Data Analytics for Cultural Heritage

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.

Auteur

Abdelhak Belhi, PhD, is senior research assistant at Qatar University. He received his Ph.D. in computer science and machine learning from the University of Lyon, France, and his MS degree in computer science from the University of Boumerdes, Algeria. His current research interests include data mining, deep learning and machine learning, computer systems security and blockchain. He is working as member of the CEPROQHA NPRP project, contributing to the development of various machine learning approaches in the context of cultural heritage digital preservation. He works on the reconstruction of missing cultural data (both visual and textual) using advanced machine learning-based approaches for classification and image inpainting. His contributions were published in reputable international journals. He reviewed multiple contributions for top journals in the field of machine learning and deep learning.

Abdelaziz Bouras, PhD, is Professor in the Computer Science and Engineering Department of the College of Engineering at Qatar University, where he also manages the Pre-Award Department at the Office of Research Support. His research focuses on distributed software for data life cycle management and preservation. His current work deals with the application of AI and deep learning approaches for prediction and decision making. He is currently leading several projects such as the Supply ledger NPRP Project (funded by the Qatar National Research Fund) on Smart Ledger technologies in Supply Chain management; and the CEPROQHA NPRP Project on Cultural Heritage preservation, in collaboration with Brunel University London and the Museum of Islamic Art. During his previous academic involvement with the University of Lyon, he managed several EU projects in the context of digital preservation of cultural heritage such as the Erasmus-Mundus Sustainable eTourism program between Europe and seven countries from South East Asia. He published many research papers in refereed journals/conferences and edited several books. He also founded two international journals (IJPD, IJPLM) in the ICT for engineering domains. He is currently chairing the IFIP International Federation of Information Processing WG5.1, which holds a regular international conference on digital life cycle information systems.

Abdulaziz Al-Ali, PhD, is Assistant Professor in the Computer Science and Engineering Department, and director of the KINDI Center for Computing Researching the College of Engineering at Qatar University. He received his BS and MS Degrees in Computer Engineering and his PhD in Machine Learning from the University of Miami, Florida. His current research interests include machine learning techniques, datamining, artificial intelligence, and their applications. Among his research projects is the inpainting of cultural heritage images using deep learning approaches, the detection of fake news in Arabic social media (a partnership with Aljazeera news channel), and detection of hypoglycemia in diabetes patients in collaboration with Weill Cornell Medicine - Qatar. Dr. Abdulaziz has published several journal articles in reputable venues of his field. He has been awarded several research grants, including grants from the National Priorities Research Program from Qatar National Research Fund.

Abdul Hamid Sadka, PhD, Professor Sadka has been an internationally recognised figure in Visual Media Processing and Communications for over 25 years, pioneering error resilient video communications and 2D/3D visual media processing. He is the Director of the Institute of Digital Futures, the former Head of the Department of Electronic and Computer Engineering at Brunel University London (2006-2012) and the Head of the Media Communication Research Group. He has 200+ refereed publications, 3 patents and his textbook "Compressed Video Communications", published by Wiley in 2002, is widely acknowledged as a seminal book in error resilient video coding & transcoding. He has attracted over £12m worth of research funding to major projects and supervised over 50 PhD students and Research Assistants to successful completion. He frequently serves on influential advisory boards, evaluation panels and international committees, and provides consultancy as well as expert witness services to international Law firms in the area of Video Compression and 2D/3D Visual Media Systems. He has an industry-acclaimed track record of business engagement into academia through a variety of modes and instruments. His business engagement profile has earned him the only academic seat on the Steering Board of the NEM (New European Media) European Technology Platform (2011-14). He is broadly connected in the Telecom and ICT industries and has several entrepreneurial business engagements...

Contenu
Cultural data categorization.- Cultural heritage data sets.- Historical manuscript analysis.- Cultural repository analytics.- Cultural image in painting and completion.- Cultural image super resolution and visual curation.- Cultural object marching and link retrieval.- Natural language processing in the cultural and historical contexts.- Cultural ontology learning.- Data analytics applications for attractiveness and targeted advertising in cultural heritage.