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170 results on '"Salcedo‐Sanz, Sancho"'

1. AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

Author

Camps-Valls, Gustau, Fernández-Torres, Miguel-Ángel, Cohrs, Kai-Hendrik, Höhl, Adrian, Castelletti, Andrea, Pacal, Aytac, Robin, Claire, Martinuzzi, Francesco, Papoutsis, Ioannis, Prapas, Ioannis, Pérez-Aracil, Jorge, Weigel, Katja, Gonzalez-Calabuig, Maria, Reichstein, Markus, Rabel, Martin, Giuliani, Matteo, Mahecha, Miguel, Popescu, Oana-Iuliana, Pellicer-Valero, Oscar J., Ouala, Said, Salcedo-Sanz, Sancho, Sippel, Sebastian, Kondylatos, Spyros, Happé, Tamara, and Williams, Tristan

Subjects
Computer Science - Artificial Intelligence, Physics - Atmospheric and Oceanic Physics, Physics - Geophysics
Abstract

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction.

Published
2024

2. Multivariate-Autoencoder Flow-Analogue Method for Heat Waves Reconstruction

Author

Marina, Cosmin M., Lorente-Ramos, Eugenio, Ayllón-Gavilán, Rafael, Gutiérrez, Pedro Antonio, Pérez-Aracil, Jorge, Salcedo-Sanz, Sancho, 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, Alonso-Betanzos, Amparo, editor, Guijarro-Berdiñas, Bertha, editor, Bolón-Canedo, Verónica, editor, Hernández-Pereira, Elena, editor, Fontenla-Romero, Oscar, editor, Camacho, David, editor, Rabuñal, Juan Ramón, editor, Ojeda-Aciego, Manuel, editor, Medina, Jesús, editor, Riquelme, José C., editor, and Troncoso, Alicia, editor

Published
2024
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3. Machine Learning as Applied to Shape Parameterization of Submerged Arch Structures

Author

LLamosas-Mayca, Waldemar Hugo, Lorente-Ramos, Eugenio, Pérez-Aracil, Jorge, Hernández-Díaz, Alejandro M., García-Román, Manuel Damián, Salcedo-Sanz, Sancho, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Val Calvo, Mikel, editor, and Adeli, Hojjat, editor

Published
2024
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4. Medium- and Long-Term Wind Speed Prediction Using the Multi-task Learning Paradigm

Author

Gómez-Orellana, Antonio M., Vargas, Víctor M., Guijo-Rubio, David, Pérez-Aracil, Jorge, Gutiérrez, Pedro A., Salcedo-Sanz, Sancho, Hervás-Martínez, César, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Val Calvo, Mikel, editor, and Adeli, Hojjat, editor

Published
2024
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5. Data Augmentation Techniques for Extreme Wind Prediction Improvement

Author

Vega-Bayo, Marta, Gómez-Orellana, Antonio Manuel, Yun, Víctor Manuel Vargas, Guijo-Rubio, David, Cornejo-Bueno, Laura, Pérez-Aracil, Jorge, Salcedo-Sanz, Sancho, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Val Calvo, Mikel, editor, and Adeli, Hojjat, editor

Published
2024
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6. Energy Flux Prediction Using an Ordinal Soft Labelling Strategy

Author

Gómez-Orellana, Antonio M., Vargas, Víctor M., Gutiérrez, Pedro A., Pérez-Aracil, Jorge, Salcedo-Sanz, Sancho, Hervás-Martínez, César, Guijo-Rubio, David, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Val Calvo, Mikel, editor, and Adeli, Hojjat, editor

Published
2024
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7. Machine Learning Health Estimation for Lithium-Ion Batteries Under Varied Conditions

Author

Leite, Gabriel M. C., Pérez-Aracil, Jorge, Marcelino, Carolina Gil, García-Gutiérrez, Gabriel, Prodanovic, Milan, García-Quismondo, Enrique, Pinilla, Sergio, Palma, Jesús, Jiménez-Fernández, Silvia, Salcedo-Sanz, Sancho, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Val Calvo, Mikel, editor, and Adeli, Hojjat, editor

Published
2024
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9. A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas

Author

Fister Jr., Iztok, Fister, Iztok, Fister, Dušan, Podgorelec, Vili, and Salcedo-Sanz, Sancho

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence
Abstract

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method's main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper.

Published
2023

11. Time series numerical association rule mining variants in smart agriculture

Author

Fister Jr., Iztok, Fister, Dušan, Fister, Iztok, Podgorelec, Vili, and Salcedo-Sanz, Sancho

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method's potential and opened the door for further extension.

Published
2022

12. New Probabilistic-Dynamic Multi-Method Ensembles for Optimization based on the CRO-SL

Author

Pérez-Aracil, Jorge, Camacho-Gómez, Carlos, Lorente-Ramos, Eugenio, Marina, Cosmin M., and Salcedo-Sanz, Sancho

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Mathematics - Optimization and Control, 68T01 and 68T20
Abstract

In this paper we propose new probabilistic and dynamic (adaptive) strategies to create multi-method ensembles based on the Coral Reefs Optimization with Substrate Layers (CRO-SL) algorithm. The CRO-SL is an evolutionary-based ensemble approach, able to combine different search procedures within a single population. In this work we discuss two different probabilistic strategies to improve the algorithm. First, we defined the Probabilistic CRO-SL (PCRO-SL), which substitutes the substrates in the CRO-SL population by {\em tags} associated with each individual. Each tag represents a different operator which will modify the individual in the reproduction phase. In each generation of the algorithm, the tags are randomly assigned to the individuals with a similar probability, obtaining this way an ensemble with a more intense change in the application of different operators to a given individual than the original CRO-SL. The second strategy discussed in this paper is the Dynamical Probabilistic CRO-SL (DPCRO-SL), in which the probability of tag assignment is modified during the evolution of the algorithm, depending on the quality of the solutions generated in each substrate. Thus, the best substrates in the search process will be assigned with a higher probability that those which showed a worse performance during the search. We test the performance of the proposed probabilistic and dynamic ensembles in different optimization problems, including benchmark functions and a real application of wind turbines layout optimization, comparing the results obtained with that of existing algorithms in the literature., Comment: 18 pages, 6 figures, 5 tables

Published
2022

13. Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques

Author

Fister, Dušan, Pérez-Aracil, Jorge, Peláez-Rodríguez, César, Del Ser, Javier, and Salcedo-Sanz, Sancho

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, 68T07, 62P12, 68T01, 68T05
Abstract

In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction techniques are proposed, for a problem of long-term summer air temperature prediction. Specifically, the prediction of average air temperature in the first and second August fortnights, using input data from previous months, at two different locations, Paris (France) and C\'ordoba (Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the mega-heatwave of 2003, which affected France and the Iberian Peninsula. Thus, an accurate prediction of long-term air temperature may be valuable also for different problems related to climate change, such as attribution of extreme events, and in other problems related to renewable energy. The analysis carried out this work is based on Reanalysis data, which are first processed by a correlation analysis among different prediction variables and the target (average air temperature in August first and second fortnights). An area with the largest correlation is located, and the variables within, after a feature selection process, are the input of different deep learning and ML algorithms. The experiments carried out show a very good prediction skill in the three proposed AI frameworks, both in Paris and C\'ordoba regions., Comment: 33 pages, 14 figures, 7 tables, under review

Published
2022

15. Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Author

Salcedo-Sanz, Sancho, Pérez-Aracil, Jorge, Ascenso, Guido, Del Ser, Javier, Casillas-Pérez, David, Kadow, Christopher, Fister, Dusan, Barriopedro, David, García-Herrera, Ricardo, Restelli, Marcello, Giuliani, Mateo, and Castelletti, Andrea

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Physics - Atmospheric and Oceanic Physics, Physics - Physics and Society, 68T01, 62P12 (primary), 68T05, 68T07, (secondary), I.2, I.5
Abstract

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn., Comment: 93 pages, 18 figures, under review

Published
2022

22. Examining 5G Technology-Based Applications for Military Communications

Author

Portilla-Figueras, Antonio, Llopis-Sánchez, Salvador, Jiménez-Fernández, Silvia, Salcedo-Sanz, Sancho, 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, Katsikas, Sokratis, editor, Cuppens, Frédéric, editor, Kalloniatis, Christos, editor, Mylopoulos, John, editor, Pallas, Frank, editor, Pohle, Jörg, editor, Sasse, M. Angela, editor, Abie, Habtamu, editor, Ranise, Silvio, editor, Verderame, Luca, editor, Cambiaso, Enrico, editor, Maestre Vidal, Jorge, editor, Sotelo Monge, Marco Antonio, editor, Albanese, Massimiliano, editor, Katt, Basel, editor, Pirbhulal, Sandeep, editor, and Shukla, Ankur, editor

Published
2023
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38. Asynchronous and Distributed Multi-agent Systems: An Approach Using Actor Model

Author

Reis, Felipe D., Nascimento, Tales B., Marcelino, Carolina G., Wanner, Elizabeth F., Borges, Henrique E., Salcedo-Sanz, Sancho, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pereira, Ana I., editor, Košir, Andrej, editor, Fernandes, Florbela P., editor, Pacheco, Maria F., editor, Teixeira, João P., editor, and Lopes, Rui P., editor

Published
2022
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39. Active GA Accelerated by Simulated Annealing to Solve SPP in Packet Networks

Author

Fonseca, Daniel S., Wanner, Elizabeth F., Marcelino, Carolina G., Silva, Gabriel P., Jimenez-Fernandez, Silvia, Salcedo-Sanz, Sancho, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pereira, Ana I., editor, Košir, Andrej, editor, Fernandes, Florbela P., editor, Pacheco, Maria F., editor, Teixeira, João P., editor, and Lopes, Rui P., editor

Published
2022
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40. A Multi-objective Approach for the Menu Planning Problem: A Brazilian Case Study

Author

Moreira, Rafaela P. C., Marcelino, Carolina G., Martins, Flávio V. C., Wanner, Elizabeth F., Jimenez-Fernandez, Silvia, Salcedo-Sanz, Sancho, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pereira, Ana I., editor, Košir, Andrej, editor, Fernandes, Florbela P., editor, Pacheco, Maria F., editor, Teixeira, João P., editor, and Lopes, Rui P., editor

Published
2022
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