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11 results on '"de la Cal, Enrique"'

1. Deep Learning for Scene Recognition from Visual Data: A Survey

Author

Matei, Alina, Glavan, Andreea, Talavera Martínez, Estefanía, de la Cal, Enrique Antonio, Villar Flecha, José Ramón, and Corchado, Emilio

Subjects
Scene Recognition, Information retrieval, business.industry, Computer science, Computer Vision, Model selection, Deep learning, Perspective (graphical), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Field (computer science), Task (project management), Deep Learning, Ensemble Techniques, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Single image, business, 0105 earth and related environmental sciences
Abstract

The use of deep learning techniques has exploded during the last few years, resulting in a direct contribution to the field of artificial intelligence. This work aims to be a review of the state-of-the-art in scene recognition with deep learning models from visual data. Scene recognition is still an emerging field in computer vision, which has been addressed from a single image and dynamic image perspective. We first give an overview of available datasets for image and video scene recognition. Later, we describe ensemble techniques introduced by research papers in the field. Finally, we give some remarks on our findings and discuss what we consider challenges in the field and future lines of research. This paper aims to be a future guide for model selection for the task of scene recognition.

Published
2020
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3. Optimising operational costs using Soft Computing techniques.

Author

Sedano, Javier, Berzosa, Alba, Villar, José R., Corchado, Emilio, and de la Cal, Enrique

Subjects
SOFT computing, MANUFACTURING execution systems, MANUFACTURING processes, COMPUTER software, FUZZY systems, ARTIFICIAL intelligence, ENTERPRISE resource planning
Abstract

A Manufacturing Execution System (MES) consists of high-cost, large-scale, multi-task software systems. Companies and factories apply these complex applications for the purposes of production management to monitor and track all aspects of factory-based manufacturing processes. Nevertheless, companies seek to control the production process with even greater rigour. Improvements associated with an MES involve the identification of new knowledge within the data set and its integration in the system, which implies a step forward to Business Process Management (BPM) systems, from which the users of an MES may gain relevant information, not only on execution procedures but to decide on the best scheduled arrangement. This work studies the data gathered from a real MES that is used in a plastic products factory. Several Artificial Intelligence and Soft Computing modelling methods based on fuzzy rules assist in the calculation of manufacturing costs and decisions over shift work rotas: two decisions that are of relevance for the improvement of the execution system. The results of the study, which identify the most suitable models to facilitate execution-related decision-making, are presented and discussed. [ABSTRACT FROM AUTHOR]

Published
2011
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4. Hackathon in Teaching: Applying Machine Learning to Life Sciences Tasks

Author

Guijo-Rubio, David, Vargas, Víctor M., Barbero-Gómez, Javier, Die, Jose V., González-Moreno, Pablo, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Villar Flecha, José Ramón, editor, Troncoso Lora, Alicia, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Martínez Álvarez, Francisco, editor, Psaila, Giuseppe, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2023
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5. A Virtual Sensor Approach to Estimate the Stainless Steel Final Chemical Characterisation

Author

Nimo, Damián, González-Enrique, Javier, Perez, David, Almagro, Juan, Urda, Daniel, Turias, Ignacio J., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martinez-de-Pison, Francisco Javier, editor, Villar Flecha, José Ramón, editor, Troncoso Lora, Alicia, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Martínez Álvarez, Francisco, editor, Psaila, Giuseppe, editor, Quintián, Héctor, editor, and Corchado Rodriguez, Emilio S., editor

Published
2023
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6. Smash: A Compression Benchmark with AI Datasets from Remote GPU Virtualization Systems

Author

Peñaranda, Cristian, Reaño, Carlos, Silla, Federico, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Villar Flecha, José Ramón, editor, Troncoso Lora, Alicia, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Martínez Álvarez, Francisco, editor, Psaila, Giuseppe, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2022
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7. A Machine Consciousness Architecture Based on Deep Learning and Gaussian Processes

Author

Merchán, Eduardo C. Garrido, Molina, Martin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, de la Cal, Enrique Antonio, editor, Villar Flecha, José Ramón, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2020
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8. Multi-objective learning of white box models with low quality data

Author

Villar, José R., Berzosa, Alba, de la Cal, Enrique, Sedano, Javier, and García-Tamargo, Marco

Subjects
*MACHINE learning, *DATA quality, *ENERGY consumption, *ENGINEERS, *TECHNOLOGICAL innovations, *ARTIFICIAL intelligence, *ALGORITHMS
Abstract

Abstract: Improving energy efficiency in buildings represents one of the main challenges faced by engineers. In fields like lighting control systems, the effect of low quality sensors compromises the control strategy and the emergence of new technologies also degrades the data quality introducing linguistic values. This research analyzes the aforementioned problem and shows that, in the field of lighting control systems, the uncertainty in the measurements gathered from sensors should be considered in the design of control loops. To cope with this kind of problems Hybrid Intelligent methods will be used. Moreover, a method for learning equation-based white box models with this low quality data is proposed. The equation-based models include a representation of the uncertainty inherited in the data. Two different evolutive algorithms are use for learning the models: the well-known NSGA-II genetic algorithm and a multi-objective simulated annealing algorithm hybridized with genetic operators. The performance of both algorithms is found valid to evolve this learning process. This novel approach is evaluated with synthetic problems. [Copyright &y& Elsevier]

Published
2012
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9. Evolutionary Computation on Road Safety

Author

Fernandes, Bruno, Vicente, Henrique, Ribeiro, Jorge, Analide, Cesar, Neves, José, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, de Cos Juez, Francisco Javier, editor, Villar, José Ramón, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Quintián, Héctor, editor, Sáez, José António, editor, and Corchado, Emilio, editor

Published
2018
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10. Prediction of the Energy Demand of a Hotel Using an Artificial Intelligence-Based Model

Author

Casteleiro-Roca, José-Luis, Gómez-González, José Francisco, Calvo-Rolle, José Luis, Jove, Esteban, Quintián, Héctor, Martín, Juan Francisco Acosta, Perez, Sara Gonzalez, Diaz, Benjamin Gonzalez, Calero-Garcia, Francisco, Méndez-Perez, Juan Albino, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, de Cos Juez, Francisco Javier, editor, Villar, José Ramón, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Quintián, Héctor, editor, Sáez, José António, editor, and Corchado, Emilio, editor

Published
2018
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11. An Evolutionary Approach to Automatic Keyword Selection for Twitter Data Analysis

Author

Beatriz de la Iglesia, Oduwa Edo-Osagie, Iain R. Lake, Obaghe Edeghere, de la Cal, Enrique Antonio, Villar Flecha, José Ramón, Quintián, Héctor, and Corchado, Emilio

Subjects
Data collection, Computer science, business.industry, Deep learning, 020208 electrical & electronic engineering, Context (language use), 02 engineering and technology, Machine learning, computer.software_genre, Field (computer science), Evolutionary computation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Baseline (configuration management), business, computer, Selection (genetic algorithm)
Abstract

In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.

Published
2020

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