An artificial neural network framework for classifying the style of cypriot hybrid examples of built heritage in 3D

Artopoulos, Georgios; Maslioukova, Maria; Zavou, Christina; Loizou, Marios; Deligiorgi, Marissia; Averkiou, Melinos

doi:10.1016/j.culher.2023.07.016

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…( 4). It represents the proportion of positive data points that are accurately identified as positive, among all positive instances [36].…”

Section: ) Classification Methodsmentioning

confidence: 99%

Predicting Optimal Learning Approaches for Nursing Students in Morocco

Fadili,

Ertel,

Mengad

et al. 2024

IJACSA

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In nursing education, recognizing and accommodating diverse learning styles is imperative for the development of effective educational programs and the success of nursing students. This article addresses the crucial challenge of classifying the learning styles of nursing students in Morocco, where contextual studies are limited. To address this research gap, a contextual approach is proposed, aiming to develop a predictive model of the most appropriate learning approach (observational, experiential, reflective and active) for each nursing student in Morocco. This model incorporates a comprehensive set of variables such as age, gender, education, work experience, preferred learning strategies, engagement in social activities, attitudes toward failure, and self-assessment preferences. We used four multivariate machine learning algorithms, namely SVM, Tree, Neural Network, and Naive Bayes, to determine the most reliable and effective classifiers. The results show that neural network and decision tree classifiers are particularly powerful in predicting the most suitable learning approach for each nursing student. This research endeavors to enhance the success of nursing students and raise the overall quality of healthcare delivery in the country by tailoring educational programs to match individual learning styles.

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“…( 4). It represents the proportion of positive data points that are accurately identified as positive, among all positive instances [36].…”

Section: ) Classification Methodsmentioning

confidence: 99%

Predicting Optimal Learning Approaches for Nursing Students in Morocco

Fadili,

Ertel,

Mengad

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…Researchers [22,23,27,[29][30][31][32][33] utilized historical building datasets for classification and segmentation to identify historical architectural elements, and even recognize the styles [34]. Point clouds automatic semantic segmentation is also an important step towards BIM models [17].…”

Section: D Models Segmentation and Classification Technologymentioning

confidence: 99%

“…And finally visualization and knowledge graph is accessible to users on web by WebGL [52]. The paper [34] developed the server side to process point clouds, segment historical buildings and classify their styles for Cypriot architectural buildings, and users can visualize the heritage assets from browser by WebGL [52,53]. Recently parametric BIM models are becoming more established practice, that are able to extract valuable information.…”

Section: Digital Twin Platformmentioning

confidence: 99%

Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

Pan,

Lin,

et al. 2024

Herit Sci

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This study focuses on the application of deep learning for transforming semantic point clouds into semantic Building Information Models (BIM) to create a Heritage Digital Twin, centering on Taoping Village, a site of historical and cultural significance in Sichuan, China. Utilizing advanced technologies such as unmanned aerial vehicles and terrestrial laser scanning, we capture detailed point cloud data of the village. A pivotal element of our methodology is the KP-SG neural network, which exhibits outstanding overall performance, particularly excelling in accurately identifying 11 categories. Among those categories, buildings and vegetation, achieves recognition rates of 81% and 83% respectively, and a 2.53% improvement in mIoU compared to KP-FCNN. This accuracy is critical for constructing detailed and accurate semantic BIM models of Taoping Village, facilitating comprehensive architecture and landscape analysis. Additionally, the KP-SG’s superior segmentation capability contributes to the creation of high-fidelity 3D models, enriching virtual reality experiences. We also introduce a digital twin platform that integrates diverse datasets, their semantic information, and visualization tools. This platform is designed to support process automation and decision-making and provide immersive experiences for tourists. Our approach, integrating semantic BIM models and a digital twin platform, marks a significant advancement in preserving and understanding traditional villages like Taoping and demonstrates the transformative potential of deep learning in cultural heritage conservation.

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“…The ANN model, for example, can classify ceramic artifacts based on their origin [13]. Deep Neural Network (DNN) and Support Vector Machine (SVM) have been used to classify architectural heritage [14]. Machine learning aids in the conservation of immovable artifacts, with models like Relevance Vector Machine (RVM) predicting diseases and the Gray Model (GM) and Verhulst model forecasting crack trends in immovable artifacts [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A color prediction model for mending materials of the Yuquan Iron Pagoda based on machine learning

Liu,

Wang

et al. 2024

Preprint

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During the restoration of iron cultural relics, the removal of rust from these artifacts is necessary. However, this rust removal process may lead to inconsistent local color on the iron relics. To address this, mending materials are applied to treat the surface, ensuring consistent local color. In the surface treatment of iron cultural relics, a significant challenge lies in modulating the color of these mending materials. This study employs machine learning methods to predict the color of mending materials corresponding to varying contents of Fe2O3, Fe3O4, and epoxy resin. The Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boost Machine (LightGBM) algorithms are utilized to develop the model, and the predictive performance of these three algorithms is compared. XGBoost exhibits the best prediction performance, achieving a square correlation coefficient (R2) of 0.94238 and a mean absolute error (MAE) of 0.68485. Additionally, the SHapley Additive exPlanations (SHAP) method is employed to analyze the most crucial raw material affecting the color of mending materials, which is identified as Fe3O4. The study illustrates the specific process of employing this model by applying it to the surface treatment of the Yuquan Iron Pagoda, demonstrating the practicality of the model. This model can be applied to assist in the surface treatment of other iron cultural relics.

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An artificial neural network framework for classifying the style of cypriot hybrid examples of built heritage in 3D

Cited by 8 publications

References 18 publications

Predicting Optimal Learning Approaches for Nursing Students in Morocco

Predicting Optimal Learning Approaches for Nursing Students in Morocco

Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

A color prediction model for mending materials of the Yuquan Iron Pagoda based on machine learning

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