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. Présenté à. 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
Voir
Manitsaris, S., Glushkova, A., Bevilacqua, F., & Moutarde, F. (2014). Capture, Modeling, and Recognition of Expert Technical Gestures in Wheel-Throwing Art of Pottery. Acm Journal On Computing And Cultural Heritage, 7. https://doi.org/10.1145/2627729 (Original work published jul)
S. Manitsaris A. Glushkova F. Bevilacqua F. MoutardeArtificial intelligence HCI Hidden Markov models Human computer interaction Human computer interfaces Inertial navigation systems Inertial sensor Inertial sensors Intangible cultural heritages Learning systems machine learning Methodological frameworks Modeling and recognition perception Real time Sensory perception Statistical modeling Wheels
Voir
Chau, N. -K., Ma, T. -T., Bouraoui, Z., & Do, T. -N. (2022). A Vietnamese Festival Preservation Application (Vol. 350). In (Vol. 350). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-7618-5_39
N.-K. Chau T.-T. Ma Z. Bouraoui T.-N. DoApplication BERT Chatbot cultural heritage machine learning Ontology Vietnamese festival
Voir
Qu, C., Zhao, T., & Ding, W. (2021). A Narrative Environment Model for the Sustainability of Intangible Cultural Heritage Under the 5G Era (Vol. 1283). In (Vol. 1283). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-62746-1_20
C. Qu T. Zhao W. Ding5G technology Advanced Analytics Big data Brand communication creative industries Data privacy Digital museum Environment modeling Harmonious development Heritage sites Holographic image Image enhancement Immersive Information communication Innovative technology Intangible Heritage Culture (IHC) Intangible cultural heritage Intangible cultural heritages Intellectual property Intellectual property rights Intelligent media technology Interactive narrative spaces Internet of things Laws and legislation Literature reviews machine learning Media technology Model-based OPC Narrative environment Pseudo-environment Soft power Sustainable development Two ways User experience
Voir
Zhang, X., & Jin, Y. (2023). A Method of Protecting Sensitive Information in Intangible Cultural Heritage Communication Network Based on Machine Learning. Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 13656 LNCS, 214-227. https://doi.org/10.1007/978-3-031-20099-1_18
X. Zhang Y. JinCharacteristic indices Communications networks Cryptography Data communication systems Intangible cultural heritages Learning algorithms machine learning Machine learning algorithms Machine-learning Network-based On-machines Sensitive informations Specific values Target tracking Targets tracking Telecommunication networks
Voir
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
Voir
Zhang, X., & Jin, Y. (2023). A Method of Protecting Sensitive Information in Intangible Cultural Heritage Communication Network Based on Machine Learning. Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 13656 LNCS, 214-227. https://doi.org/10.1007/978-3-031-20099-1_18
X. Zhang Y. JinCharacteristic indices Communications networks Cryptography Data communication systems Intangible cultural heritages Learning algorithms machine learning Machine learning algorithms Machine-learning Network-based On-machines Sensitive informations Specific values Target tracking Targets tracking Telecommunication networks
Voir
Zhang, X., & Jin, Y. (2023). A Method of Protecting Sensitive Information in Intangible Cultural Heritage Communication Network Based on Machine Learning. Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 13656 LNCS, 214-227. https://doi.org/10.1007/978-3-031-20099-1_18
X. Zhang Y. JinCharacteristic indices Communications networks Cryptography Data communication systems Intangible cultural heritages Learning algorithms machine learning Machine learning algorithms Machine-learning Network-based On-machines Sensitive informations Specific values Target tracking Targets tracking Telecommunication networks
Voir
Zhang, X., & Jin, Y. (2023). A Method of Protecting Sensitive Information in Intangible Cultural Heritage Communication Network Based on Machine Learning. Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 13656 LNCS, 214-227. https://doi.org/10.1007/978-3-031-20099-1_18
X. Zhang Y. JinCharacteristic indices Communications networks Cryptography Data communication systems Intangible cultural heritages Learning algorithms machine learning Machine learning algorithms Machine-learning Network-based On-machines Sensitive informations Specific values Target tracking Targets tracking Telecommunication networks
Voir