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Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user's maintenance requests

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Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user's maintenance requests

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D'orazio, M.; Bernardini, G.; Di Giuseppe, E. (2023). Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user's maintenance requests. VITRUVIO - International Journal of Architectural Technology and Sustainability. 8:18-29. https://doi.org/10.4995/vitruvio-ijats.2023.18811

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Título: Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user's maintenance requests
Autor: D'Orazio, Marco Bernardini, Gabriele Di Giuseppe, Elisa
Fecha difusión:
Resumen:
[EN] Preventive conservation of cultural heritage can avoid or minimize future damage, deterioration, loss and consequently, any invasive intervention. Recently, Machine Learning methods were proposed to support preventive ...[+]
Palabras clave: Cultural heritage , Preventive conservation , Maintenance , NLP , Neural Networks
Derechos de uso: Reconocimiento - No comercial (by-nc)
Fuente:
VITRUVIO - International Journal of Architectural Technology and Sustainability. (eissn: 2444-9091 )
DOI: 10.4995/vitruvio-ijats.2023.18811
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/vitruvio-ijats.2023.18811
Tipo: Artículo

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