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Cloud-Based Data Classification Framework for Cultural Heritage Conservation

dc.contributor.authorRashid, Shaimaa
dc.contributor.authorQasha, Rawaa
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:32Z
dc.date.available2024-10-21T18:24:32Z
dc.date.issued2024
dc.description.abstractNineveh is one of ancient cities of Iraq, ruled by various civilizations throughout the ages. As a result, Nineveh retains different types of tangible and intangible cultural heritage with different values that show its importance. This research contributes to the digital archiving process of the culture heritage of Nineveh by suggesting a cloud-based framework that classifies the text data obtained from various heterogonous data sources according to the type, values, civilization, and time to which they belong. We used four classical machine learning algorithms to train the classifier, such as Multinomial Naive Bayes, Support Vector Machines, Random Forest, and K Nearest Neighbors. We then chose the classifier with the highest accuracy to classify the obtained texts automatically. The finding showed that the K-Nearest Neighbors classifier is the best classifier to be adopted in the classification process.en
dc.identifier.doi10.18420/inf2024_87
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45251
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectCloud Computing
dc.subjectData Classification
dc.subjectCultural Heritage Conservation
dc.subjectMachine Learning
dc.subjectData Mining
dc.titleCloud-Based Data Classification Framework for Cultural Heritage Conservationen
dc.typeText/Conference Paper
gi.citation.endPage983
gi.citation.publisherPlaceBonn
gi.citation.startPage973
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleDigitalization of Cultural Heritage / DOCH

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