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AuthorBelhi, Abdelhak
AuthorGasmi, Houssem
AuthorAl-Ali, Abdulaziz Khalid
AuthorBouras, Abdelaziz
AuthorFoufou, Sebti
AuthorYu, Xi
AuthorZhang, Haiqing
Available date2023-04-09T08:34:47Z
Publication Date2019
Publication Name2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019
ResourceScopus
URIhttp://dx.doi.org/10.1109/SKIMA47702.2019.8982520
URIhttp://hdl.handle.net/10576/41720
AbstractCultural heritage takes an important part of the history of humankind as it is one of the most powerful tools for the transfer and preservation of moral identity. As a result, these cultural assets are considered highly valuable and sometimes priceless. Digital technologies provided multiple tools that address challenges related to the promotion and information access in the cultural context. However, the large data collections of cultural information have more potential to add value and address current challenges in this context with the recent progress in artificial intelligence (AI) with deep learning and data mining tools. Through the present paper, we investigate several approaches that are used or can potentially be used to promote, curate, preserve and value cultural heritage through new and evolutionary techniques based on deep learning tools. The deep learning approaches entirely developed by our team are intended to classify and annotate cultural data, complete missing data, or map existing data schemes and information to standardized schemes with language processing tools. 2019 IEEE.
SponsorACKNOWLEDGMENT This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors (www.ceproqha.qa).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectArtificial Intelligence
CEPROQHA Project
Cultural Heritage
Deep Learning
Digital Heritage
TitleDeep learning and cultural heritage: The CEPROQHA project case study
TypeConference Paper
dc.accessType Abstract Only


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