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Titlebook: Data Analytics for Cultural Heritage; Current Trends and C Abdelhak Belhi,Abdelaziz Bouras,Abdul Hamid Sadka Book 2021 The Editor(s) (if ap

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发表于 2025-3-21 18:17:25 | 显示全部楼层 |阅读模式
书目名称Data Analytics for Cultural Heritage
副标题Current Trends and C
编辑Abdelhak Belhi,Abdelaziz Bouras,Abdul Hamid Sadka
视频videohttp://file.papertrans.cn/263/262695/262695.mp4
概述Reports on techniques and methods that leverage AI and machine learning and their impact on the digitization of cultural heritage;.Addresses challenges of improving data acquisition, enrichment and ma
图书封面Titlebook: Data Analytics for Cultural Heritage; Current Trends and C Abdelhak Belhi,Abdelaziz Bouras,Abdul Hamid Sadka Book 2021 The Editor(s) (if ap
描述.This book considers the challenges related to the effective implementation of artificial intelligence (AI) and machine learning (ML) technologies to the cultural heritage digitization process. Particular focus is placed on improvements to the data acquisition stage, as well as the data enrichment and curation stages, using advanced artificial intelligence techniques and tools. An emphasis is placed on recent applications related to deep learning for visual recognition, generative models, natural language processing, and super resolution. The book is a valuable reference for researchers working in the multidisciplinary field of cultural heritage and AI, as well as professional experts in the art and culture domains, such as museums, libraries, and historic sites and buildings...Reports on techniques and methods that leverage AI and machine learning and their impact on the digitization of cultural heritage; .Addresses challenges of improving data acquisition, enrichment and management processes;.Highlights contributions from international researchers from diverse fields and subject areas....
出版日期Book 2021
关键词Visual recognition; Generative models; Natural language processing; Super resolution; Cultural data cate
版次1
doihttps://doi.org/10.1007/978-3-030-66777-1
isbn_softcover978-3-030-66779-5
isbn_ebook978-3-030-66777-1
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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发表于 2025-3-21 20:43:20 | 显示全部楼层
Visual Classification of Intangible Cultural Heritage Images in the Mekong Delta,VM models using automated deep learning of invariant features outperform SVM models trained on handcrafted features. Fine-tuning Inception-v3, Xception, and two non-linear SVM models learned from Inception-v3 and Xception features achieve 60.46%, 61.54%, 61.54%, and 62.89% accuracy, respectively. We
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Crowd Source Framework for Indian Digital Heritage Space,ies, and efficient AI and Ml algorithms have made it possible to address challenges of big data storage and analytics. We present our work towards building a crowd source platform for data collection, data preprocessing, classification, storage, and query-based retrieval. The platform is used for up
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Aesthetical Issues with Stochastic Evaluation,ndscape-impact analysis, (b) insights into the expression forms of the examined artists and historical periods, and finally (c) evidence that can be used in the search of the originality of an artwork of disputed authorship.
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