@article{CNNJaviBarbero2025, author = "Javier Barbero-G{\'o}mez and Cruz, Ricardo P.M. and Cardoso, Jaime S. and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The use of Convolutional Neural Network (CNN) models for image classification tasks has gained significant popularity. However, the lack of interpretability in CNN models poses challenges for debugging and validation. To address this issue, various explanation methods have been developed to provide insights into CNN models. This paper focuses on the validity of these explanation methods for ordinal regression tasks, where the classes have a predefined order relationship. Different modifications are proposed for two explanation methods to exploit the ordinal relationships between classes: Grad-CAM based on Ordinal Binary Decomposition (GradOBD-CAM) and Ordinal Information Bottleneck Analysis (OIBA). The performance of these modified methods is compared to existing popular alternatives. Experimental results demonstrate that GradOBD-CAM outperforms other methods in terms of interpretability for three out of four datasets, while OIBA achieves superior performance compared to IBA.", awards = "JCR(2023): 5.5, Position: 42/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2023): 5.5, Position: 42/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.neucom.2024.128878", issn = "0925-2312", journal = "Neurocomputing", keywords = "convolutional neural networks, explanation methods, ordinal regression", note = "JCR(2023): 5.5, Position: 42/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "128878", title = "{CNN} {E}xplanation {M}ethods for {O}rdinal {R}egression {T}asks", url = "doi.org/10.1016/j.neucom.2024.128878", volume = "615", year = "2025", } @article{aPOR2024, author = "C{\'e}sar Pel{\'a}ez-Rodr{\'i}guez and Jorge P{\'e}rez-Aracil and Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Sancho Salcedo-Sanz", abstract = "Providing an accurate prediction of Significant Wave Height (SWH), and specially of extreme SWH events, is crucial for coastal engineering activities and holds major implications in several sectors as offshore renewable energy. With the aim of overcoming the challenge of skewness and imbalance associated with the prediction of these extreme SWH events, a fuzzy-based cascade ensemble of regression models is proposed. This methodology allows to remarkably improve the predictive performance on the extreme SWH values, by using different models specialised in different ranges on the target domain. The method’s explainability is enhanced by analysing the contribution of each model, aiding in identifying those predictor variables more characteristic for the detection of extreme SWH events. The methodology has been validated tackling a long-term SWH prediction problem, considering two case studies over the southwest coast of the United States of America. Both reanalysis data, providing information on various meteorological factors, and SWH measurements, obtained from the nearby stations and the station under examination, have been considered. The goodness of the proposed approach has been validated by comparing its performance against several machine learning and deep learning regression techniques, leading to the conclusion that fuzzy ensemble models perform much better in the prediction of extreme events, at the cost of a slight deterioration in the rest of the samples. The study contributes to advancing the SWH prediction field, specially, to understanding the behaviour behind extreme SWH events, critical for various sectors reliant on oceanic conditions.", awards = "JCR (2023): 4.3, Position: 5/65 (Q1), Category: OCEANOGRAPHY", comments = "JCR (2023): 4.3, Position: 5/65 (Q1), Category: OCEANOGRAPHY", doi = "10.1016/j.apor.2024.104273", issn = "0141-1187", journal = "Applied Ocean Research", keywords = "Extreme significant wave height, Energy flux, Ensemble models, Long-term prediction, Explainable artificial intelligence", month = "December", note = "JCR (2023): 4.3, Position: 5/65 (Q1), Category: OCEANOGRAPHY", pages = "104273", title = "{F}uzzy-based ensemble methodology for accurate long-term prediction and interpretation of extreme significant wave height events", url = "doi.org/10.1016/j.apor.2024.104273", volume = "153", year = "2024", } @article{Chemuca_part1_2024, author = "Irene Trinidad-Guti{\'e}rrez and Mari C. V{\'a}zquez-Borrego and Eva Aguilera-Fern{\'a}ndez and Juan E. Velez-Casta{\~n}o and Carlos E. Muriel-L{\'o}pez and Lidia Rodr{\'i}guez-Ort{\'i}z and Antonio Manuel G{\'o}mez-Orellana and Francisco B{\'e}rchez-Moreno and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Antonio Romero-Ruiz and {\'A}lvaro Arjona-S{\'a}nchez", abstract = "Locally advanced colon cancer is a high-risk condition for tumour recurrence with poor survival. The current treatment is surgery followed by adjuvant chemotherapy based on fluoropyrimidines and oxaliplatin. This approach has improved the oncological outcomes on this population, however the mucinous condition has not been studied in depth and although the evidence is weak, it is thought to have a worse response to systemic chemotherapy. The CHEMUCCA study aims to answer this question.", awards = "JCR (2023): 3.5, Position: 31/290 (Q1), Category: SURGERY", comments = "JCR (2023): 3.5, Position: 31/290 (Q1), Category: SURGERY", doi = "10.1016/j.ejso.2024.108642", issn = "1532-2157", journal = "European Journal of Surgical Oncology", keywords = "Mucinous colon cancer, Systematic chemotherapy, Rectal cancer", month = "November", note = "JCR (2023): 3.5, Position: 31/290 (Q1), Category: SURGERY", title = "{E}fficacy of systemic {C}hemotherapy on high-risk stage {II} and {III} {M}ucnious colon cancer. {CHEMUCCA} study part {I}", url = "www.sciencedirect.com/science/article/pii/S0748798324006942?via%3Dihub", volume = "50", year = "2024", } @article{JorgeAE2024ACG, author = "Jorge P{\'e}rez-Aracil and D. Fister and C.M. Marina and C. Pel{\'a}ez-Rodr{\'i}guez and L. Cornejo-Bueno and Pedro Antonio Guti{\'e}rrez and M. Giuliani and A. Castelleti and Sancho Salcedo-Sanz", abstract = "This paper proposes two hybrid approaches based on Autoencoders (AEs) for long-term temperature prediction. The first algorithm comprises an AE trained to learn temperature patterns, which is then linked to a second AE, used to detect possible anomalies and provide a final temperature prediction. The second proposed approach involves training an AE and then using the resulting latent space as input of a neural network, which will provide the final prediction output. Both approaches are tested in long-term air temperature prediction in European cities: seven European locations where major heat waves occurred have been considered. The long-term temperature prediction for the entire year of the heatwave events has been analysed. Results show that the proposed approaches can obtain accurate long-term (up to 4 weeks) temperature prediction, improving Persistence and Climatology in the benchmark models compared. In heatwave periods, where the persistence of the temperature is extremely high, our approach beat the persistence operator in three locations and works similarly in the rest of the cases, showing the potential of this AE-based method for long-term temperature prediction.", awards = "JCR (2023): 2.6, Position: 98/253 (Q2), Category: GEOSCIENCES, MULTIDISCIPLINARY", comments = "JCR (2023): 2.6, Position: 98/253 (Q2), Category: GEOSCIENCES, MULTIDISCIPLINARY", doi = "10.1016/j.acags.2024.100185", issn = "2590-1974", journal = "Applied Computing and Geosciences", keywords = "Autoencoder, Temperature prediction, Hybrid models, Heatwave", month = "September", note = "JCR (2023): 2.6, Position: 98/253 (Q2), Category: GEOSCIENCES, MULTIDISCIPLINARY", pages = "100185", title = "{L}ong-term temperature prediction with hybrid autoencoder algorithms", url = "doi.org/10.1016/j.acags.2024.100185", volume = "23", year = "2024", } @article{262024, author = "Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and V{\'i}ctor Manuel Vargas", abstract = "In this paper we present a novel methodology, referenced as ORFEO (Ordinal classifier and Regressor Fusion for Estimating an Ordinal categorical target), to enhance the performance in ordinal classification problems for which the latent variable is observable. ORFEO is an artificial neural network model incorporating two outputs, one for ordinal classification, using the cumulative link model, and one for regression, using a linear model. Both outputs are simultaneously optimised considering a loss function that linearly combines both classification and regression losses. The main motivation behind developing the proposed approach is to enhance the performance of a standard ordinal classifier. This improvement is facilitated by considering the regression output, which allows the model to differentiate between patterns within the same category. The ORFEO model is applied to two problems in the field of marine and ocean engineering: short-term prediction of both significant wave height and flux of energy. Both problems are addressed considering four different coastal zones of the United States of America, using 13 datasets formed by buoys measurements and reanalysis data. A comprehensive comparison against 20 methodologies, including regression and nominal/ordinal classification approaches is performed, by using diverse nominal and ordinal performance metrics. Ranks achieved indicate that ORFEO outperforms all the compared methodologies in terms of all the performance measures, demonstrating the efficacy and robustness of the proposal. Finally, a statistical analysis is conducted, concluding that there are statistically significant differences across ordinal and nominal performance metrics in favour of the proposed ORFEO model.", awards = "JCR (2023): 7.5, Position: 12/180 (Q1D1), Category: ENGINEERING, MULTIDISCIPLINARY.", comments = "JCR (2023): 7.5, Position: 12/180 (Q1D1), Category: ENGINEERING, MULTIDISCIPLINARY.", doi = "10.1016/j.engappai.2024.108462", issn = "1873-6769", journal = "Engineering Applications of Artificial Intelligence", keywords = "Ordinal classification, Neural networks, Cumulative link models, Short-term prediction, Significant wave height, Flux of energy, Loss functions", month = "July", note = "JCR (2023): 7.5, Position: 12/180 (Q1D1), Category: ENGINEERING, MULTIDISCIPLINARY.", number = "E", pages = "108462", title = "{ORFEO}: {O}rdinal classifier and {R}egressor {F}usion for {E}stimating an {O}rdinal categorical target", url = "www.sciencedirect.com/science/article/pii/S0952197624006201?via%3Dihub", volume = "133", year = "2024", } @article{GEMA-AI, author = "Antonio Manuel G{\'o}mez-Orellana and Manuel Rodr{\'i}guez-Per{\'a}lvarez and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Avik Majumdar and McCaughan, Geoffrey W. and Rhiannon Taylor and Emmanuel Tsochatzis and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", awards = "JCR (2023): 11.6, Position: 9/143 (Q1), Category: GASTROENTEROLOGY {\&} HEPATOLOGY", comments = "JCR (2023): 11.6, Position: 9/143 (Q1), Category: GASTROENTEROLOGY {\&} HEPATOLOGY", issn = "1542-3565", journal = "Clinical Gastroenterology and Hepatology", note = "JCR (2023): 11.6, Position: 9/143 (Q1), Category: GASTROENTEROLOGY {\&} HEPATOLOGY", title = "{G}ender-{E}quity {M}odel for {L}iver {A}llocation using {A}rtificial {I}ntelligence ({GEMA}-{AI}) for waiting list liver transplant prioritization", volume = "Conditionally Accepted", year = "2024", } @article{PaperMutua2024, author = "Rafael Calleja-Lozano and Marcos Rivera-Gavil{\'a}n and David Guijo-Rubio and Hessheimer, Amelia J. and Gloria De la Rosa and Mikel Gastaca and Alejandra Otero and Pablo Ramirez and Andrea Bosc{\`a}-Robledo and Julio Santoyo-Santoyo and Mar{\'i}n-G{\'o}mez, Luis Miguel and Del Villar Moral, Jes{\'u}s and Yiliam Fundora and Laura Llad{\'o} and Carmelo Loinaz and Jim{\'e}nez-Garrido, Manuel C. and Gonzalo Rodr{\'i}guez-La{\'i}z and L{\'o}pez-Baena, Jos{\'e} A. and Ram{\'o}n Charco and Evaristo Varo and Fernando Rotellar and Ayaya Alonso and Rodr{\'i}guez-Sanjuan, Juan C. and Gerardo Blanco and Javier Nu{\~n}o and David Pacheco and Elisabeth Coll and Beatriz Dom{\'i}nguez-Gil and Constantino Fontdevila and Ayll{\'o}n, Mar{\'i}a Dolores and Manuel Dur{\'a}n and Rub{\'e}n Ciria and Pedro Antonio Guti{\'e}rrez and Antonio Manuel G{\'o}mez-Orellana and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Javier Brice{\~n}o", awards = "JCR (2023): 5.5, Position: 14/192 (Q1), Category: SURGERY", comments = "JCR (2023): 5.5, Position: 14/192 (Q1), Category: SURGERY", issn = "0041-1337", journal = "Transplantation", note = "JCR (2023): 5.5, Position: 14/192 (Q1), Category: SURGERY", title = "{M}achine learning algorithms in controlled donation after circulatory death under normothermic regional perfusion: {A} graft survival prediction model", volume = "Accepted", year = "2024", } @article{RAFACyb2024, author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Anthony Bagnall and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the field bridging this gap, yet unexplored in the literature. There are a wide range of time series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this paper presents the first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art. Both convolutional- and deep learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of $29$ ordinal problems has been made. In this way, this paper contributes to the establishment of the state-of-the-art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems. Datasets, code, and results are available in https://www.uco.es/grupos/ayrna/index.php/es/tsoc-dl-conv.", awards = "JCR(2023): 9.4, Position: 16/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2023): 9.4, Position: 16/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ", doi = "10.1109/TCYB.2024.3498100", issn = "2168-2267", journal = "IEEE Transactions on Cybernetics", keywords = "time series machine learning, time series analysis, time series classification, ordinal classification", note = "JCR(2023): 9.4, Position: 16/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", title = "{C}onvolutional and {D}eep {L}earning based techniques for {T}ime {S}eries {O}rdinal {C}lassification", url = "ieeexplore.ieee.org/document/10769513", volume = "Accepted on 12th November", year = "2024", } @article{perez2024autoencoder2, author = "Jorge P{\'e}rez-Aracil and Cosmin M Marina and Eduardo Zorita and David Barriopedro and Pablo Zaninelli and Matteo Giuliani and Andrea Castelletti and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz", awards = "JCR(2023): 4.1, Position: 24/134 (Q1) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR(2023): 4.1, Position: 24/134 (Q1) Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1111/nyas.15243", issn = "0077-8923", journal = "Annals of the New York Academy of Sciences", note = "JCR(2023): 4.1, Position: 24/134 (Q1) Category: MULTIDISCIPLINARY SCIENCES", publisher = "Wiley Online Library", title = "{A}utoencoder-based flow-analogue probabilistic reconstruction of heat waves from pressure fields", url = "dx.doi.org/10.1111/nyas.15243", year = "2024", } @article{middlehurst2024aeon, author = "Matthew Middlehurst and Ali Ismail-Fawaz and Antoine Guillaume and Christopher Holder and David Guijo-Rubio and Guzal Bulatova and Leonidas Tsaprounis and Lukasz Mentel and Martin Walter and Patrick Sch{\"a}fer and Anthony Bagnall", abstract = "aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regression and clustering, as well as a variety of utilities, transformations and distance measures designed for time series data. aeon also has a number of experimental modules for tasks such as anomaly detection, similarity search and segmentation. aeon follows the scikit-learn API as much as possible to help new users and enable easy integration of aeon estimators with useful tools such as model selection and pipelines. It provides a broad library of time series algorithms, including efficient implementations of the very latest advances in research. Using a system of optional dependencies, aeon integrates a wide variety of packages into a single interface while keeping the core framework with minimal dependencies. The package is distributed under the 3-Clause BSD license and is available at this https URL aeon-toolkit/aeon. This version was submitted to the JMLR journal on 02 Nov 2023 for v0.5.0 of aeon. At the time of this preprint aeon has released v0.9.0, and has had substantial changes. ", journal = "Journal of Machine Learning Research", keywords = "Python, Open source, Time series, Machine learning, Data mining, Forecasting, Classification, Extrinsic regression, Clustering", number = "289", title = "{A}eon: a {P}ython toolkit for learning from time series", url = "jmlr.org/papers/v25/23-1444.html", volume = "25", year = "2024", } @article{252024, author = "V{\'i}ctor Manuel Vargas and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio", abstract = "In this study, we present EBANO (Ensemble BAsed on uNimodal Ordinal classifiers), which is a novel ensemble approach of ordinal classifiers that includes four soft labelling approaches along with an ordinal logistic regression model. These models are integrated within the ensemble using a new aggregation methodology that automatically weights each individual classifier using a randomised search algorithm. In addition, the proposed EBANO methodology is applied to tackle short-term prediction of Significant Wave Height (SWH). Thus, we employ EBANO using a diverse set of eight datasets derived from reanalysis data and buoy-recorded SWH measurements. To approach the problem from an ordinal classification perspective, the SWH values are discretised into five ordered classes by applying hierarchical clustering. EBANO is compared with each of the individual classifiers integrated in the proposed ensemble along with a different ensemble technique termed HESCA. Both the average results and the ranks obtained show the superiority of EBANO over the compared methodologies, being more pronounced in the metrics that account for the imbalance present in the datasets considered. Finally, a statistical analysis is performed, confirming the statistical significance of the observed differences in all comparisons. This analysis underscores the effectiveness of EBANO in addressing the problem of SWH prediction, showcasing its excellence.", awards = "JCR (2023): 7.2, Position: 27/197 (Q1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR (2023): 7.2, Position: 27/197 (Q1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.knosys.2024.112223", issn = "1872-7409", journal = "Knowledge-Based Systems", note = "JCR (2023): 7.2, Position: 27/197 (Q1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "112223", title = "{EBANO}: {A} novel {E}nsemble {BA}sed on u{N}imodal {O}rdinal classifiers for the prediction of significant wave height", url = "www.sciencedirect.com/science/article/pii/S0950705124008578?via%3Dihub", volume = "300", year = "2024", } @article{312024, author = "Alejandro Morales-Mart{\'i}n and Francisco-Javier Mesas-Carrascosa and Pedro Antonio Guti{\'e}rrez and Fernando-Juan P{\'e}rez-Porras and V{\'i}ctor Manuel Vargas and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "first_page settings Order Article Reprints Open AccessArticle Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds by Alejandro Morales-Mart{\'i}n 1,* [ORCID] , Francisco-Javier Mesas-Carrascosa 2 [ORCID] , Pedro Antonio Guti{\'e}rrez 1 [ORCID] , Fernando-Juan P{\'e}rez-Porras 2 [ORCID] , V{\'i}ctor Manuel Vargas 1 [ORCID] and C{\'e}sar Herv{\'a}s-Mart{\'i}nez 1 [ORCID] 1 Department of Computer Science and Numerical Analysis, University of C{\'o}rdoba, Campus de Rabanales, 14071 C{\'o}rdoba, Spain 2 Department of Graphic Engineering and Geomatics, University of C{\'o}rdoba, Campus de Rabanales, 14071 C{\'o}rdoba, Spain * Author to whom correspondence should be addressed. Sensors 2024, 24(7), 2168; https://doi.org/10.3390/s24072168 Submission received: 21 February 2024 / Revised: 20 March 2024 / Accepted: 26 March 2024 / Published: 28 March 2024 (This article belongs to the Special Issue Remote Sensing for Spatial Information Extraction and Process) Download keyboard_arrow_down Browse Figures Review Reports Versions Notes Abstract Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing an ordinal classification model in forest areas where LiDAR point clouds can be classified into four distinct ordinal classes: ground, low vegetation, medium vegetation, and high vegetation. To do so, an effective soft labeling technique based on a novel proposed generalized exponential function (CE-GE) is applied to the PointNet network architecture. Statistical analyses based on Kolmogorov–Smirnov and Student’s t-test reveal that the CE-GE method achieves the best results for all the evaluation metrics compared to other methodologies. Regarding the confusion matrices of the best alternative conceived and the standard categorical cross-entropy method, the smoothed ordinal classification obtains a more consistent classification compared to the nominal approach. Thus, the proposed methodology significantly improves the point-by-point classification of PointNet, reducing the errors in distinguishing between the middle classes (low vegetation and medium vegetation).", awards = "JCR(2023): 3.4, Position: 24/76 (Q2) Category: INSTRUMENTS {\&} INSTRUMENTATION", comments = "JCR(2023): 3.4, Position: 24/76 (Q2) Category: INSTRUMENTS {\&} INSTRUMENTATION", doi = "10.3390/s24072168", issn = "1424-8220", journal = "Sensors", keywords = "LiDAR point cloud, Deep Learning, ordinal classification, soft labeling", note = "JCR(2023): 3.4, Position: 24/76 (Q2) Category: INSTRUMENTS {\&} INSTRUMENTATION", number = "7", pages = "1-18", title = "{D}eep {O}rdinal {C}lassification in {F}orest {A}reas {U}sing {L}ight {D}etection and {R}anging {P}oint {C}louds", url = "www.mdpi.com/1424-8220/24/7/2168", volume = "24", year = "2024", } @article{GEMA_ESP_article_2024, author = "Manuel Rodr{\'i}guez-Per{\'a}lvez and de la Rosa, Gloria and Antonio Manuel G{\'o}mez-Orellana and Victoria Aguilera Sancho and Teresa Pascual-Vicente and Sheila Pereira and Mar{\'i}a Luisa Ortiz and Giulia Pagano and Francisco Su{\'a}rez and Rocio Gonz{\'a}lez-Grande and Alba Cachero and Santiago Tom{\'e} and M{\'o}nica Barreales Valbuena and Rosa Martin-Mateos and Sonia Pascual and Mario Romero Crist{\'o}bal and Itxarone Bilbao and Carmen Alonso Martin and Elena Oton and Maria Luisa Gonzalez Dieguez and Mar{\'i}a Dolores Espinosa Aguilar and Ana Arias and Gerar Blanco and Sara Lorente Perez and Antonio Cuadrado and Amaya Red{\'i}n Garc{\'i}a and Clara S{\'a}nchez Cano and Carmen Cepeda Franco and Jose Antonio Pons and Jordi Colmenero and Alejandra Otero Ferreiro and Nerea Hern{\'a}ndez Aretxabaleta and Sarai Romero Moreno and Maria Rodriguez Soler and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Mikel Gastaca", abstract = "The Gender-Equity Model for liver Allocation corrected by serum sodium (GEMA-Na) and the Model for End-stage Liver Disease 3.0 (MELD 3.0) could amend sex disparities for accessing liver transplantation (LT). We aimed to assess these inequities in Spain and to compare the performance of GEMA-Na and MELD 3.0.", awards = "JCR(2023): 9.6, Position: 15/329 (Q1) Category: MEDICINE, GENERAL {\&} INTERNAL", booktitle = "eClinalMedicine", comments = "JCR(2023): 9.6, Position: 15/329 (Q1) Category: MEDICINE, GENERAL {\&} INTERNAL", doi = "10.1016/j.eclinm.2024.102737", issn = "2589-5370", journal = "eClinicalMedicine", month = "Agosto", note = "JCR(2023): 9.6, Position: 15/329 (Q1) Category: MEDICINE, GENERAL {\&} INTERNAL", pages = "102737", title = "{GEMA}-{N}a and {MELD} 3.0 severity scores to address sex disparities for accessing liver transplantation: a nationwide retrospective cohort study", url = "doi.org/10.1016/j.eclinm.2024.102737", volume = "74", year = "2024", } @article{TSERDavid2024, author = "David Guijo-Rubio and Matthew Middlehurst and Guilherme Arcencio and Diego Furtado Silva and Anthony Bagnall", abstract = "Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the comparison to include a wider range of standard regressors and the latest versions of TSER models used in the previous study. We show that none of the previously evaluated regressors can outperform a regression adaptation of a standard classifier, rotation forest. We introduce two new TSER algorithms developed from related work in time series classification. FreshPRINCE is a pipeline estimator consisting of a transform into a wide range of summary features followed by a rotation forest regressor. DrCIF is a tree ensemble that creates features from summary statistics over random intervals. Our study demonstrates that both algorithms, along with InceptionTime, exhibit significantly better performance compared to the other 18 regressors tested. More importantly, DrCIF is the only one that significantly outperforms a standard rotation forest regressor.", awards = "JCR (2023): 2.8, Position: 98/197 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR (2023): 2.8, Position: 98/197 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s10618-024-01027-w", issn = "1384-5810", journal = "Data Mining and Knowledge Discovery", keywords = "Time series, extrinsic regression, unsupervised feature based algorithms, regression", month = "Mayo", note = "JCR (2023): 2.8, Position: 98/197 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "1-45", title = "{U}nsupervised feature based algorithms for time series extrinsic regression", url = "link.springer.com/article/10.1007/s10618-024-01027-w", year = "2024", } @article{MemeticDSCRO_2024, author = "Francisco B{\'e}rchez-Moreno and Antonio Manuel Dur{\'a}n-Rosal and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Pedro Antonio Guti{\'e}rrez and Juan Carlos Fern{\'a}ndez", abstract = "Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance.", awards = "JCR (2023): 3.8, Position: 25/134 (Q1), Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR (2023): 3.8, Position: 25/134 (Q1), Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1038/s41598-024-57654-2", issn = "2045-2322", journal = "Scientific Reports", keywords = "Artificial neural networks, Neuroevolution, Coral reef optimisation algorithm, Local search, Classification, Robust estimators", month = "Marzo", note = "JCR (2023): 3.8, Position: 25/134 (Q1), Category: MULTIDISCIPLINARY SCIENCES", pages = "6961", title = "{A} memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks", url = "www.nature.com/articles/s41598-024-57654-2", volume = "14", year = "2024", } @article{CesarPelaez2024Fuzzy, author = "C{\'e}sar Pel{\'a}ez-Rodr{\'i}guez and Jorge P{\'e}rez-Aracil and Marina, Cosmin M. and Luis Prieto-Godino and Carlos Casanova-Mateo and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz", abstract = "This paper presents a method for providing explainability in the integration of artificial intelligence (AI) and data mining techniques when dealing with meteorological prediction. Explainable artificial intelligence (XAI) refers to the transparency of AI systems in providing explanations for their predictions and decision-making processes, and contribute to improve prediction accuracy and enhance trust in AI systems. The focus of this paper relies on the interpretability challenges in ordinal classification problems within weather forecasting. Ordinal classification involves predicting weather phenomena with ordered classes, such as temperature ranges, wind speed, precipitation levels, and others. To address this challenge, a novel and general explicable forecasting framework, that combines inductive rules and fuzzy logic, is proposed in this work. Inductive rules, derived from historical weather data, provide a logical and interpretable basis for forecasting; while fuzzy logic handles the uncertainty and imprecision in the weather data. The system predicts a set of probabilities that the incoming sample belongs to each considered class. Moreover, it allows the expert decision-making process to be strengthened by relying on the transparency and physical explainability of the model, and not only on the output of a black-box algorithm. The proposed framework is evaluated using two real-world weather databases related to wind speed and low-visibility events due to fog. The results are compared to both ML classifiers and specific methods for ordinal classification problems, achieving very competitive results in terms of ordinal performance metrics while offering a higher level of explainability and transparency compared to existing approaches.", awards = "JCR(2023): 7.2 Position: 27/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2023): 7.2 Position: 27/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.knosys.2024.111556", issn = "1872-7409", journal = "Knowledge-Based Systems", keywords = "Ordinal classification, Explainable artificial intelligence, Inductive rules, Fuzzy logic, Meteorological forecasting", month = "Mayo", note = "JCR(2023): 7.2 Position: 27/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "111556", title = "{A} general explicable forecasting framework for weather events based on ordinal classification and inductive rules combined with fuzzy logic", url = "doi.org/10.1016/j.knosys.2024.111556", volume = "291", year = "2024", } @article{ordinal_dropout_2024, author = "Francisco B{\'e}rchez-Moreno and Juan Carlos Fern{\'a}ndez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Pedro Antonio Guti{\'e}rrez", abstract = "Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to avoid overfitting and improve generalisation, but taking into account the extra information of the ordinal task, which is exploited to improve performance. The correlation between the outputs of every neuron and the target labels is used to guide the dropout process: the higher the neuron is correlated with the expected labels, the lower its probability of being dropped. Given that randomness also plays a crucial role in the regularisation process, a balancing factor () is also added to the training process to determine the influence of the ordinality with respect to a constant probability, providing a hybrid ordinal regularisation method. An extensive battery of experiments shows that the new hybrid ordinal dropout methodology perform better than standard dropout, obtaining improved results in most evaluation metrics, including not only ordinal metrics but also nominal ones.", awards = "JCR 2023: 14.7, Position: 2/143 (Q1), Category: COMPUTER SCIENCE, THEORY {\&} METHODS", comments = "JCR 2023: 14.7, Position: 2/143 (Q1), Category: COMPUTER SCIENCE, THEORY {\&} METHODS", doi = "10.1016/j.inffus.2024.102299", journal = "Information Fusion", keywords = "Deep Learning, Dropout, Ordinal Classification, Ordinal Regression, Convolutional Neural Networks", month = "Febrero", note = "JCR 2023: 14.7, Position: 2/143 (Q1), Category: COMPUTER SCIENCE, THEORY {\&} METHODS", title = "{F}usion of standard and ordinal dropout techniques to regularise deep models", url = "www.sciencedirect.com/science/article/pii/S1566253524000770", volume = "106", year = "2024", } @article{research_assistants_2024, author = "Ariel Guersenzvaig and Javier S{\'a}nchez-Monedero", abstract = "Since their mass introduction in late 2022, AI chatbots like ChatGPT have garnered considerable attention due to the promise of widespread applications. Their purported advanced writing capacity has made it difficult for experts to differentiate between machine-generated and human-generated paper abstracts, as reported in Nature (Else 2023). However, many scholars emphasize that these systems should be seen as ‘stochastic parrots’ due to their lack of true understanding (Bender et al. 2021). Furthermore, these systems have been prone to produce ‘hallucinations’ (i.e., falsehoods), among other highlighted issues. This is not the venue for an exhaustive critique; our purpose is to comment on a rather specific topic: the use of chatbots for the automation of research and bibliographical review that tends to precede all academic research. As an example, consider Elicit, a tool that aims to optimize the flow of academic research. According to its developing company, ‘If you ask a question, Elicit will show relevant papers and summaries of key information about those papers in an easy-to-use table' (https:elicit.org, faq. xxxx). It apparently does this by finding the most important information from the eight most 'relevant' articles among a selection of 400 articles that are related to the question. Alternatively, think of Perplexity Copilot (https:blog.perplexity.ai, faq, what-is-copilot. xxxx), which offers a ‘tailored list of sources and even summarized papers’ to students and academics. We often teach our students that through bibliographical research, we find out what has been said about a topic, what other related views or theories exist, what gaps are still to be filled, and so on. Importantly, we emphasize that it serves to establish the foundations of our own research. But is the review just a mere instrument that we could optimize using tools like Elicit and Perplexity Copilot? To answer this question, we must first take a detour to address a more general issue related to the way science can be carried out.", awards = "JCR 2023: 2.9, Position: 95/197 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR 2023: 2.9, Position: 95/197 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s00146-023-01861-4", issn = "1435-5655", journal = "AI {\&} SOCIETY", month = "Febrero", note = "JCR 2023: 2.9, Position: 95/197 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", title = "{AI} research assistants, intrinsic values, and the science we want", url = "link.springer.com/article/10.1007/s00146-023-01861-4", year = "2024", } @article{Ordinal_Hierarchical_DL_2024, author = "Riccardo Rosati and Luca Romeo and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and Emanuele Frontoni and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Real-world classification problems may disclose different hierarchical levels where the categories are displayed in an ordinal structure. However, no specific deep learning (DL) models simultaneously learn hierarchical and ordinal constraints while improving generalization performance. To fill this gap, we propose the introduction of two novel ordinal–hierarchical DL methodologies, namely, the hierarchical cumulative link model (HCLM) and hierarchical–ordinal binary decomposition (HOBD), which are able to model the ordinal structure within different hierarchical levels of the labels. In particular, we decompose the hierarchical–ordinal problem into local and global graph paths that may encode an ordinal constraint for each hierarchical level. Thus, we frame this problem as simultaneously minimizing global and local losses. Furthermore, the ordinal constraints are set by two approaches ordinal binary decomposition (OBD) and cumulative link model (CLM) within each global and local function. The effectiveness of the proposed approach is measured on four real-use case datasets concerning industrial, biomedical, computer vision, and financial domains. The extracted results demonstrate a statistically significant improvement to state-of-the-art nominal, ordinal, and hierarchical approaches.", awards = "JCR(2023): 10.2, Position: 7/143 (Q1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", comments = "JCR(2023): 10.2, Position: 7/143 (Q1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", doi = "10.1109/TNNLS.2024.3360641", issn = "2162-2388", journal = "IEEE Transactions on Neural Networks and Learning Systems", keywords = "Cumulative link model, deep learning, hierarchical learning, ordinal binary decomposition, ordinal regression", month = "Febrero", note = "JCR(2023): 10.2, Position: 7/143 (Q1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", pages = "1--14", title = "{L}earning {O}rdinal–{H}ierarchical {C}onstraints for {D}eep {L}earning {C}lassifie", url = "ieeexplore.ieee.org/document/10432994", year = "2024", } @article{duranboosting2023, author = "Carlos Perales-Gonz{\'a}lez and Javier P{\'e}rez-Rodr{\'i}guez and Antonio Manuel Dur{\'a}n-Rosal", abstract = "This paper explores the boosting ridge (BR) framework in the extreme learning machine (ELM) community and presents a novel model that trains the base learners as a global ensemble. In the context of Extreme Learning Machine single-hidden-layer networks, the nodes in the hidden layer are preconfigured before training, and the optimisation is performed on the weights in the output layer. The previous implementation of the BR ensemble with ELM (BRELM) as base learners fix the nodes in the hidden layer for all the ELMs. The ensemble learning method generates different output layer coefficients by reducing the residual error of the ensemble sequentially as more base learners are added to the ensemble. As in other ensemble methodologies, base learners are selected until fulfilling ensemble criteria such as size or performance. This paper proposes a global learning method in the BR framework, where base learners are not added step by step, but all are calculated in a single step looking for ensemble performance. This method considers (i) the configurations of the hidden layer are different for each base learner, (ii) the base learners are optimised all at once, not sequentially, thus avoiding saturation, and (iii) the ensemble methodology does not have the disadvantage of working with strong classifiers. Various regression and classification benchmark datasets have been selected to compare this method with the original BRELM implementation and other state-of-the-art algorithms. Particularly, 71 datasets for classification and 52 for regression, have been considered using different metrics and analysing different characteristics of the datasets, such as the size, the number of classes or the imbalanced nature of them. Statistical tests indicate the superiority of the proposed method in both regression and classification problems in all experimental scenarios.", awards = "JCR(2023): 3.8 Position: 25/134 (Q1) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR(2023): 3.8 Position: 25/134 (Q1) Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1038/s41598-023-38948-3", issn = "2045-2322", journal = "Scientific Reports", keywords = "boosting, regression, classification", month = "July", note = "JCR(2023): 3.8 Position: 25/134 (Q1) Category: MULTIDISCIPLINARY SCIENCES", pages = "11809", title = "{B}oosting ridge for the extreme learning machine globally optimised for classification and regression problems", url = "www.nature.com/articles/s41598-023-38948-3", volume = "13", year = "2023", } @article{VargasASOC2023, author = "V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and Riccardo Rosati and Luca Romeo and Emanuele Frontoni and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Ordinal problems are those where the label to be predicted from the input data is selected from a group of categories which are naturally ordered. The underlying order is determined by the implicit characteristics of the real problem. They share some characteristics with nominal or standard classification problems but also with regression ones. In the real world, there are many problems of this type in different knowledge areas, such as medical diagnosis, risk prediction or quality control. The latter has gained an increasing interest in the Industry 4.0 scenario. Some weapons manufacturer follow an aesthetic quality control process to determine the quality of the wood used to produce the stock of the weapons they manufacture. This process is an ordinal classification problem that can be automatised using machine learning techniques. Deep learning methods have been widely used for multiples types of tasks including image aesthetic quality control, where convolutional neural networks are the most common alternative, given that they are focused on solving problems where the input data are images. In this work, we propose a new exponential regularised loss function that is usedto improve the classification performance for ordinal problems when using deep neural networks. The proposed methodology is applied to a real-world aesthetic quality control problem. The results and statistical analysis prove that the proposed methodology outperforms other state-of-the-art methods, obtaining very robust results.", awards = "JCR(2023): 7.2, Position: 15/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", comments = "JCR(2023): 7.2, Position: 15/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", doi = "10.1016/j.asoc.2023.110191", issn = "1568-4946", journal = "Applied Soft Computing", keywords = "Ordinal classification, Convolutional neural networks, Loss function, Cumulative link models, Aesthetic quality control", month = "May", note = "JCR(2023): 7.2, Position: 15/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", number = "110191", title = "{E}xponential loss regularisation for encouraging ordinal constraint to shotgun stocks quality assessment", url = "www.sciencedirect.com/science/article/pii/S1568494623002090", volume = "138", year = "2023", } @article{INFFusVictor2023, author = "V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and Javier Barbero-G{\'o}mez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Recently, solving ordinal classification problems using machine learning and deep learning techniques has acquired important attention. There are many real-world problems in different areas of knowledge where a categorical variable needs to be predicted, and the existing categories follow an order associated with the nature of the problem: e.g. medical diagnosis with different states of a disease, or industrial quality assessment with different levels of quality. In these problems, it is quite common that the final label for each sample is determined by a group of experts with different opinions, and all opinions are usually summarised in a single crisp label by means of a given statistic (e.g. the median or the mode). Applying standard ordinal classifiers to these crisp labels could result in overfitting, as the labelling information is considered as totally certain. In this work, we propose a unimodal regularisation approach based on soft labelling, i.e. the ordinal information is used to introduce the inherent uncertainty of the label fusion. Specifically, said regularisation is based on using triangular distributions to simulate the aforementioned fusion of the expert opinions, where a parameter is used to decide the amount of probability that is assigned to the target category and the adjacent ones (according to the ordinal scale). The strategy could be applied to the loss function used by any ordinal classification learning algorithm, but we focus on deep learning in this paper. The proposal is compared to a baseline approach for nominal classification tasks and other state-of-the-art unimodal regularisation methods, and the experimental validation includes six benchmark datasets and five performance metrics. The results along with the statistical analysis show that the proposed methodology significantly outperforms the rest of the methods.", awards = "JCR(2023): 14.7 Position: 2/143 (Q1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", comments = "JCR(2023): 14.7 Position: 2/143 (Q1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", doi = "10.1016/j.inffus.2023.01.003", issn = "1566-2535", journal = "Information Fusion", keywords = "unimodal regularisation, deep learning, triangular distribution, ordinal loss", month = "May", note = "JCR(2023): 14.7 Position: 2/143 (Q1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", pages = "258-267", title = "{S}oft labelling based on triangular distributions for ordinal classification", url = "dx.doi.org/10.1016/j.inffus.2023.01.003", volume = "93", year = "2023", } @article{BarberoECOCDeepNEPL, author = "Javier Barbero-G{\'o}mez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Automatic classification tasks on structured data have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. Focus should be shifted from classic classification metrics towards per-class metrics (like AUC or Sensitivity) and rank agreement metrics (like Cohen's Kappa or Spearman's rank correlation coefficient). We present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC). We aim to show experimentally, using four different CNN architectures and two ordinal classification datasets, that the OBD+ECOC methodology significantly improves the mean results on the relevant ordinal and class-balancing metrics. The proposed method is able to outperform a nominal approach as well as already existing ordinal approaches, achieving a mean performance of RMSE = 1.0797 for the Retinopathy dataset and RMSE = 1.1237 for the Adience dataset averaged over 4 different architectures.", awards = "JCR(2023): 2.6, Position: 104/197 (Q3) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2023): 2.6, Position: 104/197 (Q3) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s11063-022-10824-7", issn = "1573-773X", journal = "Neural Processing Letters", keywords = "Ordinal classification, Convolutional Neural Networks, Ordinal Binary Decomposition", month = "May", note = "JCR(2023): 2.6, Position: 104/197 (Q3) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "5299--5330", title = "{E}rror-{C}orrecting {O}utput {C}odes in the {F}ramework of {D}eep {O}rdinal {C}lassification", url = "doi.org/10.1007/s11063-022-10824-7", volume = "55", year = "2023", } @article{Cluster-analysis-and-forecasting, author = "Miguel D{\'i}az-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The sanitary emergency caused by COVID-19 has compromised countries and generated a worldwide health and economic crisis. To provide support to the countries’ responses, numerous lines of research have been developed. The spotlight was put on effectively and rapidly diagnosing and predicting the evolution of the pandemic, one of the most challenging problems of the past months. This work contributes to the existing literature by developing a two-step methodology to analyze the transmission rate, designing models applied to territories with similar pandemic behavior characteristics. Virus transmission is considered as bacterial growth curves to understand the spread of the virus and to make predictions about its future evolution. Hence, an analytical clustering procedure is first applied to create groups of locations where the virus transmission rate behaved similarly in the different outbreaks. A curve decomposition process based on an iterative polynomial process is then applied, obtaining meaningful forecasting features. Information of the territories belonging to the same cluster is merged to build models capable of simultaneously predicting the 14-day incidence in several locations using Evolutionary Artificial Neural Networks. The methodology is applied to Andalusia (Spain), although it is applicable to any region across the world. Individual models trained for a specific territory are carried out for comparison purposes. The results demonstrate that this methodology achieves statistically similar, or even better, performance for most of the locations. In addition to being extremely competitive, the main advantage of the proposal lies in its complexity cost reduction. The total number of parameters to be estimated is reduced up to 93.51% for the short term and 93.31% for the mid-term forecasting, respectively. Moreover, the number of required models is reduced by 73.53% and 58.82% for the short- and mid-term forecasting horizons.", awards = "JCR(2023): 7.5, Position: 25/352 (Q1) Category: ENGINEERING, ELECTRICAL {\&} ELECTRONIC", comments = "JCR(2023): 7.5, Position: 25/352 (Q1) Category: ENGINEERING, ELECTRICAL {\&} ELECTRONIC", doi = "doi.org/10.1016/j.eswa.2023.120103", issn = "0957-4174", journal = "Expert Systems with Applications", keywords = "COVID-19 incidence estimation, Clustering, Forecasting, Neural networks, Evolutionary algorithms", month = "April", note = "JCR(2023): 7.5, Position: 25/352 (Q1) Category: ENGINEERING, ELECTRICAL {\&} ELECTRONIC", pages = "120103", title = "{C}luster analysis and forecasting of viruses incidence growth curves: {A}pplication to {SARS}-{C}o{V}-2", url = "doi.org/10.1016/j.eswa.2023.120103", year = "2023", } @article{GEMA_Liver_transplant_cohort_study_2022, author = "Manuel Luis Rodr{\'i}guez-Per{\'a}lvarez and Antonio Manuel G{\'o}mez-Orellana and Avik Majumdar and Michael Bailey and Geoffrey W McCaughan and Paul Gow and Marta Guerrero and Rhiannon Taylor and David Guijo-Rubio and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Emmanuel A Tsochatzis", abstract = "The Model for End-stage Liver Disease (MELD) and its sodium-corrected variant (MELD-Na) have created gender disparities in accessing liver transplantation. We aimed to derive and validate the Gender-Equity Model for liver Allocation (GEMA) and its sodium-corrected variant (GEMA-Na) to amend such inequities. In this cohort study, the GEMA models were derived by replacing creatinine with the Royal Free Hospital glomerular filtration rate (RFH-GFR) within the MELD and MELD-Na formulas, with re-fitting and re-weighting of each component. The new models were trained and internally validated in adults listed for liver transplantation in the UK (2010–20; UK Transplant Registry) using generalised additive multivariable Cox regression, and externally validated in an Australian cohort (1998–2020; Royal Prince Alfred Hospital [Australian National Liver Transplant Unit] and Austin Hospital [Victorian Liver Transplant Unit]). The study comprised 9320 patients: 5762 patients for model training, 1920 patients for internal validation, and 1638 patients for external validation. The primary outcome was mortality or delisting due to clinical deterioration within the first 90 days from listing. Discrimination was assessedby Harrell’s concordance statistic 449 (5·8%) of 7682 patients in the UK cohort and 87 (5·3%) of 1638 patients in the Australian cohort died or were delisted because of clinical deterioration within 90 days. GEMA showed improved discrimination in predicting mortality or delisting due to clinical deterioration within the first 90 days after waiting list inclusion compared with MELD (Harrell’s concordance statistic 0·752 [95% CI 0·700–0·804] vs 0·712 [0·656–0·769]; p=0·001 in the internal validation group and 0·761 [0·703–0·819] vs 0·739 [0·682–0·796]; p=0·036 in the external validation group), and GEMA-Na showed improved discrimination compared with MELD-Na (0·766 [0·715–0·818] vs 0·742 [0·686–0·797]; p=0·0058 in the internal validation group and 0·774 [0·720–0·827] vs 0·745 [0·690–0·800]; p=0·014 in the external validation group). The discrimination capacity of GEMA-Na was higher in women than in the overall population, both in the internal (0·802 [0·716–0·888]) and external validation cohorts (0·796 [0·698–0·895]). In the pooled validation cohorts, GEMA resulted in a score change of at least 2 points compared with MELD in 1878 (52·8%) of 3558 patients (25·0% upgraded and 27·8% downgraded). GEMA-Na resulted in a score change of at least 2 points compared with MELD-Na in 1836 (51·6%) of 3558 patients (32·3% upgraded and 19·3% downgraded). In the whole cohort, 3725 patients received a transplant within 90 days of being listed. Of these patients, 586 (15·7%) would have been differently prioritised by GEMA compared with MELD; 468 (12·6%) patients would have been differently prioritised by GEMA-Na compared with MELD-Na. One in 15 deaths could potentially be avoided by using GEMA instead of MELD and one in 21 deaths could potentially be avoided by using GEMA-Na instead of MELD-Na. GEMA and GEMA-Na showed improved discrimination and a significant re-classification benefit compared with existing scores, with consistent results in an external validation cohort. Their implementation could save a clinically meaningful number of lives, particularly among women, and could amend current gender inequities in accessing liver transplantation.", awards = "JCR(2023): 30.9, Position: 2/143 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", comments = "JCR(2023): 30.9, Position: 2/143 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", doi = "10.1016/S2468-1253(22)00354-5", issn = "2468-1253", journal = "The Lancet Gastroenterology {\&} Hepatology", month = "March", note = "JCR(2023): 30.9, Position: 2/143 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", number = "3", pages = "242--252", title = "{D}evelopment and validation of the {G}ender-{E}quity {M}odel for {L}iver {A}llocation ({GEMA}) to prioritise candidates for liver transplantation: a cohort study", url = "doi.org/10.1016/S2468-1253(22)00354-5", volume = "8", year = "2023", } @article{DLHierarchicalClassifier, author = "V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and Riccardo Rosati and Luca Romeo and Emanuele Frontoni and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "In the last years, multiple quality control tasks consist in classifying some items based on their aesthetic characteristics (aesthetic quality control, AQC), where usually the aspect of the material is not measurable and is based on expert observation. Given the increasing amount of images in this domain, deep learning (DL) models can be used to extract and classify the most discriminative patterns. Frequently, when trying to evaluate the quality of a manufactured product, the categories are naturally ordered, resulting in an ordinal classification problem. However, the ordinal categories assigned by an expert can be arranged in different levels that somehow model a hierarchy of the AQC task. In this work, we propose a DL approach to improve the classification performance in problems where the categories are naturally ordered and follow a hierarchical structure. The proposed approach is evaluated on a real-world dataset that defines an AQC task and compared with other state-of-the-art DL methods. The experimental results show that our hierarchical approach outperforms the state-of-the-art ones.", awards = "JCR(2023): 8.2, Position: 11/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", comments = "JCR(2023): 8.2, Position: 11/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", doi = "10.1016/j.compind.2022.103786", issn = "0166-3615", journal = "Computers in Industry", keywords = "Hierarchical classification, Ordinal classification, Deep learning, Aesthetic quality control, Convolutional neural networks", month = "January", note = "JCR(2023): 8.2, Position: 11/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", pages = "1-13", title = "{D}eep learning based hierarchical classifier for weapon stock aesthetic quality control assessment", url = "doi.org/10.1016/j.compind.2022.103786", volume = "144", year = "2023", } @article{SigmaEAAI2023, author = "V{\'i}ctor Manuel Vargas and Riccardo Rosati and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Adriano Mancini and Luca Romeo and Pedro Antonio Guti{\'e}rrez", abstract = "Predictive Maintenance (PdM) methods aim to facilitate the scheduling of maintenance work before equipment failure. In this context, detecting early faults in automated teller machines (ATMs) has become increasingly important since these machines are susceptible to various types of unpredictable failures. ATMs track execution status by generating massive event-log data that collect system messages unrelated to the failure event. Predicting machine failure based on event logs poses additional challenges, mainly in extracting features that might represent sequences of events indicating impending failures. Accordingly, feature learning approaches are currently being used in PdM, where informative features are learned automatically from minimally processed sensor data. However, a gap remains to be seen on how these approaches can be exploited for deriving relevant features from event-log-based data. To fill this gap, we present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from the original event-log data and a linear classifier to classify the sample based on the learned features. The proposed methodology is applied to a significant real-world collected dataset. Experimental results demonstrated how one of the proposed convolutional kernels (i.e. HYDRA) exhibited the best classification performance (accuracy of $0.759$ and AUC of $0.693$). In addition, statistical analysis revealed that the HYDRA and MiniROCKET models significantly overcome one of the established state-of-the-art approaches in time series classification (InceptionTime), and three non-temporal ML methods from the literature. The predictive model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs.", awards = "JCR(2023): 7.5, Position: 5/179 (Q1D1) Category: ENGINEERING, MULTIDISCIPLINARY.", comments = "JCR(2023): 7.5, Position: 5/179 (Q1D1) Category: ENGINEERING, MULTIDISCIPLINARY.", issn = "0952-1976", journal = "Engineering Applications of Artificial Intelligence", keywords = "Predictive Maintenance, Fault prediction, Time series classification, Feature learning, Convolutional kernels models", note = "JCR(2023): 7.5, Position: 5/179 (Q1D1) Category: ENGINEERING, MULTIDISCIPLINARY.", number = "C", pages = "1-12", title = "{A} hybrid feature learning approach based on convolutional kernels for {ATM} fault prediction using event-log data", url = "doi.org/10.1016/j.engappai.2023.106463", volume = "123", year = "2023", } @article{multi-class-classification-model-2023, author = "Antonio Manuel Dur{\'a}n-Rosal and Aggeo Dur{\'a}n-Fern{\'a}ndez and Francisco Fern{\'a}ndez-Navarro and Mariano Carbonero-Ruz", abstract = "Randomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1-of- encoding ( the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks. The empirical results obtained by the proposed models were compared to those reported by state-of-the-art models in the correct classification rate and a separability index (which measures the degree of separability in projection terms per class of the patterns belonging to the class of the others). The proposed methods show very competitive performance in the separability index and prediction accuracy compared to the neural networks version of the comparison methods (with and without direct links). Remarkably, the model provides significantly superior performance in deep models with direct links compared to its deep model counterpart.", awards = "JCR(2023): 7.2, Position: 27/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2023): 7.2, Position: 27/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.asoc.2022.109914", issn = "1568-4946", journal = "Applied Soft Computing", keywords = "Randomized-based, Feedforward Neural Networks, Random Vector Functional Link Neural Networks, Extreme Learning Machine, Generalized eigenvalue problem, Classification, Kernel methods", note = "JCR(2023): 7.2, Position: 27/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "109914", title = "{A} multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks", url = "doi.org/10.1016/j.asoc.2022.109914", volume = "133", year = "2023", } @article{XAITemperaturasAt2023, author = "Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Jorge P{\'e}rez-Aracil and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "In this paper we have tackled the problem of long-term air temperature prediction with eXplainable Artificial Intelligence (XAI) models. Specifically, we have evaluated the performance of an Artificial Neural Network (ANN) architecture with sigmoidal neurons in the hidden layer, trained by means of an evolutionary algorithm (Evolutionary ANNs, EANNs). This XAI model architecture (XAI-EANN) has been applied to the long-term air temperature prediction at different sub-regions of the South of the Iberian Peninsula. In this case, the average August air temperature has been predicted from ERA5 Reanalysis data variables, obtaining good predictions skills and explainable models in terms of the input climatological variables considered. A cluster analysis has been first carried out in terms of the average air temperature in the zone, in such a way that a number of sub-regions with different air temperature behaviour have been defined. The proposed XAI-EANN model architecture has been applied to each of the defined sub-regions, in order to find significant differences among them, which can be explained with the XAI-EANN models obtained. Finally, a comprehensive comparison against some state-of-the-art techniques has also been carried out, concluding that there are statistically significant differences in terms of accuracy in favour of the proposed XAI-EANN model, which also benefits from being an XAI model.", awards = "JCR(2023): 4.5, position: 22/110 (Q1), category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES.", comments = "JCR(2023): 4.5, position: 22/110 (Q1), category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES.", doi = "10.1016/j.atmosres.2023.106608", issn = "0169-8095", journal = "Atmospheric Research", keywords = "Air temperature, Long-term air temperature prediction, Climatology, XAI, Neural networks", note = "JCR(2023): 4.5, position: 22/110 (Q1), category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES.", number = "106608", title = "{O}ne month in advance prediction of air temperature from {R}eanalysis data with {E}xplainable {A}rtificial {I}ntelligence techniques", url = "doi.org/10.1016/j.atmosres.2023.106608", volume = "284", year = "2023", } @article{252023, author = "V{\'i}ctor Manuel Vargas and Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Deep learning techniques for ordinal classification have recently gained significant attention. Predicting an ordinal variable, that is, a variable that demonstrates a natural relationship between categories, is of relevance for a number of real-world problems in various fields of knowledge. For example, a medical diagnosis can occur at different stages of the disease. Applying standard classifiers to ordered labels can lead to errors in distant categories, when errors in an ordinal problem ideally tend to be produced in adjacent classes because of their similarity. To address this issue, we propose a soft labelling approach based on generalised triangular distributions, which are asymmetric and different for each class. The parameters of these distributions are determined using a metaheuristic and are specifically adapted to the given problem. Moreover, this approach enables the model to avoid errors in distant classes (e.g. classifying a patient with a severe disease as healthy). A comprehensive comparison was performed using eight datasets and five performance metrics. The main advantage of the proposed soft-labelling approach is that it adapts the distributions to each problem, resulting in greater flexibility and better performance. The results and statistical analysis show that the proposed methodology significantly outperforms all other methods.", awards = "JCR(2022): 8.1, Position: 13/158 (Q1D1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", comments = "JCR(2022): 8.1, Position: 13/158 (Q1D1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", doi = "10.1016/j.ins.2023.119606", issn = "0020-0255", journal = "Information Sciences", month = "Noviembre", note = "JCR(2022): 8.1, Position: 13/158 (Q1D1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", pages = "119606", title = "{G}eneralised {T}riangular {D}istributions for ordinal deep learning: novel proposal and optimisation", url = "www.sciencedirect.com/science/article/pii/S002002552301191X", volume = "648", year = "2023", } @article{guijo2023evolutionary, author = "David Guijo-Rubio and Antonio Manuel Dur{\'a}n-Rosal and Antonio Manuel G{\'o}mez-Orellana and Juan Carlos Fern{\'a}ndez", abstract = "This paper proposes a novel methodology for recovering missing time series data, a crucial task for subsequent Machine Learning (ML) analyses. The methodology is specifically applied to Significant Wave Height (SWH) time series in the field of marine engineering. The proposed approach involves two phases. Firstly, the SWH time series for each buoy is independently reconstructed using three transfer function models: regression-based, correlation-based, and distance-based. The distance-based transfer function exhibits the best overall performance. Secondly, Evolutionary Artificial Neural Networks (EANNs) are utilised for the final recovery of each time series, using as inputs highly correlated buoys that have been intermediately recovered. The EANNs are evolved considering two metrics, the novel squared error relevance area, which balances the importance of extreme and around-mean values, and the well-known mean squared error. The study considers SWH time series data from 15 buoys in two coastal zones in the United States. The results demonstrate that the distance-based transfer function is generally the best transfer function, and that EANNs outperform a range of state-of-the-art ML techniques in 12 out of the 15 buoys, with a number of connections comparable to linear models. Furthermore, the proposed methodology outperforms the two most popular approaches for time series reconstruction, BRITS and SAITS, for all buoys except one. Therefore, the proposed methodology provides a promising approach, which may be applied to time series from other fields, such as wind or solar energy farms in the field of green energy.", awards = "JCR(2023): 7.2, Position: 15/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", comments = "JCR(2023): 7.2, Position: 15/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", doi = "10.1016/j.asoc.2023.110647", issn = "1568-4946", journal = "Applied Soft Computing", month = "Septiembre", note = "JCR(2023): 7.2, Position: 15/169 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", pages = "110647", title = "{A}n {E}volutionary {A}rtificial {N}eural {N}etwork approach for spatio-temporal wave height time series reconstruction", url = "doi.org/10.1016/j.asoc.2023.110647", volume = "146", year = "2023", } @article{TNNLS2021Victor, author = "V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and Javier Barbero-G{\'o}mez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Activation functions lie at the core of every neural network model, from shallow to deep convolutional neural networks. Their properties and characteristics shape the output range of each layer and, thus, their capabilities. Modern approaches rely mostly on a single function choice for the whole network, usually ReLU or other similar alternatives. In this work, we propose two new activation functions, analyse their properties and compare them with 17 different function proposals from recent literature on six distinct problems with different characteristics. The objective is to shed some light about their comparative performance. The results show that the proposed functions achieved better performance than the most commonly used ones.", awards = "JCR(2023): 10.2 Position: 7/143 (Q1D1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", comments = "JCR(2023): 10.2 Position: 7/143 (Q1D1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", doi = "10.1109/TNNLS.2021.3105444", issn = "2162-237X", journal = "IEEE Transactions on Neural Networks and Learning Systems", keywords = "activation functions, convolutional networks, ELU", month = "Marzo", note = "JCR(2023): 10.2 Position: 7/143 (Q1D1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS", number = "3", pages = "1478--1488", title = "{A}ctivation functions for convolutional neural networks: proposals and experimental study", url = "doi.org/10.1109/TNNLS.2021.3105444", volume = "34", year = "2023", } @article{Crossroads_in_Liver_Transplantation_2022, author = "R. Calleja and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and J. Brice{\~n}o", abstract = "Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor–recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered “unbalanced.” In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D–R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.", awards = "JCR(2022): 2.6, Position: 87/167 (Q3) Category: MEDICINE, GENERAL {\&} INTERNAL", comments = "JCR(2022): 2.6, Position: 87/167 (Q3) Category: MEDICINE, GENERAL {\&} INTERNAL", doi = "10.3390/medicina58121743", issn = "1648-9144", journal = "Medicina", keywords = "Donor recipient matching, Artificial intelligence, Deep learning, Artificial Neural Networks, Random forest, Liver transplantation outcomes", month = "November", note = "JCR(2022): 2.6, Position: 87/167 (Q3) Category: MEDICINE, GENERAL {\&} INTERNAL", number = "12", pages = "1743", title = "{C}rossroads in {L}iver {T}ransplantation: {I}s {A}rtificial {I}ntelligence the {K}ey to {D}onor–{R}ecipient {M}atchin", url = "doi.org/10.3390/medicina58121743", volume = "58", year = "2022", } @article{durandistribution2022, author = "Antonio Manuel Dur{\'a}n-Rosal and Mariano Carbonero-Ruz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Modelling extreme values distributions, such as wave height time series where the higher waves are much less frequent than the lower ones, has been tackled from the point of view of the Peak-Over-Threshold (POT) methodologies, where modelling is based on those values higher than a threshold. This threshold is usually predefined by the user, while the rest of values are ignored. In this paper, we propose a new method to estimate the distribution of the complete time series, including both extreme and regular values. This methodology assumes that extreme values time series can be modelled by a normal distribution in a combination of a uniform one. The resulting theoretical distribution is then used to fix the threshold for the POT methodology. The methodology is tested in nine real-world time series collected in the Gulf of Alaska, Puerto Rico and Gibraltar (Spain), which are provided by the National Data Buoy Center (USA) and Puertos del Estado (Spain). By using the Kolmogorov-Smirnov statistical test, the results confirm that the time series can be modelled with this type of mixed distribution. Based on this, the return values and the confidence intervals for wave height in different periods of time are also calculated.", awards = "JCR(2022): 4.6 Position: 22/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR(2022): 4.6 Position: 22/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1038/s41598-022-22243-8", issn = "2045-2322", journal = "Scientific Reports", keywords = "mixed distribution, pot, wave height time series", month = "October", note = "JCR(2022): 4.6 Position: 22/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", pages = "17327", title = "{A} mixed distribution to fix the threshold for {P}eak-{O}ver-{T}hreshold wave height estimation", url = "www.nature.com/articles/s41598-022-22243-8", volume = "12", year = "2022", } @article{AILiverTransplantation2022, author = "Javier Brice{\~n}o and Rafael Calleja and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Decision-making based on artificial intelligence (AI) methodology is increasingly present in all areas of modern medicine. In recent years, models based on deep-learning have begun to be used in organ transplantation. Taking into account the huge number of factors and variables involved in donor-recipient (D-R) matching, AI models may be well suited to improve organ allocation. AI-based models should provide two solutions: complement decision-making with current metrics based on logistic regression and improve their predictability. Hundreds of classifiers could be used to address this problem. However, not all of them are really useful for D-R pairing. Basically, in the decision to assign a given donor to a candidate in waiting list, a multitude of variables are handled, including donor, recipient, logistic and perioperative variables. Of these last two, some of them can be inferred indirectly from the team's previous experience. Two groups of AI models have been used in the D-R matching: artificial neural networks (ANN) and random forest (RF). The former mimics the functional architecture of neurons, with input layers and output layers. The algorithms can be uni- or multi-objective. In general, ANNs can be used with large databases, where their generalizability is improved. However, they are models that are very sensitive to the quality of the databases and, in essence, they are black-box models in which all variables are important. Unfortunately, these models do not allow to know safely the weight of each variable. On the other hand, RF builds decision trees and works well with small cohorts. In addition, they can select top variables as with logistic regression. However, they are not useful with large databases, due to the extreme number of decision trees that they would generate, making them impractical. Both ANN and RF allow a successful donor allocation in over 80% of D-R pairing, a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease, balance of risk score, and survival outcomes following liver transplantation scores. Many barriers need to be overcome before these deep-learning-based models can be included for D-R matching. The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.", awards = "JCR (2022): 3.3 Position: 52/93 (Q3) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", comments = "JCR (2022): 3.3 Position: 52/93 (Q3) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", doi = "10.1016/j.hbpd.2022.03.001", issn = "1499-3872", journal = "Hepatobiliary {\&} Pancreatic Diseases International", keywords = "Donor-recipient matching, Artificial intelligence, Deep Learning, Artificial neural networks, Random forest, Liver transplantation outcome", month = "August", note = "JCR (2022): 3.3 Position: 52/93 (Q3) Category: GASTROENTEROLOGY {\&} HEPATOLOGY", number = "4", pages = "347-353", title = "{A}rtificial intelligence and liver transplantation: {L}ooking for the bestdonor-recipient pairing", url = "doi: 10.1016/j.hbpd.2022.03.001", volume = "21", year = "2022", } @article{FogCarlosCastillo, author = "C. Castillo-Bot{\'o}n and David Casillas-P{\'e}rez and Carlos Casanova-Mateo and S. Ghimire and E. Cerro-Prada and Pedro Antonio Guti{\'e}rrez and R.C. Deo and Sancho Salcedo-Sanz", abstract = "Atmospheric low-visibility events are usually associated with fog formation. Extreme low-visibility events deeply affect the air and ground transportation, airports and motor-road facilities causing accidents and traffic problems every year. Machine Learning (ML) algorithms have been successfully applied to many fog formation and low-visibility prediction problems. The associated problem can be formulated either as a regression or as a classification task, which has an impact on the type of ML approach to be used and on the quality of the predictions obtained. In this paper we carry out a complete analysis of low-visibility events prediction problems, formulated as both regression and classification problems. We discuss the performance of a large number of ML approaches in each type of problem, and evaluate their performance under a common comparison framework. According to the obtained results, we will provide indications on what the most efficient formulation is to tackle low-visibility predictions and the best performing ML approaches for low-visibility events prediction.", awards = "JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES.", comments = "JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES.", doi = "10.1016/j.atmosres.2022.106157", issn = "0169-8095", journal = "Atmospheric Research", keywords = "Low-visibility events Orographic and hill-fogs Classification problems Regression problems Machine Learning algorithms", month = "July", note = "JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES.", pages = "106157", title = "{M}achine learning regression and classification methods for fog events prediction", url = "doi.org/10.1016/j.atmosres.2022.106157", volume = "272", year = "2022", } @article{COVID-19-forecasting-framework, author = "Miguel D{\'i}az-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Antonio Manuel G{\'o}mez-Orellana and Isaac T{\'u}{\~n}ez and Luis Ortigosa-Moreno and Armando Romanos-Rodr{\'i}guez and Javier Padillo-Ruiz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For this reason, predicting the SARS-CoV-2 transmission rate has become one of the most important and challenging problems of the past months. In this paper, we apply a two-stage mid and long-term forecasting framework to the epidemic situation in eight districts of Andalusia, Spain. First, an analytical procedure is performed iteratively to fit polynomial curves to the cumulative curve of contagions. Then, the extracted information is used for estimating the parameters and structure of an evolutionary artificial neural network with hybrid architectures (i.e., with different basis functions for the hidden nodes) while considering single and simultaneous time horizon estimations. The results obtained demonstrate that including polynomial information extracted during the training stage significantly improves the mid- and long-term estimations in seven of the eight considered districts. The increase in average accuracy (for the joint mid- and long-term horizon forecasts) is 37.61% and 35.53% when considering the single and simultaneous forecast approaches, respectively.", awards = "JCR(2022): 8.5, Position: 23/275 (Q1) Category: ENGINEERING, ELECTRICAL {\&} ELECTRONIC", comments = "JCR(2022): 8.5, Position: 23/275 (Q1) Category: ENGINEERING, ELECTRICAL {\&} ELECTRONIC", doi = "10.1016/j.eswa.2022.117977", issn = "0957-4174", journal = "Expert Systems with Applications", keywords = "COVID-19 contagion forecasting, Curve decomposition, Evolutionary artificial neural networks, Time series", month = "June", note = "JCR(2022): 8.5, Position: 23/275 (Q1) Category: ENGINEERING, ELECTRICAL {\&} ELECTRONIC", pages = "117977", title = "{COVID}-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: {A} case study in {A}ndalusia, {S}pain", url = "doi.org/10.1016/j.eswa.2022.117977", volume = "207", year = "2022", } @article{TracolimusCancerLiverTransplantation2022, author = "Manuel Rodr{\'i}guez-Per{\'a}lvarez and Jordi Colmenero and Antonio Gonz{\'a}lez and Mikel Gastaca and Anna Curell and Ar{\'a}nzazu Caballero-Marcos and Ana S{\'a}nchez-Mart{\'i}nez and Tommaso Di Maira and Jos{\'e} Ignacio Herrero and Carolina Almohalla and Sara Lorente and Antonio Cuadrado-Lav{\'i}n and Sonia Pascual and Mar{\'i}a {\'A}ngeles L{\'o}pez-Garrido and Rocio Gonz{\'a}lez-Grande and Antonio Manuel G{\'o}mez-Orellana and Rafael Alejandre and Javier Zamora-Olaya and Carmen Bernal-Bellido", abstract = "Cancer is the leading cause of death after liver transplantation (LT). This multicenter case–control nested study aimed to evaluate the effect of maintenance immunosuppression on post-LT malignancy. The eligible cohort included 2495 LT patients who received tacrolimus-based immunosuppression. After 13 922 person/years follow-up, 425 patients (19.7%) developed malignancy (cases) and were matched with 425 controls by propensity score based on age, gender, smoking habit, etiology of liver disease, and hepatocellular carcinoma (HCC) before LT. The independent predictors of post-LT malignancy were older age (HR = 1.06 [95% CI 1.05–1.07]; p r internal neoplasms (after excluding non-melanoma skin cancer). Therefore, tacrolimus minimization, as monitored by CET, is the key to modulate immunosuppression in order to prevent cancer after LT.", awards = "JCR(2022): 9.369 Position: 2/24 (Q1) Category: TRANSPLANTATION.", comments = "JCR(2022): 9.369 Position: 2/24 (Q1) Category: TRANSPLANTATION.", doi = "10.1111/ajt.17021", issn = "1600-6135", journal = "American Journal of Transplantation", keywords = "Hepatocellular carcinoma, immunosuppression, malignancy, neoplasm, tacrolimus", month = "March", note = "JCR(2022): 9.369 Position: 2/24 (Q1) Category: TRANSPLANTATION.", number = "6", pages = "1671--1682", title = "{C}umulative exposure to tacrolimus and incidence of cancer after liver transplantation", url = "doi.org/10.1111/ajt.17021", volume = "22", year = "2022", } @article{DeepOrdinalClassificationAAQ2022, author = "Riccardo Rosati and Luca Romeo and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Emanuele Frontoni", abstract = "Nowadays, decision support systems (DSSs) are widely used in several application domains, from industrial to healthcare and medicine fields. Concerning the industrial scenario, we propose a DSS oriented to the aesthetic quality control (AQC) task, which has quickly established itself as one of the most crucial challenges of Industry 4.0. Taking into account the increasing amount of data in this domain, the application of machine learning (ML) and deep learning (DL) techniques offers great opportunities to automatize the overall AQC process. State-of-the-art is mainly oriented to approach this problem with a nominal DL classification method which does not exploit the ordinal structure of the AQC task, thus not penalizing the error among distant AQC classes (which is a relevant aspect for the real use case). The paper introduces a DL ordinal methodology for the AQC classification. Differently from other deep ordinal methods, we combined the standard categorical cross-entropy with the cumulative link model and we imposed the ordinal constraint via the thresholds and slope parameters. Experimental results were performed for solving an AQC task on a novel image dataset originated from a specific company's demand (i.e., aesthetic assessment of wooden stocks). We demonstrated how the proposed methodology is able to reduce misclassification errors (up to 0.937 quadratic weight kappa loss) among distant classes while overcoming other state-of-the-art deep ordinal models and reducing the bias factor related to the item geometry. The proposed DL approach was integrated as the main core of a DSS supported by Internet of Things (IoT) architecture that can support the human operator by reducing up to 90% the time needed for the qualitative analysis carried out manually in this specific domain.", awards = "JCR (2022): 6.0 Position: 41/145 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR (2022): 6.0 Position: 41/145 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s00521-022-07050-6", issn = "0941-0643", journal = "Neural Computing and Applications", keywords = "Ordinal classification, Deep ordinal models, Quality control, Decision support system", month = "March", note = "JCR (2022): 6.0 Position: 41/145 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "11625--11639", title = "{A} novel deep ordinal classification approach for aesthetic quality control classification", url = "dx.doi.org/10.1007/s00521-022-07050-6", volume = "34", year = "2022", } @article{PRUnimodal2022, author = "V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Currently, the use of deep learning for solving ordinal classification problems, where categories follow a natural order, has not received much attention. In this paper, we propose an unimodal regularisation based on the beta distribution applied to the cross-entropy loss. This regularisation encourages the distribution of the labels to be a soft unimodal distribution, more appropriate for ordinal problems. Given that the beta distribution has two parameters that must be adjusted, a method to automatically determine them is proposed. The regularised loss function is used to train a deep neural network model with an ordinal scheme in the output layer. The results obtained are statistically analysed and show that the combination of these methods increases the performance in ordinal problems. Moreover, the proposed beta distribution performs better than other distributions proposed in previous works, achieving also a reduced computational cost.", awards = "JCR(2022): 8.0 Position: 22/145 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2022): 8.0 Position: 22/145 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.patcog.2021.108310", issn = "0031-3203", journal = "Pattern Recognition", keywords = "Ordinal regression, Unimodal distribution, Convolutional network, Beta distribution, Stick-breaking", month = "February", note = "JCR(2022): 8.0 Position: 22/145 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "108310", title = "{U}nimodal regularisation based on beta distribution for deep ordinal regression", url = "doi.org/10.1016/j.patcog.2021.108310", volume = "122", year = "2022", } @article{DJustice2022, author = "L. Dencik and Javier S{\'a}nchez-Monedero", abstract = "Data justice has emerged as a key framework for engaging with the intersection of datafication and society in a way that privileges an explicit concern with social justice. Engaging with justice concerns in the analysis of information and communication systems is not in itself new, but the concept of data justice has been used to denote a shift in understanding of what is at stake with datafication beyond digital rights. In this essay, we trace the lineage and outline some of the different traditions and approaches through which the concept is currently finding expression. We argue that in doing so, we are confronted with tensions that denote a politics of data justice both in terms of what is at stake with datafication and what might be suitable responses. ", awards = "JCI (2022): 3.6 Position: 15/411 (Q1) Category: LAW", comments = "JCI (2022): 3.6 Position: 15/411 (Q1) Category: LAW", doi = "10.14763/2022.1.1615", issn = "2197-6775", journal = "Internet Policy Review", keywords = "Social justice, Datafication, Digital society, Algorithms, Data, Access to justice", month = "January", note = "JCI (2022): 3.6 Position: 15/411 (Q1) Category: LAW", number = "1", title = "{D}ata justice", url = "doi.org/10.14763/2022.1.1615", volume = "11", year = "2022", } @article{ZonalMultiTaskEANN2022, author = "Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The prediction of wave height and flux of energy is essential for most ocean engineering applications. To simultaneously predict both wave parameters, this paper presents a novel approach using short-term time prediction horizons (6h and 12h). Specifically, the methodology proposed presents a twofold simultaneity: 1) both parameters are predicted by a single model, applying the multi-task learning paradigm, and 2) the prediction tasks are tackled for several neighbouring ocean buoys with such single model by the development of a zonal strategy. Multi-Task Evolutionary Artificial Neural Network (MTEANN) models are applied to two different zones located in the United States, considering measurements collected by three buoys in each zone. Zonal MTEANN models have been compared in a two-phased procedure: 1) against the three individual MTEANN models specifically trained for each buoy of the zone, and 2) against some state-of-the-art regression techniques. Results achieved show that the proposed zonal methodology obtains not only better performance than the individual MTEANN models, but it also requires a lower number of connections. Besides, the zonal MTEANN methodology outperforms state-of-the-art regression techniques. Hence, the proposed approach results in an excellent method for predicting both significant wave height and flux of energy at short-term prediction time horizons.", awards = "JCR(2022): 8.7 Position: 26/115 (Q1) Category: ENERGY {\&} FUELS", comments = "JCR(2022): 8.7 Position: 26/115 (Q1) Category: ENERGY {\&} FUELS", doi = "10.1016/j.renene.2021.11.122", issn = "0960-1481", journal = "Renewable Energy", keywords = "Wave height prediction, Energy flux prediction, Marine energy, Multi-task machine learning, Zonal models, Evolutionary artificial neural networks", month = "January", note = "JCR(2022): 8.7 Position: 26/115 (Q1) Category: ENERGY {\&} FUELS", pages = "975989", title = "{S}imultaneous short-term significant wave height and energy flux prediction using zonal multi-task evolutionary artificial neural networks", url = "doi.org/10.1016/j.renene.2021.11.122", volume = "184", year = "2022", } @article{2220221, author = "David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Javier Barbero-G{\'o}mez and Jos{\'e} V. Die and Pablo Gonz{\'a}lez Moreno", abstract = "La programaci{\'o}n ha sido tradicionalmente una competencia perteneciente a las ingenier{\'i}as, que recientemente est{\'a} adquiriendo una importancia significativa en {\'a}reas como Ciencias de la Vida, donde resulta fundamental para la resoluci{\'o}n de problemas basados en an{\'a}lisis de datos. Entre el alumnado de dichas {\'a}reas, se observa una necesidad de mejorar las habilidades relacionadas con la programaci{\'o}n aplicadas al an{\'a}lisis de datos. Similarmente, estudiantes de ingenier{\'i}a con capacidad t{\'e}cnica demostrada pueden carecer de la base tem{\'a}tica, igualmente fundamental para la resoluci{\'o}n de problemas. Mediante la herramienta del hackathon, y el trabajo en equipo, se ha combinado a especialistas (profesorado y alumnado) de ambas disciplinas a los cuales se enfrent{\'o} a una serie de problemas en los campos de la biolog{\'i}a, gen{\'e}tica, ciencias forestales y ambientales. Para la resoluci{\'o}n de estos problemas se utilizaron din{\'a}micas de competici{\'o}n entre los grupos. De cada equipo se valor{\'o} la metodolog{\'i}a empleada para la obtenci{\'o}n de los datos, su an{\'a}lisis, interpretaci{\'o}n de resultados, y exposici{\'o}n de las diversas tareas aplicadas para su resoluci{\'o}n. El proyecto ha conseguido que el alumnado resuelva los problemas planteados, dif{\'i}cilmente abordables con equipos de una sola titulaci{\'o}n, generando un aprendizaje com{\'u}n y una experiencia multidisciplinar para su formaci{\'o}n.", doi = "10.21071/ripadoc.v11i2.14185 ", issn = "2531-1336", journal = "Revista de Innovaci{\'o}n y Buenas Pr{\'a}cticas Docentes", number = "2", pages = "1-19", title = "{H}ackathon en docencia: aprendizaje autom{\'a}tico aplicado a {C}iencias de la {V}ida ", url = "http://www.uco.es/ucopress/ojs/index.php/ripadoc/article/view/14185", volume = "11", year = "2022", } @article{BiometricSytemsPoliticsRecognition2021, author = "Fieke Jansen and Javier S{\'a}nchez-Monedero and Lina Dencik", abstract = "Biometric identity systems are now a prominent feature of contemporary law enforcement, including in Europe. Often advanced on the premise of efficiency and accuracy, they have also been the subject of significant controversy. Much attention has focussed on longer-standing biometric data collection, such as finger-printing and facial recognition, foregrounding concerns with the impact such technologies can have on the nature of policing and fundamental human rights. Less researched is the growing use of voice recognition in law enforcement. This paper examines the case of the recent Speaker Identification Integrated Project, a European wide initiative to create the first international and interoperable database of voice biometrics, now the third largest biometric database at Interpol. Drawing on Freedom of Information requests, interviews and public documentation, we outline the emergence and features of SiiP and explore how voice is recognised and attributed meaning. We understand Speaker Identification Integrated Project as constituting a particular ‘regime of recognition’ premised on the use of soft biometrics (age, language, accent and gender) to disembed voice in order to optimise for difference. This, in turn, has implications for the nature and scope of law enforcement, people's position in society, and justice concerns more broadly.", awards = "JCR (2021): 8.731 Position: 1/111 (Q1) Category: SOCIAL SCIENCES, INTERDISCIPLINARY", comments = "JCR (2021): 8.731 Position: 1/111 (Q1) Category: SOCIAL SCIENCES, INTERDISCIPLINARY", doi = "10.1177/20539517211063604", issn = "2053-9517", journal = "Big Data \{\&} Society", keywords = "Speaker identification, biometrics, law enforcement, identity, politics of recognition", month = "December", note = "JCR (2021): 8.731 Position: 1/111 (Q1) Category: SOCIAL SCIENCES, INTERDISCIPLINARY", number = "2", pages = "20539517211063604", title = "{B}iometric identity systems in law enforcement and the politics of (voice) recognition: {T}he case of {S}ii{P}", url = "doi.org/10.1177/20539517211063604", volume = "8", year = "2021", } @article{BarberoCNNParkinsonESWA, author = "Javier Barbero-G{\'o}mez and Pedro Antonio Guti{\'e}rrez and V{\'i}ctor Manuel Vargas and Juan-Antonio Vallejo-Casas and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "3D image scans are an assessment tool for neurological damage in Parkinson's disease (PD) patients. This diagnosis process can be automatized to help medical staff through Decision Support Systems (DSSs), and Convolutional Neural Networks (CNNs) are good candidates, because they are effective when applied to spatial data. This paper proposes a 3D CNN ordinal model for assessing the level or neurological damage in PD patients. Given that CNN need large datasets to achieve acceptable performance, a data augmentation method is adapted to work with spatial data. We consider the Ordinal Graph-based Oversampling via Shortest Paths (OGO-SP) OGO-SP method, which applies a gamma probability distribution for inter-class data generation. A modification of OGO-SP is proposed, the OGO-SP-beta algorithm, which applies the beta distribution for generating synthetic samples in the inter-class region, a better suited distribution when compared to gamma. The evaluation of the different methods is based on a novel 3D image dataset provided by the Hospital Universitario Reina Sof{\'i}a (C{\'o}rdoba, Spain). We show how the ordinal methodology improves the performance with respect to the nominal one, and how OGO-SP-beta yields better performance than OGO-SP.", awards = "JCR(2021): 8.665 Position: 21/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2021): 8.665 Position: 21/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.eswa.2021.115271", issn = "0957-4174", journal = "Expert Systems with Applications", keywords = "Artificial Neural Networks, Ordinal Classification, Data augmentation Computer-Aided Diagnosis", month = "November", note = "JCR(2021): 8.665 Position: 21/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "115271", title = "{A}n ordinal {CNN} approach for the assessment of neurological damage in {P}arkinson's disease patients", url = "www.sciencedirect.com/science/article/pii/S0957417421007028", volume = "182", year = "2021", } @article{ClusteringGuijo19, author = "David Guijo-Rubio and Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Alicia Troncoso and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", awards = "JCR(2021): 19.118 Position: 3/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2021): 19.118 Position: 3/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1109/TCYB.2019.2962584", issn = "2168-2267", journal = "IEEE Transactions on Cybernetics", keywords = "Time series clustering, data mining, segmentation, feature extraction", month = "November", note = "JCR(2021): 19.118 Position: 3/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", number = "11", pages = "5409--5422", title = "{T}ime series clustering based on the characterisation of segment typologies", url = "doi.org/10.1109/TCYB.2019.2962584", volume = "51", year = "2021", } @article{PPCientificoDCompeticion2021, author = "David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Antonio Manuel Dur{\'a}n-Rosal and Antonio Manuel G{\'o}mez-Orellana and Javier Barbero-G{\'o}mez and Juan Carlos Fern{\'a}ndez and Pedro Antonio Guti{\'e}rrez", abstract = "La Ciencia de Datos es el {\'a}rea que comprende el desarrollo de m{\'e}todos cient{\'i}ficos, procesos y sistemas para extraer conocimiento a partir de datos recopilados previamente, con el objetivo de analizar los procedimientos llevados a cabo actualmente. El perfil profesional asociado a este campo es el del Cient{\'i}fico de Datos, generalmente llevado a cabo por Ingenieros Inform{\'a}ticos gracias a que las aptitudes y competencias adquiridas durante su formaci{\'o}n se ajustan perfectamente a lo requerido en este puesto laboral. Debido a la necesidad de formaci{\'o}n de nuevos Cient{\'i}ficos de Datos, entre otros fines, surgen plataformas en las que {\'e}stos pueden adquirir una amplia experiencia, como es el caso de Kaggle. El principal objetivo de esta experiencia docente es proporcionar al alumnado una experiencia pr{\'a}ctica con un problema real, as{\'i} como la posibilidad de cooperar y competir al mismo tiempo. As{\'i}, la adquisici{\'o}n y el desarrollo de las competencias necesarias en Ciencia de Datos se realiza en un entorno altamente motivador. La realizaci{\'o}n de actividades relacionadas con este perfil ha tenido una repercusi{\'o}n directa sobre el alumnado, siendo fundamental la motivaci{\'o}n, la capacidad de aprendizaje y el reciclaje continuo de conocimientos a los que se someten los Ingenieros Inform{\'a}ticos.", doi = "10.21071/ripadoc.v10i2.13256", issn = "2531-1336", journal = "Revista de Innovaci{\'o}n y Buenas Pr{\'a}cticas Docentes", keywords = "Competicion, Experciencia profesional, Inteligencia artificial, Perfil profesional", month = "Oct", number = "2", pages = "101-106", title = "{P}otenciando el perfil profesional {C}ient{\'i}fico de {D}atos mediante din{\'a}micas de competici{\'o}n", url = "doi.org/10.21071/ripadoc.v10i2.13256", volume = "10", year = "2021", } @article{Hyperthermia_vs_Normothermia_2021, author = "Angela Casado-Adam and Lidia Rodriguez-Ortiz and Sebastian Rufian-Pe{\~n}a and Cristobal Mu{\~n}oz-Casares and Teresa Caro-Cuenca and Rosa Ortega-Salas and Maria Auxiliadora Fernandez-Peralbo and Maria Dolores Luque-de-Castro and Juan M. Sanchez-Hidalgo and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Antonio Romero-Ruiz and Javier Brice{\~n}o and {\'A}lvaro Arjona-S{\'a}nchez", abstract = "Background: The treatment of ovarian carcinomatosis with cytoreductive surgery and HIPEC is still controversial. The effect and pharmacokinetics of the chemotherapeutics used (espe-cially taxanes) are currently under consideration. Methods: A phase II, simple blind and randomized controlled trial (NTC02739698) was performed. The trial included 32 patients with primary or recurrent ovarian carcinomatosis undergoing cytoreductive surgery (CRS) and intraoperative intraperitoneal chemotherapy with paclitaxel (PTX): 16 in hyperthermic (42–43 ◦C) and 16 in normothermic (37 ◦C) conditions. Tissue, serum and plasma samples were taken in every patient before and after intraperitoneal chemotherapy to measure the concentration of PTX. To analyze the immunohistochemical profile of p53, p27, p21, ki67, PCNA and caspase-3 and the pathological response, a scale of intensity and percentage of expression and a grouped Miller and Payne system were used, respectively. Perioperative characteristics and morbi-mortality were also analyzed. Results: The main characteristics of patients, surgical morbidity, hemotoxicity and nephrotoxicity were similar in both groups. The concentration of paclitaxel in the tissue was higher than that observed in plasma and serum, although no statistically significant differences were found between the two groups. No statistically significant association regarding pathological response and apoptosis (caspase-3) between both groups was proved. There were no statistically significant differences between the normothermic and the hyperthermic group for pathological response and apoptosis. Conclusions: The use of intraperitoneal PTX has proven adequate pharmacokinetics with reduction of cell cycle and proliferation markers globally without finding statistically significant differences between its administration under hyperthermia versus normothermia conditions.", awards = "JCR(2021): 4.964, Position: 54/172 (Q2) Category: MEDICINE, GENERAL {\&} INTERNAL", comments = "JCR(2021): 4.964, Position: 54/172 (Q2) Category: MEDICINE, GENERAL {\&} INTERNAL", doi = "10.3390/jcm11195785", issn = "2077-0383", journal = "Journal of Clinical Medicine", keywords = "Ovarian cancer, Peritoneal carcinomatosis, Intraperitoneal chemotherapy", month = "September", note = "JCR(2021): 4.964, Position: 54/172 (Q2) Category: MEDICINE, GENERAL {\&} INTERNAL", pages = "1-14", title = "{T}he {R}ole of {I}ntraperitoneal {I}ntraoperative {C}hemotherapy with {P}aclitaxel in the {S}urgical {T}reatment of {P}eritoneal {C}arcinomatosis from {O}varian {C}ancer—{H}yperthermia versus {N}ormothermia: {A} {R}andomized {C}ontrolled {T}ri", url = "doi.org/10.3390/jcm11195785", volume = "11", year = "2021", } @article{Duran2021a, author = "{\'A}ngel Carmona-Poyato and Nicolas Luis Fern{\'a}ndez-Garc{\'i}a and Francisco Jos{\'e} Madrid-Cuevas and Antonio Manuel Dur{\'a}n-Rosal", abstract = "Piecewise Linear Approximation is one of the most commonly used strategies to represent time series effectively and approximately. This approximation divides the time series into non-overlapping segments and approximates each segment with a straight line. Many suboptimal methods were proposed for this purpose. This paper proposes a new optimal approach, called OSFS, based on feasible space (FS) [1], that minimizes the number of segments of the approximation and guarantees the error bound using the -norm. On the other hand, a new performance measure combined with the OSFS method has been used to evaluate the performance of some suboptimal methods and that of the optimal method that minimizes the holistic approximation error (-norm). The results have shown that the OSFS method is optimal and demonstrates the advantages of -norm over -norm.", awards = "JCR(2021): 8.518 Position: 22/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2021): 8.518 Position: 22/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.patcog.2021.107917", issn = "0031-3203", journal = "Pattern Recognition", keywords = "Data representation, optimal time series segmentation, error bound guarantee, L-norm", month = "July", note = "JCR(2021): 8.518 Position: 22/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "107917", title = "{A} new approach for optimal offline time-series segmentation with error bound guarantee", url = "doi.org/10.1016/j.patcog.2021.107917", volume = "115", year = "2021", } @article{PLOSPulmon2021, author = "Due{\~n}as-Jurado, Jos{\'e} Mar{\'i}a and Pedro Antonio Guti{\'e}rrez and A. Casado-Adam and F. Santos-Luna and {\'A}ngel Salvatierra-Vel{\'a}zquez and S. C{\'a}rcel and C.J.C. Robles-Arista and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", awards = "JCR(2021): 3.752 Position: 29/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR(2021): 3.752 Position: 29/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1371/journal.pone.0252148", issn = "1932-6203", journal = "PLoS One", month = "June", note = "JCR(2021): 3.752 Position: 29/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", number = "6", pages = " e0252148", title = " {N}ew models for donor-recipient matching in lung transplantations", url = "doi.org/10.1371/journal.pone.0252148", volume = "16", year = "2021", } @article{Duran2021b, author = "Antonio Manuel Dur{\'a}n-Rosal and Julio Camacho-Ca{\~n}am{\'o}n and Pedro Antonio Guti{\'e}rrez and Maria Victoria Guiote-Moreno and Ester Rodr{\'i}guez-C{\'a}ceres and Juan Antonio Vallejo-Casas and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Parkinson's disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of 123I-ioflupane, considering a binary classification problem (absence or existence of Parkinson's disease). In this work, we propose a new methodology for classifying this kind of images in three classes depending on the level of severity of the disease in the image. To tackle this problem, we use an ordinal classifier given the natural order of the class labels. A novel strategy to perform feature selection is developed because of the large number of voxels in the image, and a method for generating synthetic images is proposed to improve the quality of the classifier. The methodology is tested on 434 studies conducted between September 2015 and January 2019, divided into three groups: 271 without alteration of the presynaptic nigrostriatal pathway, 73 with a slight alteration and 90 with severe alteration. Results confirm that the methodology improves the state-of-the-art algorithms, and that it is able to find informative voxels outside the standard regions of interest used for this problem. The differences are assessed by statistical tests which show that the proposed image ordinal classification could be considered as a decision support system in medicine.", awards = "JCR(2021): 4.996 Position: 19/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR(2021): 4.996 Position: 19/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1038/s41598-021-86538-y", issn = "2045-2322", journal = "Scientific Reports", keywords = "parkinson, data augmentation, ordinal classification", month = "March", note = "JCR(2021): 4.996 Position: 19/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", pages = "7067", title = "{O}rdinal classification of the affectation level of 3{D}-images in {P}arkinson diseases", url = "www.nature.com/articles/s41598-021-86538-y", volume = "11", year = "2021", } @article{Billel2021JCLP, author = "Billel Amiri and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and Rabah Dizene and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Kahina Dahmani", abstract = "An efficient management of solar power systems requires direct and continuous predictions of global irradiation received on inclined planes. This paper proposes a new approach that simultaneously estimates and forecasts inclined solar irradiation. The method is based on a multi-task Hybrid Evolutionary Neural Network with two output neurons: one estimates the irradiation at the current instant and another predicts it for the next hour. An Evolutionary Algorithm is used to learn the most proper topology (number of neurons and connections). Two studies are carried out to evaluate the performance of the method, considering experimental ground data for the same inclination angle and satellite data with different tilt angles. The data only contain one measured variable, what improves its applicability to other sites. The potential of three different basis functions in the hidden layer is compared (Sigmoidal Units, Radial Basis Functions and Product Units), concluding that the results achieved by Sigmoidal Units are better. Single and multi-task models are also compared with a statistical analysis, which shows no significant differences. However, the proposed multi-task option is much simpler and computationally efficient than individual models. The problem tackled is more complex and challenging than previous works, due to inclined solar irradiation is predicted based on the horizontal irradiation and also because the model simultaneously estimates and predicts irradiation. However, the performance obtained is excellent compared to the literature.", awards = "JCR(2021): 11.072 Position: 24/279 (Q1) Category: ENVIRONMENTAL SCIENCES", comments = "JCR(2021): 11.072 Position: 24/279 (Q1) Category: ENVIRONMENTAL SCIENCES", doi = "10.1016/j.jclepro.2020.125577", issn = "0959-6526", journal = "Journal of Cleaner Production", keywords = "solar forecasting, inclined plane, artificial neural networks, evolutionary learning, hybrid algorithms, optimization", month = "March", note = "JCR(2021): 11.072 Position: 24/279 (Q1) Category: ENVIRONMENTAL SCIENCES", pages = "125577", title = "{A} novel approach for global solar irradiation forecasting on tilted plane using hybrid evolutionary neural networks", url = "doi.org/10.1016/j.jclepro.2020.125577", volume = "287", year = "2021", } @article{Pablo Rodríguez2021, author = "Pablo Rodr{\'i}guez and Santiago Gra{\~n}a and Eva Elisa Alvarez-Le{\'o}n and Manuela Battaglini and Francisco Javier Darias and Miguel A. Hern{\'a}n and Raquel L{\'o}pez and Paloma Llaneza and Maria Cristina Mart{\'i}n and Oriana Ramirez-Rubio and Adriana Roman{\'i} and Berta Su{\'a}rez-Rodr{\'i}guez and Javier S{\'a}nchez-Monedero and Alex Arenas and Lucas Lacasa", abstract = "While Digital contact tracing (DCT) has been argued to be a valuable complement to manual tracing in the containment of COVID-19, no empirical evidence of its effectiveness is available to date. Here, we report the results of a 4-week population-based controlled experiment that took place in La Gomera (Canary Islands, Spain) between June and July 2020, where we assessed the epidemiological impact of the Spanish DCT app Radar Covid. After a substantial communication campaign, we estimate that at least 33% of the population adopted the technology and further showed relatively high adherence and compliance as well as a quick turnaround time. The app detects about 6.3 close-contacts per primary simulated infection, a significant percentage being contacts with strangers, although the spontaneous follow-up rate of these notified cases is low. Overall, these results provide experimental evidence of the potential usefulness of DCT during an epidemic outbreak in a real population.", awards = "JCR (2021): 17.694 Position: 6/73 (Q1) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR (2021): 17.694 Position: 6/73 (Q1) Category: MULTIDISCIPLINARY SCIENCES", issn = "2041-1723", journal = "Nature Communications", month = "January", note = "JCR (2021): 17.694 Position: 6/73 (Q1) Category: MULTIDISCIPLINARY SCIENCES", number = "1", pages = "587", title = "{A} population-based controlled experiment assessing the epidemiological impact of digital contact tracing", url = "doi.org/10.1038/s41467-020-20817-6", volume = "12", year = "2021", } @article{orellana2021, author = "Antonio Manuel G{\'o}mez-Orellana and Juan Carlos Fern{\'a}ndez and Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", awards = "JCR(2021): 3.252 Position: 80/119 (Q3) Category: ENERGY {\&} FUELS", comments = "JCR(2021): 3.252 Position: 80/119 (Q3) Category: ENERGY {\&} FUELS", doi = "https://doi.org/10.3390/en14020468", issn = "1996-1073", journal = "Energies", month = "January", note = "JCR(2021): 3.252 Position: 80/119 (Q3) Category: ENERGY {\&} FUELS", number = "2", pages = "468", title = "{B}uilding {S}uitable {D}atasets for {S}oft {C}omputing and {M}achine {L}earning {T}echniques from {M}eteorological {D}ata {I}ntegration: {A} {C}ase {S}tudy for {P}redicting {S}ignificant {W}ave {H}eight and {E}nergy {F}lux", url = "http://doi.org/10.3390/en14020468", volume = "14", year = "2021", } @article{132020, author = "Javier S{\'a}nchez-Monedero and Lina Dencik", awards = "JCR(2021): 5.054 Position: 12/148 (Q1) Category: SOCIOLOGY", comments = "JCR(2021): 5.054 Position: 12/148 (Q1) Category: SOCIOLOGY", journal = "Information, Communication and Society", note = "JCR(2021): 5.054 Position: 12/148 (Q1) Category: SOCIOLOGY", title = "{T}he politics of deceptive borders: ’biomarkers of deceit’ and the case of i{B}order", url = "doi.org/10.1080/1369118X.2020.1792530", volume = "Accepted", year = "2021", } @article{Ana Valdivia2021, author = "Ana Valdivia and Javier S{\'a}nchez-Monedero and Jorge Casillas", awards = "JCR(2021): 8.993 Position: 20/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2021): 8.993 Position: 20/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", journal = "International Journal of Intelligent Systems", note = "JCR(2021): 8.993 Position: 20/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", number = "4", pages = "1619-1643", title = "{H}ow fair can we go in machine learning? {A}ssessing the boundaries of fairness in decision trees", url = "doi.org/10.1002/int.22354", volume = "36", year = "2021", } @article{PlosGuijo2021, author = "David Guijo-Rubio and Javier Brice{\~n}o and Pedro Antonio Guti{\'e}rrez and Maria Dolores Ayll{\'o}n and Rub{\'e}n Ciria and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Donor-Recipient (D-R) matching is one of the main challenges to be fulfilled nowadays. Due to the increasing number of recipients and the small amount of donors in liver transplantation, the allocation method is crucial. In this paper, to establish a fair comparison, the United Network for Organ Sharing database was used with 4 different end-points (3 months, and 1, 2 and 5 years), with a total of 39, 189 D-R pairs and 28 donor and recipient variables. Modelling techniques were divided into two groups: 1) classical statistical methods, including Logistic Regression (LR) and Na{\"i}ve Bayes (NB), and 2) standard machine learning techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB) or Support Vector Machines (SVM), among others. The methods were compared with standard scores, MELD, SOFT and BAR. For the 5-years end-point, LR (AUC = 0.654) outperformed several machine learning techniques, such as MLP (AUC = 0.599), GB (AUC = 0.600), SVM (AUC = 0.624) or RF (AUC = 0.644), among others. Moreover, LR also outperformed standard scores. The same pattern was reproduced for the others 3 end-points. Complex machine learning methods were not able to improve the performance of liver allocation, probably due to the implicit limitations associated to the collection process of the database.", awards = "JCR(2021): 3.752 Position: 29/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR(2021): 3.752 Position: 29/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1371/journal.pone.0252068", journal = "PLoS One", keywords = "machine learning, statistical techniques, donor-recipient matching, liver transplant, transplantation, liver, liver transplantation, UNOS database, UNOS", month = "Mayo", note = "JCR(2021): 3.752 Position: 29/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", number = "5", pages = " e0252068", title = "{S}tatistical methods versus machine learning techniques for donor-recipient matching in liver transplantation", url = "doi.org/10.1371/journal.pone.0252068", volume = "16", year = "2021", } @article{Pamucar2020, author = "Dragan Pamucar and Morteza Yazdani and Radojko Obradovic and Anil Kumar and Mercedes Torres-Jim{\'e}nez", abstract = "The objectives of this study are to mitigate the risk and disturbances to the supply chain, to offer required models for resolving the complex issues that arise, and to maintain the stability of the support system. Also, the uncertain conditions in a supply chain force decision‐makers and experts to adopt a fuzzy‐based evaluation platform to ensure secure and reliable consequences. The current study proposed a fuzzy neutrosophic decision‐making approach for supplier evaluation and selection. The model is composed of a new weight aggregator that uses pairwise comparison, which has not been reported to date. The model uses a Dombi aggregator that is more qualified than other aggregators. The Dombi t‐conorms and t‐norms have the same properties as those of the general t‐conorm and t‐norm, which can enhance the flexibility of the information aggregation process via the adjustment of a parameter. A decision‐making environment with uncertain conditions and multiple factors is supposed. We applied this approach in a construction company to analyse the suppliers in a resilient supply chain management (RSCM) system using a MABAC (multiattribute border approximation area comparison) tool. The accuracy of the proposed model was examined via sensitivity analysis tests. This study proposes a novel fuzzy‐neutrosophic‐based approach for resilient supplier selection. The main contributions of this study work are the design, implementation and analysis of a multiattribute evaluation system with respect to fuzzy neutrosophic values. In this evaluation system, a new pairwise comparison is conducted with trapezoidal neutrosophic linguistic variables to determine the importance weights of supplier criteria. Typically, the provision of opinions regarding the qualitative performances of suppliers is a difficult and confusing responsibility for experts and supplier evaluators. Therefore, the proposed approach overcomes this problem by utilizing a pairwise comparison by neutrosophic values and proposes original Dombi aggregation operators for dealing with fuzzy neutrosophic sets.", awards = "JCR(2020): 8.709 Position: 12/139 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2020): 8.709 Position: 12/139 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1002/int.22279", issn = "0884-8173", journal = "International Journal of Intelligent Systems", keywords = "Dombi aggregators, fuzzy decision making, MABAC, neutrosophic sets, resilient supply chain", month = "December", note = "JCR(2020): 8.709 Position: 12/139 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", number = "12", pages = "1934-1986", title = "{A} novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection probl", url = "doi.org/10.1002/int.22279", volume = "35", year = "2020", } @article{1520181, author = "Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and Laura Cornejo-Bueno and Luis Prieto and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Wind power ramp events (WPREs) are strong increases or decreases of wind speed in a short period of time. Predicting WPREs in wind farms is of vital importance given that they can produce damages in the turbines, and, in any case, they suddenly affect the wind farm production. In contrast to previous binary definitions of the prediction problem (ramp versus non-ramp), a three-class prediction model is used in this paper, proposing a novel discretization function, able to detect the nature of WPREs: negative ramp, non-ramp and positive ramp events. Moreover, the natural order of these labels is exploited to obtain better results in the prediction of these events. The independent variables used for prediction include, in this case, past wind speed values and meteorological data obtained from physical models (reanalysis data). Reanalysis will be also used for recovering missing data from the measuring stations in the wind farm. The proposed prediction methodology is based on Reservoir Computing and an over-sampling process for alleviating the high degree of unbalance in the dataset (non-ramp events are much more frequent than ramps). Three elements are combined in the prediction method: a recurrent neural network layer, a nonlinear kernel mapping and an ordinal logistic regression,to exploit the information provided by the order of the classes). Preprocessing is based on a variation of the standard synthetic minority over-sampling technique (SMOTE), which is applied to the reservoir activations (since the direct application over the input variables would damage its temporal structure). The performance of the method is analysed by comparing it against other state-of-the-art classifiers, such as Support Vector Machines, nominal logistic regression, an autoregressive ordinal neural network, or the use of leaky integrator neurons instead of the standard sigmoidal units. From the results obtained, the benefits of the kernel mapping and the ordinal model are clear, and, in general, the performance obtained with the Reservoir Computing approach is shown to be very robust in the detection of ramps.", awards = "JCR(2020): 2.908 Position: 63/139 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2020): 2.908 Position: 63/139 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s11063-018-9922-5", issn = "1370-4621", journal = "Neural Processing Letters", month = "December", note = "JCR(2020): 2.908 Position: 63/139 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", number = "3", pages = "57--74", title = "{O}rdinal multi-class architecture for predicting wind power ramp events based on reservoir computing", url = "doi.org/10.1007/s11063-018-9922-5", volume = "52", year = "2020", } @article{SolarEnergyGuijo2920, author = "David Guijo-Rubio and Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Antonio Manuel G{\'o}mez-Orellana and Carlos Casanova-Mateo and Julia Sanz-Justo and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper evaluates the performance of different evolutionary neural network models in a problem of solar radiation prediction at Toledo, Spain. The prediction problem has been tackled exclusively from satellite-based measurements and variables, which avoids the use of data from ground stations or atmospheric soundings. Specifically, three types of neural computation approaches are considered: neural networks with sigmoid-based neurons, radial basis function units and product units. In all cases these neural computation algorithms are trained by means of evolutionary algorithms, leading to robust and accurate models for solar radiation prediction. The results obtained in the solar radiation estimation at the radiometric station of Toledo show an excellent performance of evolutionary neural networks tested. The structure sigmoid unit-product unit with evolutionary training has been shown as the best model among all tested in this paper, able to obtain an extremely accurate prediction of the solar radiation from satellite images data, and outperforming all other evolutionary neural networks tested, and alternative Machine Learning approaches such as Support Vector Regressors or Extreme Learning Machines.", awards = "JCR(2020): 7.147 Position: 3/62 (Q1) Category: THERMODYNAMICS", comments = "JCR(2020): 7.147 Position: 3/62 (Q1) Category: THERMODYNAMICS", doi = "10.1016/j.energy.2020.118374", issn = "1873-6785", journal = "Energy", keywords = "Solar radiation estimation, evolutionary artificial neural networks, satellite data, physical models", month = "November", note = "JCR(2020): 7.147 Position: 3/62 (Q1) Category: THERMODYNAMICS", pages = "118374", title = "{E}volutionary artificial neural networks for accurate solar radiation prediction", url = "doi.org/10.1016/j.energy.2020.118374", volume = "210", year = "2020", } @article{COOTGuijo2020, author = "David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Purpose of review: Machine Learning techniques play an important role in organ transplantation. Analysing the main tasks for which they are being applied, together with the advantages and disadvantages of their use, can be of crucial interest for clinical practitioners. Recent findings: In the last 10 years, there has been an explosion of interest in the application of ML techniques to organ transplantation. Several approaches have been proposed in the literature aiming to find universal models by considering multicenter cohorts or from different countries. Moreover, recently, deep learning has also been applied demonstrating a notable ability when dealing with a vast amount of information. Summary: Organ transplantation can benefit from ML in such a way to improve the current procedures for donor-recipient matching or to improve standard scores. However, a correct preprocessing is needed to provide consistent and high quality databases for ML algorithms, aiming to robust and fair approaches to support expert decision-making systems.", awards = "JCR(2020): 2.640 Position: 16/25 (Q3) Category: TRANSPLANTATION", comments = "JCR(2020): 2.640 Position: 16/25 (Q3) Category: TRANSPLANTATION", issn = "1087-2418", journal = "Current Opinion in Organ Transplantation", keywords = "Machine learning, organ transplantation, liver transplant, unos database", month = "August", note = "JCR(2020): 2.640 Position: 16/25 (Q3) Category: TRANSPLANTATION", number = "4", pages = "399-405", title = "{M}achine learning methods in organ transplantation", url = "doi.org/10.1097/MOT.0000000000000774", volume = "25", year = "2020", } @article{Vargas2020Neucom, author = "V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper proposes a deep convolutional neural network model for ordinal regression by considering a family of probabilistic ordinal link functions in the output layer. The link functions are those used for cumulative link models, which are traditional statistical linear models based on projecting each pattern into a 1-dimensional space. A set of ordered thresholds splits this space into the different classes of the problem. In our case, the projections are estimated by a non-linear deep neural network. To further improve the results, we combine these ordinal models with a loss function that takes into account the distance between the categories, based on the weighted Kappa index. Three different link functions are studied in the experimental study, and the results are contrasted with statistical analysis. The experiments run over two different ordinal classification problems and the statistical tests confirm that these models improve the results of a nominal model and outperform other robust proposals considered in the literature.", awards = "JCR(2020): 5.719 Position: 30/140 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2020): 5.719 Position: 30/140 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1016/j.neucom.2020.03.034", issn = "0925-2312", journal = "Neurocomputing", keywords = "Deep learning, Ordinal regression, Cumulative link models, Kappa index", month = "August", note = "JCR(2020): 5.719 Position: 30/140 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", pages = "48-58", title = "{C}umulative link models for deep ordinal classification", url = "doi.org/10.1016/j.neucom.2020.03.034", volume = "401", year = "2020", } @article{Duran2019_optimal, author = "{\'A}ngel Carmona-Poyato and Nicol{\'a}s Luis Fern{\'a}ndez-Garc{\'i}a and Francisco Jos{\'e} Madrid-Cuevas and Antonio Manuel Dur{\'a}n-Rosal", abstract = "Emerging technologies have led to the creation of huge databases that require reducing their high dimensionality to be analysed. Many suboptimal methods have been proposed for this purpose. On the other hand, few efficient optimal methods have been proposed due to their high computational complexity. However, these methods are necessary to evaluate the performance of suboptimal methods. This paper proposes a new optimal approach, called OSTS, to improve the segmentation of time series. The proposed method is based on A* algorithm and it uses an improved version of the well-known Salotti method for obtaining optimal polygonal approximations. Firstly, a suboptimal method for time-series segmentation is applied to obtain pruning values. In this case, a suboptimal method based on Bottom-Up technique is selected. Then, the results of the suboptimal method are used as pruning values to reduce the computational time of the proposed method. The proposal has been compared to other suboptimal methods and the results have shown that the method is optimal, and, in some cases, the computational time is similar to other suboptimal methods.", awards = "JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.patrec.2020.04.006", issn = "0167-8655", journal = "Pattern Recognition Letters", month = "July", note = "JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "153-159", title = "{A} new approach for optimal time-series segmentation", url = "doi.org/10.1016/j.patrec.2020.04.006", volume = "135", year = "2020", } @article{CarlosCastillo2020Water, author = "Carlos Castillo-Bot{\'o}n and David Casillas-P{\'e}rez and Carlos Casanova-Mateo and L.M. Moreno-Saavedra and B. Morales-D{\'i}az and J. Sanz-Justo and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz", abstract = "This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persistence-based algorithms. On the other hand, the short-term analysis of the dammed water level in the hydropower reservoir was modeled as a prediction problem, where machine learning regression techniques were studied. A set of models, including different types of neural networks, Support Vector regression, or Gaussian processes was tested. Real data from a hydropower reservoir located in Galicia, Spain, qwew considered, together with predictive variables from upstream measuring stations. We show that the techniques presented in this paper offer an excellent tool for the long- and short-term analysis and prediction of dammed water level in reservoirs for hydropower purposes, especially important for the management of water resources in areas with hydrology stress, such as Spain.", awards = "JCR(2020): 3.103 Position: 39/98 (Q2) Category: WATER RESOURCES -- SCIE", comments = "JCR(2020): 3.103 Position: 39/98 (Q2) Category: WATER RESOURCES -- SCIE", doi = "10.3390/w12061528", issn = "2073-4441", journal = "Water", keywords = "dammed water level, hydropower reservoirs, detrended fluctuation analysis, ARMA models, machine learning regressors, reservoir management", month = "May", note = "JCR(2020): 3.103 Position: 39/98 (Q2) Category: WATER RESOURCES -- SCIE", number = "6", pages = "1528", title = "{A}nalysis and {P}rediction of {D}ammed {W}ater {L}evel in a {H}ydropower {R}eservoir {U}sing {M}achine {L}earning and {P}ersistence-{B}ased {T}echniques", url = "dx.doi.org/10.3390/w12061528", volume = "12", year = "2020", } @article{DeepSCRO2020, author = "Alejandro Mart{\'i}n and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and David Camacho and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Convolutional Neural Networks stands at the front of many solutions which deal with computer vision related tasks. The use and the applications of these models are growing unceasingly, as well as the complexity required to deal with bigger and highly complex problems. However, hitting the most suitable model for solving a specific task is not trivial. A very manually intensive and time consuming trial-and-error experimentation is needed in order to find an architecture, hyperparameters and parameters which reach a certain level of performance. Moreover, this process leads to oversized models, diminishing their generalisation capacity. In this paper, we leverage a metaheuristic and a hybridisation process to optimise the reasoning block of CNN models, composed by fully connected and dropout layers, conducting a full reconstruction that leads to lighter models with better performance. Our approach is architecture-independent and operates at the topology, hyperparameters and parameters (connection weights) levels. For that purpose, we have implemented the Hybrid Statistically-driven Coral Reef Optimisation (HSCRO) algorithm as an extension of SCRO, a metaheuristic which does not require to adjust any parameter since they are automatically and dynamically chosen based on the statistical characteristics of the evolution. In addition, a hybridisation process employs the backpropagation algorithm to make a final fine-grained weights adjustment. In the experiments, the VGG-16 model is successfully optimised in two different scenarios (the CIFAR-10 and the CINIC-10 datasets), resulting in a lighter architecture, with an 88% reduction of the connection weights, but without losing its generalisation performance.", awards = "JCR(2020): 6.725 Position: 11/112 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", comments = "JCR(2020): 6.725 Position: 11/112 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", doi = "10.1016/j.asoc.2020.106144", issn = "1568-4946", journal = "Applied Soft Computing", keywords = "Neuroevolution, Convolutional Neural Networks, Optimisation, Hybridisation, Coral Reef Optimisation", month = "May", note = "JCR(2020): 6.725 Position: 11/112 (Q1) Category: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS", pages = "106144", title = "{O}ptimising {C}onvolutional {N}eural {N}etworks using a {H}ybrid {S}tatistically-driven {C}oral {R}eef {O}ptimisation algorithm", url = "doi.org/10.1016/j.asoc.2020.106144", volume = "90", year = "2020", } @article{TormentasBarajas2019ATMOS, author = "David Guijo-Rubio and Carlos Casanova-Mateo and Juilia Sanz-Justo and Pedro Antonio Guti{\'e}rrez and Sara Cornejo-Bueno and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Sancho Salcedo-Sanz", abstract = "In this paper we tackle a problem of convective situations analysis at Adolfo-Suarez Madrid-Barajas International Airport (Spain), based on Ordinal Regression algorithms. The diagnosis of convective clouds is key in a large airport like Barajas, since these meteorological events are associated with strong winds and local precipitation, which may affect air and land operations at the airport. In this work, we deal with a 12-h time horizon in the analysis of convective clouds, using as input variables data from a radiosonde station and also from numerical weather models. The information about the objective variable (convective clouds presence at the airport) has been obtained from the Madrid-Barajas METAR and SPECI aeronautical reports. We treat the problem as an ordinal regression task, where there exist a natural order among the classes. Moreover, the classification problem is highly imbalanced, since there are very few convective clouds events compared to clear days. Thus, a process of oversampling is applied to the database in order to obtain a better balance of the samples for this specific problem. An important number of ordinal regression methods are then tested in the experimental part of the work, showing that the best approach for this problem is the SVORIM algorithm, based on the Support Vector Machine strategy, but adapted for ordinal regression problems. The SVORIM algorithm shows a good accuracy in the case of thunderstorms and Cumulonimbus clouds, which represent a real hazard for the airport operations.", awards = "JCR(2020): 5.369 Position: 16/94 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES", comments = "JCR(2020): 5.369 Position: 16/94 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES", doi = "10.1016/j.atmosres.2019.104798", issn = "0169-8095", journal = "Atmospheric Research", keywords = "Convective clouds, Convective analysis, Airports, Machine learning techniques, Ordinal regression", month = "May", note = "JCR(2020): 5.369 Position: 16/94 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES", pages = "104798", title = "{O}rdinal regression algorithms for the analysis of convective situations over {M}adrid-{B}arajas airport", url = "doi.org/10.1016/j.atmosres.2019.104798", volume = "236", year = "2020", } @article{ApAcoustics2020, author = "Francisco Javier Jim{\'e}nez-Romero and David Guijo-Rubio and Francisco Ram{\'o}n Lara-Raya and Antonio Ruiz-Gonz{\'a}lez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "In the last decade, the sound quality of electric induction mjavascript:moveDown('liauthorsfield5')otors is a hot topic in the research field. Specially, due to its high number of applications, the population is exposed to physical and psychological discomfort caused by the noise emission. Therefore, it is necessary to minimise its psychological impact on the population. In this way, the main goal of this work is to evaluate the use of multitask artificial neural networks as a modelling technique for simultaneously predicting psychoacoustic parameters of induction motors. Several inputs are used, such as, the electrical magnitudes of the motor power signal and the number of poles, instead of separating the noise of the electric motor from the environmental noise. Two different kind of artificial neural networks are proposed to evaluate the acoustic quality of induction motors, by using the equivalent sound pressure, the loudness, the roughness and the sharpness as outputs. Concretely, two different topologies have been considered: simple models and more complex models. The former are more interpretable, while the later lead to higher accuracy at the cost of hiding the cause-effect relationship. Focusing on the simple interpretable models, product unit neural networks achieved the best results: 38.77 for MSE and 13.11 for SEP. The main benefit of this product unit model is its simplicity, since only 10 inputs variables are used, outlining the effective transfer mechanism of multitask artificial neural networks to extract common features of multiple tasks. Finally, a deep analysis of the acoustic quality of induction motors in done using the best product unit neural networks.", awards = "JCR(2020): 2.639 Position: 11/32 (Q2) Category: ACOUSTICS", comments = "JCR(2020): 2.639 Position: 11/32 (Q2) Category: ACOUSTICS", doi = "10.1016/j.apacoust.2020.107332", issn = "0003-682X", journal = "Applied Acoustics", keywords = "Artificial neural networks, Sigmoid units, Pulse width modulation, Sound quality, Induction motor", month = "March", note = "JCR(2020): 2.639 Position: 11/32 (Q2) Category: ACOUSTICS", pages = "107332", title = "{V}alidation of artificial neural networks to model the acoustic behaviour of induction motors", url = "doi.org/10.1016/j.apacoust.2020.107332", volume = "166", year = "2020", } @article{Neunet2020, author = "Manuel Dorado-Moreno and N. Navarin and Pedro Antonio Guti{\'e}rrez and L. Prieto and A. Sperduti and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "In Machine Learning, the most common way to address a given problem is to optimize an error measure by training a single model to solve the desired task. However, sometimes it is possible to exploit latent information from other related tasks to improve the performance of the main one, resulting in a learning paradigm known as Multi-Task Learning (MTL). In this context, the high computational capacity of deep neural networks (DNN) can be combined with the improved generalization performance of MTL, by designing independent output layers for every task and including a shared representation for them. In this paper we exploit this theoretical framework on a problem related to Wind Power Ramps Events (WPREs) prediction in wind farms. Wind energy is one of the fastest growing industries in the world, with potential global spreading and deep penetration in developed and developing countries. One of the main issues with the majority of renewable energy resources is their intrinsic intermittency, which makes it difficult to increase the penetration of these technologies into the energetic mix. In this case, we focus on the specific problem of WPREs prediction, which deeply affect the wind speed and power prediction, and they are also related to different turbines damages. Specifically, we exploit the fact that WPREs are spatially-related events, in such a way that predicting the occurrence of WPREs in different wind farms can be taken as related tasks, even when the wind farms are far away from each other. We propose a DNN-MTL architecture, receiving inputs from all the wind farms at the same time to predict WPREs simultaneously in each of the farms locations. The architecture includes some shared layers to learn a common representation for the information from all the wind farms, and it also includes some specification layers, which refine the representation to match the specific characteristics of each location. Finally we modified the Adam optimization algorithm for dealing with imbalanced data, adding costs which are updated dynamically depending on the worst classified class. We compare the proposal against a baseline approach based on building three different independent models (one for each wind farm considered), and against a state-of-the-art reservoir computing approach. The DNN-MTL proposal achieves very good performance in WPREs prediction, obtaining a good balance for all the classes included in the problem (negative ramp, no ramp and positive ramp).", awards = "JCR(2020): 8.050 Position: 25/273 (Q1) Category: NEUROSCIENCES", comments = "JCR(2020): 8.050 Position: 25/273 (Q1) Category: NEUROSCIENCES", doi = "10.1016/j.neunet.2019.12.017", issn = "0893-6080", journal = "Neural Networks", keywords = "Wind power ramp events, Multi-task learning, Multi-output, Deep neural networks, Renewable energies", month = "March", note = "JCR(2020): 8.050 Position: 25/273 (Q1) Category: NEUROSCIENCES", pages = "401-411", title = "{M}ulti-task learning for the prediction of wind power ramp events with deep neural networks", url = "doi.org/10.1016/j.neunet.2019.12.017", volume = "123", year = "2020", } @article{NCAA2020, author = "David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Carlos Casanova-Mateo and Juan Carlos Fern{\'a}ndez and Antonio Manuel G{\'o}mez-Orellana and Pablo Salvador-Gonz{\'a}lez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The prediction of convective clouds formation is a very important problem in different areas such as agriculture, natural hazards prevention or transport-related facilities, among others. In this paper we evaluate the capacity of different types of evolutionary artificial neural networks to predict the formation of convective clouds, tackling the problem as a classification task. We use data from Madrid-Barajas airport, including variables and indices derived from the Madrid-Barajas airport radiosonde station. As objective variable, we use the cloud information contained in the METAR and SPECI meteorological reports from the same airport and we consider a prediction time-horizon of 12 hours. The performance of different types of evolutionary artificial neural networks has been discussed and analysed, including three types of basis functions (Sigmoidal Unit, Product Unit and Radial Basis Function), and two types of models, a mono-objective evolutionary algorithm with two objective functions and a multi-objective evolutionary algorithm optimised by the two objective functions simultaneously. We show that some of the developed neuro-evolutionary models obtain high quality solutions to this problem, due to its high unbalance characteristic.", awards = "JCR(2020): 5.606 Position: 31/140 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2020): 5.606 Position: 31/140 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", issn = " 0941-0643", journal = "Neural Computing and Applications", keywords = "Convection initialization prediction, machine learning algorithms, neural networks, unbalanced databases", month = "February", note = "JCR(2020): 5.606 Position: 31/140 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "13917-13929", title = "{P}rediction of convective clouds formation using evolutionary neural computation techniques", url = "doi.org/10.1007/s00521-020-04795-w", volume = "32", year = "2020", } @article{VIH-ANN2020, author = "Antonio Rivero-Ju{\'a}rez and David Guijo-Rubio and Francisco T{\'e}llez and Rosario Palacios and Dolores Merino and Juan Mac{\'i}as and Juan Carlos Fern{\'a}ndez and Pedro Antonio Guti{\'e}rrez and Antonio Rivero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", awards = "JCR(2020): 3.240 Position: 26/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR(2020): 3.240 Position: 26/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1371/journal.pone.0227188", issn = "1932-6203", journal = "PLoS One", keywords = "Hepatitis C Virus, Artificial Neural Networks, Typology of patients", note = "JCR(2020): 3.240 Position: 26/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", number = "1", pages = "e0227188", title = "{U}sing machine learning methods to determine a typology of patients with {HIV}-{HCV} infection to be treated with antivirals", url = "doi.org/10.1371/journal.pone.0227188", volume = "15", year = "2020", } @article{OceanEngineeringGuijo2020, author = "David Guijo-Rubio and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper presents a novel approach to tackle simultaneously short- and long-term energy flux prediction (specifically, at 6h, 12h, 24h and 48h time horizons). The methodology proposed is based on the Multi-Task Learning paradigm in order to solve the four problems with a single model. We consider Multi-Task Evolutionary Artificial Neural Networks (MTEANN) with four outputs, one for each time prediction horizon. For this purpose, three buoys located at the Gulf of Alaska are considered. Measurements collected by these buoys are used to obtain the target values of energy flux, whereas, only reanalysis data are used as input values, allowing the applicability to other locations. The performance of three different basis functions (Sigmoidal Unit, Radial Basis Function and Product Unit) are compared against some popular stateof-the-art approaches such as Extreme Learning Machines and Support Vector Regressors. The results show that MTEANN methodology using Sigmoidal Units in the hidden layer and a linear output achieves the best performance. In this way, the multi-task methodology is an excellent and lower-complexity approach for energy flux prediction at both short- and long-term prediction time horizons. Furthermore, the results also confirm that reanalysis data is enough for describing well the problem tackled.", awards = "JCR(2020): 3.795 Position: 1/16 (Q1) Category: MARINE ENGINEERING", comments = "JCR(2020): 3.795 Position: 1/16 (Q1) Category: MARINE ENGINEERING", doi = "10.1016/j.oceaneng.2020.108089", issn = "0029-8018", journal = "Ocean Engineering", keywords = "Ocean Engineering, Flux of energy prediction", month = "108089", note = "JCR(2020): 3.795 Position: 1/16 (Q1) Category: MARINE ENGINEERING", title = "{S}hort- and long-term energy flux prediction using {M}ulti-{T}ask {E}volutionary {A}rtificial {N}eural {N}etworks", url = "doi.org/10.1016/j.oceaneng.2020.108089", volume = "216", year = "2020", } @article{Perales19, author = "C. Perales-Gonz{\'a}lez and Mariano Carbonero-Ruz and David Becerra-Alonso and J. P{\'e}rez-Rodr{\'i}guez and Francisco Fernandez-Navarro", awards = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1016/j.neucom.2019.06.040", issn = "0925-2312", journal = "Neurocomputing", keywords = "Extreme Learning Machine, Ensemble, Hierarchy, Diversity, Negative Correlation", month = "October", note = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", pages = "196--211", title = "{R}egularized ensemble neural networks models in the {E}xtreme {L}earning {M}achine framework", url = "doi.org/10.1016/j.neucom.2019.06.040", volume = "361", year = "2019", } @article{OCAPIS2019, author = "Mar{\'i}a Cristina Heredia-G{\'o}mez and Salvador Garc{\'i}a and Pedro Antonio Guti{\'e}rrez and Francisco Herrera", abstract = "Ordinal data are those where a natural order exists between the labels. The classification and preprocessing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis (Ordinal Classification and Preprocessing in Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two preprocessing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript", doi = "10.1007/s13748-019-00175-1", issn = "2192-6360", journal = "Progress in Artificial Intelligence", keywords = "Ordinal classification, Ordinal regression, Data preprocessing, Machine learning, R, Scala ", month = "September", number = "3", pages = "287-292", title = "{OCAPIS}: {R} package for {O}rdinal {C}lassification and {P}reprocessing in {S}cala", url = "doi.org/10.1007/s13748-019-00175-1", volume = "8", year = "2019", } @article{Duran2018_DBBePSO, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and {\'A}ngel Carmona-Poyato and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Large time series are difficult to be mined and preprocessed, hence reducing their number of points with minimum information loss is an active field of study. This paper proposes new methods based on time series segmentation, including the adaptation of the particle swarm optimisation algorithm (PSO) to this problem, and more advanced PSO versions, such as barebones PSO (BBPSO) and its exploitation version (BBePSO). Moreover, a novel algorithm is derived, referred to as dynamic exploitation barebones PSO (DBBePSO), which updates the importance of the social and cognitive components throughout the generations. All these algorithms are further improved by considering a final local search step based on the combination of two well-known standard segmentation algorithms (Bottom-Up and Top-Down). The performance of the different methods is evaluated using 15 time series from various application fields, and the results show that the novel algorithm (DBBePSO) and its hybrid version (HDBBePSO) outperform the rest of segmentation techniques.", awards = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1016/j.neucom.2018.05.129", issn = "0925-2312", journal = "Neurocomputing", month = "August", note = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", pages = "45--55", title = "{A} hybrid dynamic exploitation barebones particle swarm optimisation algorithm for time series segmentation", url = "doi.org/10.1016/j.neucom.2018.05.129", volume = "353", year = "2019", } @article{Duran2019Dynamical, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", doi = "https://doi.org/10.1007/s13748-019-00176-0", issn = "2192-6352", journal = "Progress in Artificial Intelligence", month = "June", number = "2", pages = "253-262", title = "{D}ynamical {M}emetization in {C}oral {R}eef {O}ptimization {A}lgorithms for {O}ptimal {T}ime {S}eries {A}pproximation", url = "doi.org/10.1007/s13748-019-00176-0", volume = "8", year = "2019", } @article{MonotonicReview2019, author = "Jos{\'e}-Ram{\'o}n Cano and Pedro Antonio Guti{\'e}rrez and Bartosz Krawczyk and Michal Wozniak and Salvador Garc{\'i}a", abstract = "Currently, knowledge discovery in databases is an essential first step when identifying valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview of the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of monotonic classification research in specialized literature and can be used as a functional guide for the field.", awards = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1016/j.neucom.2019.02.024", issn = "0925-2312", journal = "Neurocomputing", keywords = "Monotonic classification, Ordinal classification, Taxonomy, Software, Performance metrics, Monotonic data sets", month = "May", note = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", pages = "168-182", title = "{M}onotonic classification: {A}n overview on algorithms, performance measures and data sets", url = "doi.org/10.1016/j.neucom.2019.02.024", volume = "341", year = "2019", } @article{ValverdeMoreno19, author = "M. Valverde-Moreno and Mercedes Torres-Jim{\'e}nez and A.M. Lucia-Casademunt and Y. Mu{\~n}oz-Oca{\~n}a", awards = "JCR(2019): 2.067 Position: 45/138 (Q2) Category: PSYCHOLOGY, MULTIDISCIPLINARY", comments = "JCR(2019): 2.067 Position: 45/138 (Q2) Category: PSYCHOLOGY, MULTIDISCIPLINARY", doi = "10.3389/fpsyg.2019.00723", issn = "1664-1078", journal = "Frontiers in Psychology", keywords = "employees participation in decision making, PDM, perceived supervisor support, gender gap in PDM, national cultural values, European countries", month = "April", note = "JCR(2019): 2.067 Position: 45/138 (Q2) Category: PSYCHOLOGY, MULTIDISCIPLINARY", number = "723", pages = "1--13", title = "{C}ross cultural analysis of direct employee participation: dealing with gender and cultural values", url = "doi.org/10.3389/fpsyg.2019.00723", volume = "10", year = "2019", } @article{1620162, author = "Mar{\'i}a P{\'e}rez-Ortiz and Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Javier S{\'a}nchez-Monedero and A. Nikolaou and Francisco Fernandez-Navarro and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series.", awards = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1016/j.neucom.2016.11.101", issn = "0925-2312", journal = "Neurocomputing", month = "January", note = "JCR(2019): 4.438 Position: 28/136 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", pages = "3-14", title = "{O}n the use of evolutionary time series analysis for segmenting paleoclimate data", url = "doi.org/10.1016/j.neucom.2016.11.101", volume = "326-327", year = "2019", } @article{ORCAPaper, author = "Javier S{\'a}nchez-Monedero and Pedro Antonio Guti{\'e}rrez and Mar{\'i}a P{\'e}rez-Ortiz", abstract = "Ordinal regression, also named ordinal classification, studies classification problems where there exist a natural order between class labels. This structured order of the labels is crucial in all steps of the learning process in order to take full advantage of the data. ORCA (Ordinal Regression and Classification Algorithms) is a Matlab/Octave framework that implements and integrates different ordinal classification algorithms and specifically designed performance metrics. The framework simplifies the task of experimental comparison to a great extent, allowing the user to: (i) describe experiments by simple configuration files; (ii) automatically run different data partitions; (iii) parallelize the executions; (iv) generate a variety of performance reports and (v) include new algorithms by using its intuitive interface. Source code, binaries, documentation, descriptions and links to data sets and tutorials (including examples of educational purpose) are available at https://github.com/ayrna/orca.", awards = "JCR(2019): 3.484 Position: 40/136 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2019): 3.484 Position: 40/136 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", issn = "1533-7928", journal = "Journal of Machine Learning Research", keywords = "Ordinal regression, ordinal classification, Matlab, Octave, threshold models", note = "JCR(2019): 3.484 Position: 40/136 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", number = "125", pages = "1--5", title = "{ORCA}: {A} {M}atlab/{O}ctave {T}oolbox for {O}rdinal {R}egression", url = "www.jmlr.org/papers/v20/", volume = "20", year = "2019", } @article{Comino2019Validation, author = "Francisco Comino and David Guijo-Rubio and Manuel Ruiz de Adana and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", awards = "JCR(2019): 3.461 Position: 11/61 (Q1) Category: THERMODYNAMICS", comments = "JCR(2019): 3.461 Position: 11/61 (Q1) Category: THERMODYNAMICS", issn = "0140-7007", journal = "International Journal of Refrigeration", note = "JCR(2019): 3.461 Position: 11/61 (Q1) Category: THERMODYNAMICS", pages = "434-442", title = "{V}alidation of multitask artificial neural networks to model desiccant wheels activated at low temperature", url = "doi.org/10.1016/j.ijrefrig.2019.02.002", volume = "100", year = "2019", } @article{Fernandez2019Multi, author = "Juan Carlos Fern{\'a}ndez and Mariano Carbonero-Ruz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", awards = "JCR(2019): 3.325 Position: 42/136 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2019): 3.325 Position: 42/136 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "https://doi.org/10.1007/s10489-019-01447-y", issn = "0924-669X", journal = "Applied Intelligence", note = "JCR(2019): 3.325 Position: 42/136 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", number = "9", pages = "3447-3463", title = "{M}ulti-objective evolutionary optimization using the relationship between {F}1 and accuracy metrics in classification tasks", url = "http://doi.org/10.1007/s10489-019-01447-y", volume = "49", year = "2019", } @article{Fernandez2019a, author = "Silvia Jim{\'e}nez-Fern{\'a}ndez and Carlos Camacho-G{\'o}mez and Ricardo Mallol-Poyato and Juan Carlos Fern{\'a}ndez and Javier Del Ser and Antonio Portilla-Figueras and Sancho Salcedo-Sanz", abstract = "In this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.", awards = "JCR(2019): 2.576 Position: 120/265 (Q2) Category: ENVIRONMENTAL SCIENCES", comments = "JCR(2019): 2.576 Position: 120/265 (Q2) Category: ENVIRONMENTAL SCIENCES", doi = "https://doi.org/10.3390/su11010169", issn = "2071-1050", journal = "Sustainability", note = "JCR(2019): 2.576 Position: 120/265 (Q2) Category: ENVIRONMENTAL SCIENCES", number = "1", pages = "169", title = "{O}ptimal {M}icrogrid {T}opology {D}esign and {S}iting of {D}istributed {G}eneration {S}ources {U}sing a {M}ulti-{O}bjective {S}ubstrate {L}ayer {C}oral {R}eefs {O}ptimization {A}lgorithm", url = "doi.org/10.3390/su11010169", volume = "11", year = "2019", } @article{Duran2018_fog, author = "Antonio Manuel Dur{\'a}n-Rosal and Juan Carlos Fern{\'a}ndez and Carlos Casanova-Mateo and Julia Sanz-Justo and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper proposes the application of novel artificial neural networks with evolutionary training and different basic functions, (sigmoidal, product and radial), for a real problem of fog events classification from meteorological input variables. Specifically, a Multiobjective Evolutionary Algorithm is considered as artificial neural network training mechanism in order to obtain a binary classification model for the detection of fog events at Valladolid airport (Spain). The evolutionary neural models developed are based on two-dimensional performance measures: traditional accuracy and the minimum sensitivity, as the lowest percentage of examples correctly predicted as belonging to each class with respect to the total number of examples in the corresponding class. These performance measures are directly related to features associated with any classifier: its global performance and the rate of the worst classified class. These two objectives are usually in conflict when the optimization process tries to construct models with a high classification rate level in the generalization dataset, and also with a good classification level for each class or minimum sensitivity. A sensitivity analysis of the proposed models is carried out, and thus the subjacent relations between the input variables and the output classification target can be better understood.", awards = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.asoc.2018.05.035", issn = "1568-4946", journal = "Applied Soft Computing", month = "September", note = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "347-358", title = "{E}fficient {F}og {P}rediction with {M}ulti-objective {E}volutionary {N}eural {N}etworks", url = "doi.org/10.1016/j.asoc.2018.05.035", volume = "70", year = "2018", } @article{32016, author = "Juan Carlos Fern{\'a}ndez and Manuel Cruz-Ram{\'i}rez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper proposes a framework to obtain ensembles of classifiers from a Multi-objective Evolutionary Algorithm (MOEA), improving the restrictions imposed by two non-cooperative performance measures for multiclass problems: (1) the Correct Classification Rate or Accuracy (CCR) and, (2) the Minimum Sensitivity (MS) of all classes, i.e., the lowest percentage of examples correctly predicted as belonging to each class with respect to the total number of examples in the corresponding class. The proposed framework is based on collecting Pareto fronts of Artificial Neural Networks models for multiclass problems by the Memetic Pareto Evolutionary NSGA2 (MPENSGA2) algorithm, and it builds a new Pareto front (ensemble) from stored fronts. The ensemble built significantly improves the closeness to the optimum solutions and the diversity of the Pareto front. For verifying it, the performance of the new front obtained has been measured with the habitual use of weighting methodologies, such as Majority Voting, Simple Averaging and Winner Takes All. In addition to CCR and MS measures, three trade-off measures have been used to obtain the goodness of a Pareto front as a whole: Hyperarea, Laumanns’s Hyperarea (LAUMANNS) and Zitzler’s Spread (M3). The proposed framework can be adapted for any MOEA that aims to improve the compaction and diversity of its Pareto front, and whose fitness functions impose severe restrictions for multiclass problems.", awards = "JCR(2018): 4.664 Position: 21/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2018): 4.664 Position: 21/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "http://dx.doi.org/10.1007/s00521-016-2781-y", issn = "0941-0643", journal = "Neural Computing and Applications", keywords = "Ensemble, Multi-objective Evolutionary Algorithm, Multiclass classification, Artificial Neural Networks, Minimum Sensitivity, Pareto Performance measures", month = "June", note = "JCR(2018): 4.664 Position: 21/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", number = "1", pages = "289-305", title = "{S}ensitivity versus accuracy in ensemble models of {A}rtificial {N}eural {N}etworks from {M}ulti-objective {E}volutionary {A}lgorithms", url = "http://dx.doi.org/10.1007/s00521-016-2781-y", volume = "30", year = "2018", } @article{DuranINS2018, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Francisco Jos{\'e} Mart{\'i}nez-Estudillo and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time series segmentation is aimed at representing a time series by using a set of segments. Some researchers perform segmentation by approximating each segment with a simple model (e.g. a linear interpolation), while others focus their efforts on obtaining homogeneous groups of segments, so that common patterns or behaviours can be detected. The main hypothesis of this paper is that both objectives are conflicting, so time series segmentation is proposed to be tackled from a multiobjective perspective, where both objectives are simultaneously considered, and the expert can choose the desired solution from a Pareto Front of different segmentations. A specific multiobjective evolutionary algorithm is designed for the purpose of deciding the cut points of the segments, integrating a clustering algorithm for fitness evaluation. The experimental validation of the methodology includes three synthetic time series and three time series from real-world problems. Nine clustering quality assessment metrics are experimentally compared to decide the most suitable one for the algorithm. The proposed algorithm shows good performance for both clustering quality and reconstruction error, improving the results of other mono-objective alternatives of the state-of-the-art and showing better results than a simple weighted linear combination of both corresponding fitness functions.", awards = "JCR(2018): 5.524 Position: 9/155 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", comments = "JCR(2018): 5.524 Position: 9/155 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", doi = "10.1016/j.ins.2018.02.041", issn = "0020-0255", journal = "Information Sciences", keywords = "Time series segmentation, multiobjective optimisation, clustering, evolutionary computation", month = "May", note = "JCR(2018): 5.524 Position: 9/155 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", pages = "186--201", title = "{S}imultaneous optimisation of clustering quality and approximation error for time series segmentation", url = "doi.org/10.1016/j.ins.2018.02.041", volume = "442-443", year = "2018", } @article{ASOC2018, author = "Javier S{\'a}nchez-Monedero and Mar{\'i}a P{\'e}rez-Ortiz and Aurora S{\'a}ez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Melanoma is a type of cancer that develops from the pigment-containing cells known as melanocytes. Usually occurring on the skin, early detection and diagnosis is strongly related to survival rates. Melanoma recognition is a challenging task that nowadays is performed by well trained dermatologists who may produce varying diagnosis due to the task complexity. This motivates the development of automated diagnosis tools, in spite of the inherent difficulties (intra-class variation, visual similarity between melanoma and non-melanoma lesions, among others). In the present work, we propose a system combining image analysis and machine learning to detect melanoma presence and severity. The severity is assessed in terms of melanoma thickness, which is measured by the Breslow index. Previous works mainly focus on the binary problem of detecting the presence of the melanoma. However, the system proposed in this paper goes a step further by also considering the stage of the lesion in the classification task. To do so, we extract 100 features that consider the shape, colour, pigment network and texture of the benign and malignant lesions. The problem is tackled as a five-class classification problem, where the first class represents benign lesions, and the remaining four classes represent the different stages of the melanoma (via the Breslow index). Based on the problem definition, we identify the learning setting as a partial order problem, in which the patterns belonging to the different melanoma stages present an order relationship, but where there is no order arrangement with respect to the benign lesions. Under this assumption about the class topology, we design several proposals to exploit this structure and improve data preprocessing. In this sense, we experimentally demonstrate that those proposals exploiting the partial order assumption achieve better performance than 12 baseline nominal and ordinal classifiers (including a deep learning model) which do not consider this partial order. To deal with class imbalance, we additionally propose specific over-sampling techniques that consider the structure of the problem for the creation of synthetic patterns. The experimental study is carried out with clinician-curated images from the Interactive Atlas of Dermoscopy, which eases reproducibility of experiments. Concerning the results obtained, in spite of having augmented the complexity of the classification problem with more classes, the performance of our proposals in the binary problem is similar to the one reported in the literature.", awards = "JCR(2018): 4.873 Position: 11/106 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2018): 4.873 Position: 11/106 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.asoc.2017.11.042", journal = "Applied Soft Computing", keywords = "Melanoma, Computer vision, Machine learning, Ordinal classification, Partial order, Skin cancer", month = "March", note = "JCR(2018): 4.873 Position: 11/106 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "341-355", title = "{P}artial order label decomposition approaches for melanoma diagnosis", url = "doi.org/10.1016/j.asoc.2017.11.042", volume = "64", year = "2018", } @article{Ayllon2018, author = "Mar{\'i}a Dolores Ayll{\'o}n and Rub{\'e}n Ciria and Manuel Cruz-Ram{\'i}rez and Mar{\'i}a P{\'e}rez-Ortiz and Irene G{\'o}mez and Roberto Valente and John O’Grady and Manuel de la Mata and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Nigel D. Heaton and Javier Brice{\~n}o", abstract = "n 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in Espa{\~n}a [MADRE]). The aim is to test the ANN-based methodology in a different European health care system in order to validate it. An ANN model was designed using a cohort of patients from King’s College Hospital (KCH; n=822). The ANN was trained and tested using KCH pairs for both 3- and 12-month survival models. End points were probability of graft survival (correct classification rate [CCR]) and nonsurvival (minimum sensitivity [MS]). The final model is a rule-based system for facilitating the decision about the most appropriate D-R matching. Models designed for KCH had excellent prediction capabilities for both 3 months (CCR-area under the curve [AUC]=0.94; MS-AUC=0.94) and 12 months (CCR-AUC=0.78; MS-AUC=0.82), almost 15% higher than the best obtained by other known scores such as Model for End-Stage Liver Disease and balance of risk. Moreover, these results improve the previously reported ones in the multicentric MADR-E database. In conclusion, the use of ANN for D-R matching in LT in other health care systems achieved excellent prediction capabilities supporting the validation of these tools. It should be considered as the most advanced, objective, and useful tool to date for the management of waiting lists.", awards = "JCR(2018): 4.159 Position: 16/203 (Q1) Category: SURGERY", comments = "JCR(2018): 4.159 Position: 16/203 (Q1) Category: SURGERY", doi = "10.1002/lt.24870", issn = "1527-6465", journal = "Liver Transplantation", month = "February", note = "JCR(2018): 4.159 Position: 16/203 (Q1) Category: SURGERY", number = "2", pages = "192--203", title = "{V}alidation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantati", url = "doi.org/10.1002/lt.24870", volume = "24", year = "2018", } @article{PUNNTimeSeries2016, author = "Francisco Fernandez-Navarro and Maria Angeles de la Cruz and Pedro Antonio Guti{\'e}rrez and Adiel Casta{\~n}o-M{\'e}ndez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time Series Forecasting (TSF) consists on estimating models to predict future values based on previously observed values of time series, and it can be applied to solve many real-world problems. TSF has been traditionally tackled by considering AutoRegressive Neural Networks (ARNNs) or Recurrent Neural Networks (RNNs), where hidden nodes are usually configured using additive activation functions, such as sigmoidal functions. ARNNs are based on a short-term memory of the time series in the form of lagged time series values used as inputs, while RNNs include a long-term memory structure. The objective of this paper is twofold. First, it explores the potential of multiplicative nodes for ARNNs, by considering Product Unit (PU) activation functions, motivated by the fact that PUs are specially useful for modelling highly correlated features, such as the lagged time series values used as inputs for ARNNs. Second, it proposes a new hybrid RNN model based on PUs, by estimating the PU outputs from the combination of a long-term reservoir and the short-term lagged time series values. A complete set of experiments with 29 datasets shows competitive performance for both model proposals, and a set of statistical tests confirms that they achieve the state-of-the-art in TSF, with specially promising results for the proposed hybrid RNN. The experiments in this paper shows that the recurrent model is very competitive for relatively large time series, where longer forecast horizons are required, while the autorregresive model is a good selection if the dataset is small or if a low computational cost is needed. ", awards = "JCR(2018): 4.664 Position: 21/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2018): 4.664 Position: 21/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s00521-016-2494-2", issn = " 0941-0643", journal = "Neural Computing and Applications", keywords = "Time Series Forecasting, Product Unit Neural Networks, Recurrent Neural Networks, Evolutionary Neural Networks", month = "February", note = "JCR(2018): 4.664 Position: 21/133 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", number = "3", pages = "779-791", title = "{T}ime series forecasting by recurrent product unit neural networks", url = "http://dx.doi.org/10.1007/s00521-016-2494-2", volume = "29", year = "2018", } @article{GarciaJurado18, author = "A. Garc{\'i}a-Jurado and P. Castro-Gonz{\'a}lez and Mercedes Torres-Jim{\'e}nez and A. Leal-Rodriguez", awards = "JCR(2018): 1.381 Position: 14/23 (Q3) Category: COMPUTER SCIENCE, CYBERNETICS.", comments = "JCR(2018): 1.381 Position: 14/23 (Q3) Category: COMPUTER SCIENCE, CYBERNETICS.", doi = "10.1108/K-07-2018-0350", issn = "0368-492X", journal = "Kybernetes", keywords = "E-commerce, Milennial, Gamification, Technology acceptance model, Flow, Behavior", note = "JCR(2018): 1.381 Position: 14/23 (Q3) Category: COMPUTER SCIENCE, CYBERNETICS.", number = "6", pages = "1278--1300", title = "{V}aluating the role of {G}amification and {F}low in e-consumers: {M}illennials versus {G}eneration {X}", url = "doi.org/10.1108/K-07-2018-0350", volume = "48", year = "2018", } @article{Ricardo Cruz2018, author = "Ricardo Cruz and Kelwin Fernandes and Joaquim F. Pinto-Costa and Mar{\'i}a P{\'e}rez-Ortiz and Jaime S. Cardoso", awards = "JCR(2018): 1.410 Position: 92/133 (Q3) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2018): 1.410 Position: 92/133 (Q3) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1007/s10044-018-0705-4", journal = "Pattern Analysis and Applications", note = "JCR(2018): 1.410 Position: 92/133 (Q3) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "931--939", title = "{B}inary {R}anking for {O}rdinal {C}lass {I}mbalance", url = "http://doi.org/10.1007/s10044-018-0705-4", volume = "21", year = "2018", } @article{SCRO2018, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper is focused on reducing the number of elements in time series with minimum information loss, with specific applications on time series segmentation. A modification of the coral reefs optimization metaheuristic (CRO) is proposed for this purpose, which is called statistical CRO (SCRO), where the main parameters of the algorithm are adjusted based on the mean and standard deviation associated with the fitness distribution. Moreover, the algorithm is combined with the Bottom-Up and Top-Down methodologies (traditional local search methods for time series segmentation), resulting in a hybrid methodology (HSCRO). We evaluate the performance of these algorithms using 16 time series from different application areas. The statistically-driven version of CRO is shown to improve the results of the standard CRO, eliminating the necessity of manually adjusting the main parameters of the algorithm and dynamically adjusting these parameters throughout the evolution. Moreover, when compared with other local search methods and metaheuristics from the state of the art, HSCRO shows robust segmentation results, consistently obtaining lower approximation errors.", awards = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1016/j.asoc.2017.11.037", issn = "1568-4946", journal = "Applied Soft Computing", note = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", pages = "139-153", title = "{A} statistically-driven {C}oral {R}eef {O}ptimization algorithm for optimal size reduction of time series", url = "doi.org/10.1016/j.asoc.2017.11.037", volume = "63", year = "2018", } @article{energy-computational-models, author = "M.D. Redel-Mac{\'i}as and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Pedro Antonio Guti{\'e}rrez and S. Pinzi and A.J. Cubero-Atienza and M.P. Dorado", abstract = "The properties of biodiesel differ depending on feedstock fatty acid content. Moreover, biodiesel fatty acid composition influences the combustion process. For these reasons, noise emissions of a direct injection Perkins diesel engine fueled with olive pomace oil methyl ester (monounsaturated methyl esters) and palm oil methyl ester (saturated methyl esters) were studied under several steady-state engine operating conditions. In this work, different approaches for sound prediction of the engine based on Neural Network (NN) models, such as Product Unit NN (PUNN), Radial Basis Function NN (RBFNN) and response surface models have been proposed. Error was measured considering Mean Square Error (MSE) and Standard Error of Prediction (SEP). It can be concluded that the use of a hybrid model combining PU and RBF improves noise prediction accuracy, providing an acceptable value of both MSE and SEP when monounsaturated methyl ester/diesel fuel blends are used. However, best results for saturated methyl ester/diesel fuel blends were achieved by PUNN model. Whereas taking into account the simplicity of the model, PUNN model is the most appropriate for both monounsaturated and saturated methyl ester/diesel fuel blends. Response surface models have shown worse results based on the coefficient of correlation. Also, the effect of independent variables in the models has been studied and an inverse relationship between frequency and engine noise has been found", awards = "JCR(2018): 5.537 Position: 3/60 (Q1) Category: THERMODYNAMICS", comments = "JCR(2018): 5.537 Position: 3/60 (Q1) Category: THERMODYNAMICS", doi = "10.1016/j.energy.2017.11.143", issn = "0360-5442", journal = "Energy", keywords = "biodiesel, combustion noise, evolutionary computation, product unit neural networks, radial basic function, regression model", note = "JCR(2018): 5.537 Position: 3/60 (Q1) Category: THERMODYNAMICS", pages = "110-119", title = "{C}omputational models to predict noise emissions of a diesel engine fueled with saturated and monounsaturated fatty acid methyl esters", url = "doi.org/10.1016/j.energy.2017.11.143", volume = "144", year = "2018", } @article{Guijo2018FogOrdinal, author = "David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Carlos Casanova-Mateo and Julia Sanz-Justo and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The prediction of low-visibility events is very important in many human activities, and crucial in transportation facilities such as airports, where they can cause severe impact in flight scheduling and safety. The design of accurate predictors for low-visibility events can be approached by modelling future visibility conditions based on past values of different input variables, recorded at the airport. The use of autoregressive time series forecasters involves adjusting the order of the model (number of past series values or size of the sliding window), which usually depends on the dynamical nature of the time series. Moreover, the same window size is normally used for all the data, thought it would be reasonable to use different sliding windows. In this paper, we propose a hybrid prediction model for daily low-visibility events, which combines fixed-size and dynamic windows, and adapts its size according to the dynamics of the time series. Moreover, visibility is labelled using three ordered categories (FOG, MIST and CLEAR), and the prediction is then carried out by means of ordinal classifiers, in order to take advantage of the ordinal nature of low-visibility events. We evaluate the model using a dataset from Valladolid airport (Spain), where radiation fog is very common in autumn and winter months. The considered data set includes five different meteorological input variables (wind speed and direction, temperature, relative humidity and QNH - pressure adjusted at mean sea level) and the Runway Visual Range (RVR), which is used to characterize the low-visibility events at the airport. The results show that the proposed hybrid window model with ordinal classification leads to very robust performance prediction in daily time-horizon, improving the results obtained by the persistence model and alternative prediction schemes tested.", awards = "JCR(2018): 4.114 Position: 13/86 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES", comments = "JCR(2018): 4.114 Position: 13/86 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES", doi = "https://doi.org/10.1016/j.atmosres.2018.07.017", journal = "Atmospheric Research", month = "Diciembre", note = "JCR(2018): 4.114 Position: 13/86 (Q1) Category: METEOROLOGY {\&} ATMOSPHERIC SCIENCES", pages = "64-73", title = "{P}rediction of low-visibility events due to fog using ordinal classification", url = "doi.org/10.1016/j.atmosres.2018.07.017", volume = "214", year = "2018", } @article{ibexDuran2017, author = "Antonio Manuel Dur{\'a}n-Rosal and M{\'o}nica de la Paz Mar{\'i}n and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The discovery of useful patterns embodied in a time series is of fundamental relevance in many real applications. Repetitive structures and common type of segments can also provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterization methodology combining a hybrid genetic algorithm and a clustering technique to automatically group common patterns from this kind of financial time series and address the problem of identifying stock market prices trends. This hybrid genetic algorithm includes a local search method aimed to improve the quality of the final solution. The local search algorithm is based on maximizing a likelihood ratio, assuming normality for the series and the subseries in which the original one is segmented. To do so, we select two stock market index time series: IBEX35 Spanish index (closing prices) and a weighted average (AVG) time series of the IBEX35 (Spanish), BEL20 (Belgian), CAC40 (French) and DAX (German) indexes. These are processed to obtain segments that are mapped into a five dimensional space composed of five statistical measures, with the purpose of grouping them according to their statistical properties. Experimental results show that it is possible to discover homogeneous patterns in both time series.", awards = "JCR(2017): 1.787 Position: 63/132 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2017): 1.787 Position: 63/132 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", issn = "1370-4621", journal = "Neural Processing Letters", month = "December", note = "JCR(2017): 1.787 Position: 63/132 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", number = "3", pages = "767-790", title = "{I}dentifying market behaviours using {E}uropean {S}tock {I}ndex time series by a hybrid segmentation algorithm", url = "doi.org/10.1007/s11063-017-9592-8", volume = "46", year = "2017", } @article{Duran2017ESWH, author = "Antonio Manuel Dur{\'a}n-Rosal and Juan Carlos Fern{\'a}ndez and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper presents a methodology for the detection and prediction of Segments containing very high Significant Wave Height (SSWH) values in oceans. This kind of prediction is needed in order to account for potential changes in a long-term future operational environment of marine and coastal structures. The methodology firstly characterizes the wave height time series by approximating it using a sequence of labeled segments, and then a binary classifier is trained to predict the occurrence of SSWH periods based on past height values. A genetic algorithm (GA) combined with a likelihood-based local search is proposed for the first stage (detection), and the second stage (prediction) is tackled by an Artificial Neural Network (ANN) trained with a Multiobjective Evolutionary Algorithm (MOEA). Given the unbalanced nature of the dataset (SSWH are rarer than non SSWH), the MOEA is specifically designed to obtain a balance between global accuracy and individual sensitivities for both classes. The results obtained show that the GA is able to group SSWH in a specific cluster of segments and that the MOEA obtains ANN models able to perform an acceptable prediction of these SSWH.", awards = "JCR(2017): 2.214 Position: 2/14 (Q1) Category: ENGINEERING, MARINE", comments = "JCR(2017): 2.214 Position: 2/14 (Q1) Category: ENGINEERING, MARINE", doi = "10.1016/j.oceaneng.2017.07.009", issn = "0029-8018", journal = "Ocean Engineering", keywords = "Time series segmentation, Multiobjective evolutionary algorithm, Local search, Prediction, Detection, Extreme significant wave height, Minimum sensitivity", month = "September", note = "JCR(2017): 2.214 Position: 2/14 (Q1) Category: ENGINEERING, MARINE", pages = "268-279", title = "{D}etection and prediction of segments containing extreme significant wave heights", url = "doi.org/10.1016/j.oceaneng.2017.07.009", volume = "142", year = "2017", } @article{162017, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and M. D. Ayll{\'o}n-Ter{\'a}n and N. Heaton and R. Ciria and J. Brice{\~n}o and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Liver transplantation is a promising and widely-accepted treatment for patients with terminal liver disease. However, transplantation is restricted by the lack of suitable donors, resulting in significant waiting list deaths. This paper proposes a novel donor-recipient allocation system that uses machine learning to predict graft survival after transplantation using a dataset comprised of donor-recipient pairs from the King's College Hospital (United Kingdom). The main novelty of the system is that it tackles the imbalanced nature of the dataset by considering semi-supervised learning, analysing its potential for obtaining more robust and equitable models in liver transplantation. We propose two different sources of unsupervised data for this specific problem (recent transplants and virtual donor-recipient pairs) and two methods for using these data during model construction (a semi-supervised algorithm and a label propagation scheme). The virtual pairs and the label propagation method are shown to alleviate the imbalanced distribution. The results of our experiments show that the use of synthetic and real unsupervised information helps to improve and stabilise the performance of the model and leads to fairer decisions with respect to the use of only supervised data. Moreover, the best model is combined with the Model for End-stage Liver Disease score (MELD), which is at the moment the most popular assignation methodology worldwide. By doing this, our decision-support system considers both the compatibility of the donor and the recipient (by our prediction system) and the recipient severity (via the MELD score), supporting then the principles of fairness and benefit. ", awards = "JCR(2017): 4.396 Position: 14/132 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2017): 4.396 Position: 14/132 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.knosys.2017.02.020", issn = "0950-7051", journal = "Knowledge-Based Systems", month = "May", note = "JCR(2017): 4.396 Position: 14/132 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "75--87", title = "{S}ynthetic semi-supervised learning in imbalanced domains: {C}onstructing a model for donor-recipient matching in liver transplantation", url = "doi.org/10.1016/j.knosys.2017.02.020", volume = "123", year = "2017", } @article{dorado2017c, author = "Manuel Dorado-Moreno and Laura Cornejo-Bueno and Pedro Antonio Guti{\'e}rrez and Luis Prieto and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Sancho Salcedo-Sanz", awards = "JCR(2017): 4.900 Position: 7/33 (Q1) Category: GREEN {\&} SUSTAINABLE SCIENCE {\&} TECHNOLOGY", comments = "JCR(2017): 4.900 Position: 7/33 (Q1) Category: GREEN {\&} SUSTAINABLE SCIENCE {\&} TECHNOLOGY", doi = "10.1016/j.renene.2017.04.016", issn = "0960-1481", journal = "Renewable Energy", month = "April", note = "JCR(2017): 4.900 Position: 7/33 (Q1) Category: GREEN {\&} SUSTAINABLE SCIENCE {\&} TECHNOLOGY", pages = "428-437", title = "{R}obust estimation of wind power ramp events with reservoir computing", url = "http://dx.doi.org/10.1016/j.renene.2017.04.016", volume = "111", year = "2017", } @article{182016, author = "Antonio Manuel Dur{\'a}n-Rosal and Manuel Dorado-Moreno and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper presents four configurations of a genetic algorithm (GA) combined with a local search (LS) method for time series segmentation with the purpose of correctly recognising extreme values. The LS method is based on likelihood maximisation of a beta distribution. The proposal is tested on three real ocean wave height time series, where extreme values are frequently found. Concretely, the time series are taken from two oceanographic buoys in the Gulf of Alaska, and another one from Puerto Rico. The results show that the different combinations of LS improve the results of the GA. Furthermore, the algorithm provides segmentations where extreme values are grouped in a well defined cluster, which allows the study of the characteristics of this type of events.", doi = "10.1007/s13748-016-0105-1", issn = "2192-6360", journal = "Progress in Artificial Intelligence", month = "March", number = "1", pages = "59-66", title = "{I}dentification of extreme wave heights with an evolutionary algorithm in combination with a likelihood-based segmentation", url = "http://dx.doi.org/10.1007/s13748-016-0105-1", volume = "6", year = "2017", } @article{VazquezDeFrancisco17, author = "M.J. V{\'a}zquez-de-Francisco and Mercedes Torres-Jim{\'e}nez and P. Caldentey-del-Pozo and O. Nekhay", awards = "JCR(2017): 0.415 Position: 311/353 (Q4) Category: ECONOMICS", comments = "JCR(2017): 0.415 Position: 311/353 (Q4) Category: ECONOMICS", issn = "1576-0162", journal = "Revista de Econom{\'i}a Mundial", keywords = "Higher Education, University Cooperation for Development, Human Development, Impact Evaluation, Mixed Methods", note = "JCR(2017): 0.415 Position: 311/353 (Q4) Category: ECONOMICS", pages = "95--116", title = "{E}valuating impacts of university cooperation for development from the voice of the south", url = "www.sem-wes.org/sites/default/files/revistas/7_VAZQUEZ.pdf", volume = "47", year = "2017", } @article{152017, author = "Manuel Dorado-Moreno and Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and R. Ciria and J. Brice{\~n}o and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Objective: Create an efficient decision-support model to assist medical experts in the process of organ allocation in liver transplantation. The mathematical model proposed here uses different sources of information to predict the probability of organ survival at different thresholds for each donor-recipient pair considered. Currently, this decision is mainly based on the Model for End-stage Liver Disease, which depends only on the severity of the recipient and obviates donor-recipient compatibility. We therefore propose to use information concerning the donor, the recipient and the surgery, with the objective of allocating the organ correctly. Methods and materials: The database consists of information concerning transplants conducted in 7 different Spanish hospitals and the King's College hospital (United Kingdom). The state of the patients is followed up for 12 months. We propose to treat the problem as an ordinal classification one, where we predict the organ survival at different thresholds: less than 15 days, between 15 and 90 days, between 90 and 365 days and more than 365 days. This discretization is intended to produce finer-grain survival information (compared with the common binary approach). However, it results in a highly imbalanced dataset in which more than 85% of cases belong to the last class. To solve this, we combine two approaches, a cost-sensitive evolutionary ordinal artificial neural network (ANN) (in which we propose to incorporate dynamic weights to make more emphasis on the worst classified classes) and an ordinal over-sampling technique (which adds virtual patterns to the minority classes and thus alleviates the imbalanced nature of the dataset). Results: The results obtained by our proposal are promising and satisfactory, considering the overall accuracy, the ordering of the classes and the sensitivity of minority classes. In this sense, both the dynamic costs and the over-sampling technique improve the base results of the considered ANN-based method. Comparing our model with other state-of-the-art techniques in ordinal classification, competitive results can also be appreciated. The results achieved with this proposal improve the ones obtained by other state-of-the-art models: we were able to correctly predict more than 70% of the transplantation results, with a geometric mean of the sensitivities of 33.34%, which is much higher than the one obtained by other models. Conclusions: The combination of the proposed cost-sensitive evolutionary algorithm together with the application of an over-sampling technique improves the predictive capability of our model in a significant way (especially for minority classes), which can help the surgeons make more informed decisions about the most appropriate recipient for an specific donor organ, in order to maximize the probability of survival after the transplantation and therefore the fairness principle.", awards = "JCR(2017): 2.879 Position: 31/132 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2017): 2.879 Position: 31/132 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.artmed.2017.02.004 ", issn = "0933-3657", journal = "Artificial Intelligence in Medicine ", note = "JCR(2017): 2.879 Position: 31/132 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "1-11", title = "{D}ynamically weighted {E}volutionary {O}rdinal {N}eural {N}etwork for solving an {I}mbalanced {L}iver {T}ransplantation {P}roblem", url = "doi.org/10.1016/j.artmed.2017.02.004", volume = "77", year = "2017", }