https://hdl.handle.net/10259/3848 2026-05-07T09:25:53Z 2026-05-07T09:25:53Z Antón Maraña, Paula Baruque Zanón, Bruno Porras Alfonso, Santiago Arranz Val, Pablo https://hdl.handle.net/10259/10938 2025-10-09T00:05:34Z 2023-01-01T00:00:00Z

Tourism Reputation Index for Assessing Perceptions on Destinations Using Collaborative Text Data Antón Maraña, Paula; Baruque Zanón, Bruno; Porras Alfonso, Santiago; Arranz Val, Pablo The tourism sector is experiencing a change in trend due to the widespread use of Web 2.0 by tourists. This phenomenon has prompted tourism agents to apply Big Data techniques and Business Intelligence () tools to find out what tourists think. In this sense, the analysis of the Electronic Word Of Mouth in Social Networks is particularly relevant, and the academic literature has faced some difficulties in extracting data, determining its meaning and linking it to the destination. The aim of this work is to is to present the results that can be generated by the design of a tourism reputation index, through the analysis of data created collaboratively on the Twitter Social Network. For this purpose, a BI tool has been created to extract data from short text messages posted by users (tweets) and process them by implementing an Extract, Transform and Load process. In this process, Natural Language Processing is carried out, focusing on Sentiment Analysis tasks, applying an automatic classification model (Multinomial Naive Bayes) and then an automatic thematic categorisation of these texts depending on the words they contain. Subsequently, the data is analysed, calculating a global online reputation index based on sub-indexes of perception on different tourism aspects. These ratings are shown to users through an interactive dashboard, which allows comparison between different queries associated with a tourist location and a temporal period. This research helps tourism agents to better understand the public perception about destinations and make appropriate and well-informed decisions in real time, facilitating the intelligent management of data that enables the creation of smart tourism destinations with competitive, unique and sustainable advantages.; El sector turístico está experimentando un cambio de tendencia ante el uso generalizado de la Web 2.0. por parte de los turistas. Este fenómeno ha impulsado a los agentes turísticos a aplicar técnicas Big Data y herramientas de Inteligencia de Negocio (IN) a la hora de conocer qué piensan y qué desean los turistas. En este sentido, toma especial relevancia el análisis del Electronic Word Of Mouth en las Redes Sociales, y la literatura académica ha encontrado algunas dificultades al extraer datos, determinar su significado y vincularlo con el destino. El objetivo de este trabajo es presentar los resultados que se pueden obtener con el diseño de un índice reputacional turístico, por medio del análisis de los datos creados de manera colaborativa en la Red Social de Twitter. Para ello, se ha creado una herramienta de IN que permite extraer datos de los mensajes cortos de texto publicados por los usuarios (tweets) y procesarlos mediante la implementación de un proceso de Extract, Transform and Load. En este proceso, se realiza un Procesamiento del Lenguaje Natural, centrado en tareas de Análisis de Sentimientos, aplicando un modelo de clasificación automática (Multinomial Naive Bayes) y, seguidamente, una categorización temática automática de estos textos en función de los términos que contengan. Posteriormente, se analizan los datos, calculando un índice de reputación online global a partir de subíndices de percepción sobre diferentes aspectos turísticos. Estos índices se muestran a los usuarios mediante un cuadro de mando interactivo, que permite comparar entre diferentes consultas asociadas a una localidad turística y a un periodo temporal. Este trabajo contribuye a que los agentes turísticos conozcan la percepción sobre los destinos y tomen decisiones adecuadas e informadas en tiempo real, facilitando una gestión inteligente de los datos que permita crear destinos turísticos inteligentes con ventajas competitivas, únicas y sostenibles.

2023-01-01T00:00:00Z Nascimento, Antonia Maiara Marques do Ruiz González, Rubén Martínez-Martínez, Víctor Medeiros, Artur Mendes Santos, Fábio Sandro dos Rêgo, Elizanilda Ramalho do Pimenta, Samy Sudré, Cláudia Pombo Bento, Cintia dos Santos Cambra Baseca, Carlos Barroso, Priscila Alves https://hdl.handle.net/10259/10875 2025-09-16T00:05:35Z 2025-07-01T00:00:00Z

Ornamental Potential Classification and Prediction for Pepper Plants (Capsicum spp.): A Comparison Using Morphological Measurements and RGB Images as Data Source Nascimento, Antonia Maiara Marques do; Ruiz González, Rubén; Martínez-Martínez, Víctor; Medeiros, Artur Mendes; Santos, Fábio Sandro dos; Rêgo, Elizanilda Ramalho do; Pimenta, Samy; Sudré, Cláudia Pombo; Bento, Cintia dos Santos; Cambra Baseca, Carlos; Barroso, Priscila Alves Anticipating the ornamental quality of plants is of significant importance for genetic breeding programs. This study investigated the potential of predicting and classifying whether ornamental pepper plants will exhibit desirable ornamental traits based on RGB images, comparing these results with an approach relying on morphological measurements. To achieve this, pepper plants from fifteen accessions were cultivated, and photographs were taken weekly throughout their growth cycle until fruit maturation. A Vision Transformer (ViT)-based model was employed to predict the suitability of the plants for ornamental purposes, and its predictions were validated against assessments conducted by eight experts. An XGBoost-based classifier was employed as well for estimations based on morphological measurements with an accuracy over 92%. The results showed that the ornamental suitability of plants can be accurately estimated and predicted up to seven weeks in advance from photos, with accuracy over 80%. Interestingly, higher-resolution RGB images did not significantly improve the accuracy of the ViT model. Furthermore, the estimation of ornamental potential using morphological measurements and RGB images yielded similar accuracy, indicating that a single photograph can effectively replace costly and time-consuming morphological measurements. As far as the authors are aware, this work is the first to forecast the ornamental potential of pepper plants (Capsicum spp.) multiple weeks ahead of time using image-based deep learning models.

2025-07-01T00:00:00Z Gil Arroyo, Beatriz Marcos Sanz, Juan Arroyo Puente, Ángel Urda Muñoz, Daniel Basurto Hornillos, Nuño Herrero Cosío, Álvaro https://hdl.handle.net/10259/10545 2025-06-13T07:30:25Z 2025-04-01T00:00:00Z

Dataset for defect detection in textile manufacturing Gil Arroyo, Beatriz; Marcos Sanz, Juan; Arroyo Puente, Ángel; Urda Muñoz, Daniel; Basurto Hornillos, Nuño; Herrero Cosío, Álvaro This dataset, collected during November 2022 at Textil Santanderina, a leading textile manufacturer based in Cabezón de la Sal (Cantabria, Spain), comprises high-resolution images of Batavia and Sarga fabrics. The images were captured as part of a project to document and analyze the intricate weaves and patterns of these fabrics. Using a high-resolution camera under controlled lighting conditions, detailed images were obtained to ensure consistent quality and accurate representation of the fabric's texture and colour. The dataset is provided in processed format, where images have been downscaled from 16 bits to 8 bits, cropped, and classified into cases and controls. The primary reuse potential of this dataset lies in its application for Artificial Intelligence (AI) and Machine Learning (ML) models aimed at defect detection in textile manufacturing. By leveraging these high-quality processed images, researchers and developers can train models to identify and classify various types of fabric defects, such as weave inconsistencies, colour variations, and surface irregularities. This can significantly enhance the efficiency and accuracy of quality control processes in textile production. Additionally, the dataset serves as a valuable resource for academic research in textile engineering and material science. It can be used to study the properties and behaviours of Batavia and Sarga weaves under different conditions, contributing to advancements in fabric design and manufacturing techniques. The detailed visual information provided by the processed images also supports the development of new methodologies for automated textile inspection and quality assurance. By making this dataset available, Textil Santanderina and University of Burgos aim to support innovation and improvement in textile quality control through AI-driven solutions, fostering collaboration and development within the industry. Artículo de datos

2025-04-01T00:00:00Z Sedano, Javier Curiel Herrera, Leticia Elena Corchado, Emilio Cal, Enrique de la Villar, José Ramón https://hdl.handle.net/10259/8583 2024-02-06T01:05:26Z 2010-04-01T00:00:00Z

A soft computing method for detecting lifetime building thermal insulation failures Sedano, Javier; Curiel Herrera, Leticia Elena; Corchado, Emilio; Cal, Enrique de la; Villar, José Ramón The detection of thermal insulation failures in buildings in operation responds to the challenge of improving building energy efficiency. This multidisciplinary study presents a novel four-step soft computing knowledge identification model called IKBIS to perform thermal insulation failure detection. It proposes the use of Exploratory Projection Pursuit methods to study the relation between input and output variables and data dimensionality reduction. It also applies system identification theory and neural networks for modeling the thermal dynamics of the building. Finally, the novel model is used to predict dynamic thermal biases, and two real cases of study as part of its empirical validation.

2010-04-01T00:00:00Z