TY - GEN
T1 - Deep learning and cultural heritage
T2 - 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019
AU - Belhi, Abdelhak
AU - Gasmi, Houssem
AU - Al-Ali, Abdulaziz Khalid
AU - Bouras, Abdelaziz
AU - Foufou, Sebti
AU - Yu, Xi
AU - Zhang, Haiqing
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Cultural 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.
AB - Cultural 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.
KW - Artificial Intelligence
KW - CEPROQHA Project
KW - Cultural Heritage
KW - Deep Learning
KW - Digital Heritage
UR - http://www.scopus.com/inward/record.url?scp=85081066163&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081066163&partnerID=8YFLogxK
U2 - 10.1109/SKIMA47702.2019.8982520
DO - 10.1109/SKIMA47702.2019.8982520
M3 - Conference contribution
AN - SCOPUS:85081066163
T3 - 2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019
BT - 2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 August 2019 through 28 August 2019
ER -