2493
dc.authoridSEKER, FERHAT/0000-0001-6397-1232
dc.contributor.authorBozkurt, Alper
dc.contributor.authorSeker, Ferhat
dc.date.accessioned2025-01-06T17:36:27Z
dc.date.available2025-01-06T17:36:27Z
dc.date.issued2023
dc.description.abstractThe classification of the United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Sites (WHS) is essential for promoting sustainable tourism and ensuring the long-term conservation of cultural and natural heritage sites. Therefore, two commonly used techniques for classification problems, multilayer perceptron (MLP) and radial basis function (RBF) neural networks, were utilized to define the pros and cons of their applications. Then, according to the findings, both correlation attribute evaluator (CAE) and relief attribute evaluator (RAE) identified the region and date of inscription as the most prominent features in the classification of UNESCO WHS. As a result, a trade-off condition arises when classifying a large dataset for sustainable tourism between MLP and RBF regarding evaluation time and accuracy. MLP achieves a slightly higher accuracy rate with higher processing time, while RBF achieves a slightly lower accuracy rate but with much faster evaluation time.
dc.identifier.doi10.3390/su151713031
dc.identifier.issn2071-1050
dc.identifier.issue17
dc.identifier.scopus2-s2.0-85170221018
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su151713031
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1886
dc.identifier.volume15
dc.identifier.wosWOS:001061139400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectartificial intelligence
dc.subjectneural networks
dc.subjectmultilayer perceptron (MLP)
dc.subjectradial basis function (RBF)
dc.subjectsustainable tourism
dc.subjectUNESCO World Heritage Sites
dc.titleHarmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites
dc.typeArticle

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