DC FieldValueLanguage
dc.contributor.authorTriantafyllou, Ioannis-
dc.contributor.authorVallianos, Vassilis-
dc.contributor.authorChrysanthopoulos, Christos-
dc.contributor.authorStoyannidis, Yannis-
dc.contributor.authorDendrinos, Markos-
dc.contributor.authorPanagiotopoulos, Themis-
dc.date.accessioned2025-03-11T08:32:38Z-
dc.date.available2025-03-11T08:32:38Z-
dc.date.issued2024-06-17-
dc.identifier.isbn979-8-3503-6370-8-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/3053-
dc.description.abstractThis paper researches the intentions and the potential benefits associated with the integration of deep and machine learning technologies into archival and records management practices. With the escalating volume and intricacy of digital records, conventional methods of organizing, categorizing, and administering records confront modern-day challenges. Deep learning (DL) technologies offer prospects to revolutionize the maintenance, accessibility, and utilization of records. This research proposes a case study implementation of deep learning methodologies for thematic and type categorization of records within the University of West Attica (UNIWA). Findings highlight the necessity of deepening the standardization of governmental records management processes in the new big data era. By delving into this subject, the paper endeavors to contribute to a deeper comprehension of the transformative potential of deep and machine learning technologies in archives and records management, aiming to guide future practices and decision-making in the field. Additionally, it represents the initial practical segment of an ongoing research endeavor concerning the computational archival science of records at UNIWA.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofProceedings of 2024 XXVII International Conference on Soft Computing and Measurements (SCM 2024)en_US
dc.subjectDeep learning (DL)en_US
dc.subjectMachine learning (ML)en_US
dc.subjectSubject classificationen_US
dc.subjectComputational archival scienceen_US
dc.subjectUniversity archivesen_US
dc.subjectArchives and records managementen_US
dc.titleUNIWA Diavgeia: automated subject and type categorization on organizational recordsen_US
dc.typeConference Paperen_US
dc.relation.conference27th International Conference on Soft Computing and Measurements (SCM 2024), 22-24 May 2024, Saint Petersburg, Russian Federationen_US
dc.identifier.doi10.1109/SCM62608.2024.10554260en_US
dc.relation.deptDepartment of Archival, Library and Information Studiesen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage320en_US
dc.identifier.epage323en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldSocial Sciencesen_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Paper-
item.languageiso639-1en-
item.grantfulltextnone-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.deptDepartment of Archival, Library and Information Studies-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0001-5273-0855-
crisitem.author.orcid0000-0001-9551-8360-
crisitem.author.orcid0000-0001-5675-3069-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου
CORE Recommender
Show simple item record

Page view(s)

15
checked on Apr 11, 2025

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.