Supervised Models to Support Investigations of Ancient Coins

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dc.contributor.authorNaso, Lucaen_US
dc.contributor.authorSole, Laviniaen_US
dc.contributor.authorPatti, Andreaen_US
dc.contributor.authorArmetta, Francescoen_US
dc.contributor.authorCelso, Fabrizio Loen_US
dc.contributor.authorPatatu, Wladimiro Carloen_US
dc.contributor.authorSaladino, Maria Luisaen_US
dc.contributor.editorCampana, Stefanoen_US
dc.contributor.editorFerdani, Danieleen_US
dc.contributor.editorGraf, Holgeren_US
dc.contributor.editorGuidi, Gabrieleen_US
dc.contributor.editorHegarty, Zackaryen_US
dc.contributor.editorPescarin, Sofiaen_US
dc.contributor.editorRemondino, Fabioen_US
dc.date.accessioned2025-09-05T20:57:32Z
dc.date.available2025-09-05T20:57:32Z
dc.date.issued2025
dc.description.abstractThis paper presents the initial findings of the ongoing MML-ARCH project, which uses machine learning (ML) algorithms to create predictive, supervised models for analyzing archaeological, numismatic and physicochemical data. Specifically, the study proposes using convolutional neural network (CNN) algorithms to predict the minting year of ancient Roman Republican coins based on the iconography on the obverse and reverse.en_US
dc.description.sectionheadersDigital Technologies for CHANGES (CHANGES SESSION) - Part 1
dc.description.seriesinformationDigital Heritage
dc.identifier.doi10.2312/dh.20253153
dc.identifier.isbn978-3-03868-277-6
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/dh.20253153
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/dh20253153
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Supervised learning by regression; Applied computing → Archaeology
dc.subjectComputing methodologies → Supervised learning by regression
dc.subjectApplied computing → Archaeology
dc.titleSupervised Models to Support Investigations of Ancient Coinsen_US
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