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dc.contributor.authorZelinka, Ivan
dc.contributor.authorLara, Melvin
dc.contributor.authorWindsor, Leah C.
dc.contributor.authorLozi, René
dc.date.accessioned2024-02-29T15:09:31Z
dc.date.available2024-02-29T15:09:31Z
dc.date.issued2023
dc.identifier.citationApplied Soft Computing. 2023, vol. 138, art. no. 110217.cs
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.urihttp://hdl.handle.net/10084/152269
dc.description.abstractThe Voynich manuscript is a more than 600-year-old historical manuscript. It is considered one of the most mysterious books in the world. Over the last 100 years, this book has resisted attempts to decipher its content; hence, it is written in unidentified language. Since the discovery of the manuscript, many known and unknown cryptographers have unsuccessfully tried to decipher this book. Also, many mathematical methods have been implemented to determine whether it is a fraudulent historical text or an authentic text containing valuable information. This article aims to show the use of deep learning networks and classical methods to measure the similarity between the individual characters of the alphabet and between other alphabets and Voynich. The first part of the article demonstrates the effectiveness of our method in determining the similarities between individual characters of the Voynich alphabet. In the second part, we find the similarity between the Voynich Manuscript and other individual alphabet sets (languages). In other words, this article shows another possible direction in the research of Voynich manuscript to identify the language dialect family from which Voynich manuscript can theoretically come.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesApplied Soft Computingcs
dc.relation.urihttps://doi.org/10.1016/j.asoc.2023.110217cs
dc.rights© 2023 The Authors. Published by Elsevier B.V.cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectVoynichcs
dc.subjectmanuscriptcs
dc.subjectdeep learningcs
dc.subjectsimilaritycs
dc.subjectdialectcs
dc.titleSoftcomputing in identification of the origin of Voynich manuscript by comparison with ancient dialectscs
dc.typearticlecs
dc.identifier.doi10.1016/j.asoc.2023.110217
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume138cs
dc.description.firstpageart. no. 110217cs
dc.identifier.wos001042692200001


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© 2023 The Authors. Published by Elsevier B.V.
Except where otherwise noted, this item's license is described as © 2023 The Authors. Published by Elsevier B.V.