Xuelin, Q., Jue, H., Ying, S., & Zheng, L. Digital Style Design of Nanjing Brocade Based on Deep Learning. 339-342. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICCST53801.2021.00077
Q. Xuelin H. Jue S. Ying L. ZhengComponent: nanjing brocade Deep learning Digital methods Digital protection Digitisation E-learning Intangible cultural heritages Nanjing Silk fabrics Style designs Style transfer Textiles component: nanjing brocade Deep learning Digital protection style transfer
View
Vadamodula, P., Cristin, R., & Daniya, T. Design and Development of a Digital Preservation Voice Enabled Application for Cultural Heritage Towards Fishing in Vernacular Language. 475, 295-308. Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-2840-6_23
Prasad Vadamodula R. Cristin T. DaniyaCoders and encoders Culture Deep learning Fish farming heritage Natural language processing Synthesizers
View
Sharma, R., & Kukreja, V. Deep learning-Based Comic Recognition and Analysis for the Preservation of Indigenous Intangible Heritage. 136-140. https://doi.org/10.1109/ICSH57060.2023.10482818
R. Sharma V. KukrejaComic recognition convolutional neural network Convolutional neural networks cultural heritage Cultural heritages Deep learning Fast region-based convolutional neural network Faster R-CNN Historic preservation Indigenous intangible heritage Language processing Learning systems NLP Natural language processing Natural language processing systems Natural languages Region-based
View
Putra, F. A., Jamil, D. A. C., Prabandanu, B. A., Faruq, S., Pradana, F. A., Alya, R. F., et al. (2021). Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method. Presented at the. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICIC54025.2021.9632937
F.A. Putra D.A.C. Jamil B.A. Prabandanu S. Faruq F.A. Pradana R.F. Alya H.A. Santoso F. Al Zami F.O. SaputraAuthentication Batik Batik authenticity classification Batik authenticity classification CNN Classification (of information) Convolution convolutional neural network Convolutional neural networks Crime Deep learning Historic preservation Indonesia Learning algorithms machine learning Machine learning models Machine-learning Neural networks algorithms Transfer learning Transfer learning Transfer learning methods
View
Tang, M., Xie, S., He, M., & Liu, X. (2022). Character Recognition in Endangered Archives: Shui Manuscripts Dataset, Detection and Application Realization. Applied Sciences, 12. https://doi.org/10.3390/app12115361 (Original work published jun)
Minli Tang Shaomin Xie Mu He Xiangrong LiuShui manuscript character recognition dataset Deep learning target detection
View
Rallis, I., Bakalos, N., Doulamis, N., Doulamis, A., & Voulodimos, A. (2021). Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies. Visual Computer, 37, 47-62. https://doi.org/10.1007/s00371-019-01741-3 (Original work published jan)
Ioannis Rallis Nikolaos Bakalos Nikolaos Doulamis Anastasios Doulamis Athanasios VoulodimosBidirectional LSTM Brain Dance summarization Deep learning Educational learning Gesture recognition Hierarchical representation Identification procedure Intangible cultural heritage Intangible cultural heritages Learning systems Long short-term memory Pose identification Quantitative assessments Representative selection Serious games
View
He, J., & Tao, H. (2025). Applied research on innovation and development of blue calico of Chinese intangible cultural heritage based on artificial intelligence. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-96587-2
Jialiang He Hong TaoApplied research article Artificial intelligence Color controlled study Deep learning Feature extraction Generative adversarial network generator human Inheritance
View
Wang, Y., & Fu, R. (2020). A Methodological Reflection: Deconstructing Cultural Elements for Enhancing Cross-Cultural Appreciation of Chinese Intangible Cultural Heritage (Vol. 12215 LNCS). In (Vol. 12215 LNCS). Springer. https://doi.org/10.1007/978-3-030-50267-6_32
Y. Wang R. FuAdversarial networks Algorithm model Artificial intelligence Bamboo Bamboo weaving patterns Blue calico Case-studies Chinese people Co-design Computer graphics Computer image processing Computer science Computer Vision Computers Cotton fabrics Deep learning Design ethnography Digital cultural heritage Employment Evaluation metrics Experience-centred design (ECD) Generative adversarial networks Hci researches Human computer interaction Human computer interaction (HCI) Image generations Image processing Intangible Cultural Heritage Protection Intangible cultural heritage regeneration Intangible cultural heritages Jiangxi Province Learning systems machine learning New approaches Pattern designs Practical method Printed patterns Printing and dyeing Research through design (RtD) Stress intensity factors Traditional Chinese painting User satisfaction Weaving patterns
View
Bizzarri, S., De Gennaro, T., Careccia, C., Bertozzi, A., Degli Esposti, M., Al Dahini, H. M., & Al Ghefeili, B. R. (2022). A Multidisciplinary Approach to the Conservation of Salut, Sultanate of Oman. Studies In Conservation, 67, 23-31. https://doi.org/10.1080/00393630.2022.2084965 (Original work published aug)
Stefano Bizzarri Tiziana De Gennaro Caterina Careccia Andrea Bertozzi Michele Degli Esposti Huda Al Dahini Buthaina Al Ghefeili3D model 3D models Architectural heritage architecture Deep learning Digital storage drone survey Drones Earthen architectures Historic preservation Knowledge management Knowledge transfer mud plaster Mudbrick Multi-disciplinary approach Personnel training Stratigraphy Students Surveys Three dimensional computer graphics Traditional masonries Architectural heritage drone survey Earthen architecture Knowledge transfer mud plaster traditional masonry
View