@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{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
",
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",
volume = "Accepted on 12th November",
year = "2024",
}


@article{CNNJaviBarbero2024,
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",
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",
title = "{CNN} {E}xplanation {M}ethods for {O}rdinal {R}egression {T}asks",
volume = "Accepted on 6th 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",
}


@conference{bagnall2024hands,
author = "Anthony Bagnall and Matthew Middlehurst and Germain Forestier and Ali Ismail-Fawaz and Antoine Guillaume and David Guijo-Rubio and Chang Wei Tan and Angus Dempster and Geoffrey I Webb",
abstract = "Time series classification and regression are rapidly evolving fields that find areas of application in all domains of machine learning and data science. This hands on tutorial will provide an accessible overview of the recent research in these fields, using code examples to introduce the process of implementing and evaluating an estimator. We will show how to easily reproduce published results and how to compare a new algorithm to state-of-the-art. Finally, we will work through real world examples from the field of Electroencephalogram (EEG) classification and regression. EEG machine learning tasks arise in medicine, brain-computer interface research and psychology. We use these problems to how to compare algorithms on problems from a single domain and how to deal with data with different characteristics, such as missing values, unequal length and high dimensionality. The latest advances in the fields of time series classification and regression are all available through the aeon toolkit, an open source, scikit-learn compatible framework for time series machine learning which we use to provide our code examples.",
booktitle = "Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
keywords = "Time series, Machine learning, Classification, Extrinsic regression",
pages = "6410--6411",
title = "{A} {H}ands-on {I}ntroduction to {T}ime {S}eries {C}lassification and {R}egression",
url = "dl.acm.org/doi/abs/10.1145/3637528.3671443",
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
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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)
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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",
}


@conference{GEMA_OP14_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 = "Background: The Gender-Equity model for liver Allocation corrected by sodium (GEMA-Na) may save a meaningful number of lives while palliating gender disparities among liver transplant (LT) candidates (PMID 36528041). We aimed to validate its performance in Spain, where waiting time for LT is reduced. Methods: Nationwide cohort study including adult candidates for elective LT from 25 centers in Spain (2014-2021). The primary outcome was mortality or delisting for sickness with right-censoring at 90 days. The GEMA-Na score was calculated according to the published formula available at: http://gema-transplant.com/. The discrimination of GEMA-Na was assessed by the Harrel’s c-statistic (Hc) and compared with MELD-Na, and MELD 3.0.",
awards = "(JCR: 5.1)",
booktitle = "ILTS Annual Congress 2024",
comments = "(JCR: 5.1)",
month = "Septiembre",
note = "(JCR: 5.1)",
pages = "1--309",
title = "{V}alidation of the {G}ender-{E}quity {M}odel for liver {A}llocation ({GEMA}) in {S}pain: a nationwide cohort study",
url = "journals.lww.com/lt/citation/2024/09001/ilts_annual_congress_2024_abstracts.1.aspx",
volume = "30",
year = "2024",
}


@conference{GEMA_FP21_2024,
author = "M. Rodr{\'i}guez-Per{\'a}lvarez and Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and A. Majumdar and G. McCaughan and R. Taylor and E.A. Tsochatzis and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "Background: Current prioritization models for liver transplantation (LT) are hampered by their linear nature, which does not fully capture the severity of patients with extreme analytical values. Methods: Cohort study including adult patients who qualified for
elective LT in the United Kingdom (2010-2020, model training and internal validation) and in two Australian institutions (1998-2020, external validation). The Gender-Equity model for Liver Allocation corrected by serum sodium (GEMA-Na) was compared with a shallow artificial neural network optimized by neuroevolution and hybridization (GEMA-AI) using the same input variables. The primary outcome was mortality or delisting for sickness within the first 90 days. Discrimination was assessed by Harrell’s c-statistic (Hc).",
awards = "(JCR: 5.1)",
booktitle = "ILTS Annual Congress 2024",
comments = "(JCR: 5.1)",
month = "Septiembre",
note = "(JCR: 5.1)",
pages = "1--309",
title = "{E}xplainable artificial neural networks improve the performance of the {G}ender-{E}quity {M}odel for liver {A}llocation ({GEMA}) to prioritize candidates for liver transplantation",
url = "journals.lww.com/lt/citation/2024/09001/ilts_annual_congress_2024_abstracts.1.aspx",
volume = "30",
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",
}


@conference{perez2024autoencoder1,
author = "Jorge P{\'e}rez-Aracil and Cosmin M Marina and Pedro Antonio Guti{\'e}rrez and David Barriopedro and Ricardo Garc{\'i}a-Herrera and Matteo Giuliani and Ronan McAdam and Enrico Scoccimarro and Eduardo Zorita and Andrea Castelletti and Sancho Salcedo-Sanz",
abstract = "The Analogue Method (AM) is a classical statistical downscaling technique applied to field reconstruction. It is widely used for prediction and attribution tasks. The method is based on the principle that two similar atmospheric states cause similar local effects. The core of the AM method is a K-nearest neighbor methodology. Thus, two different states have similarities according to the analogy criterion. The method has remained unchanged since its definition, although some attempts have been made to improve its performance. Machine learning (ML) techniques have recently been used to improve AM performance, however, it remains very similar. An ML-based hybrid approach for heatwave (HW) analysis based on the AM is presented here. It is based on a two-step procedure: in the first step, a non-supervised task is developed, where an autoencoder (AE) model is trained to reconstruct the predictor variable, i.e. the pressure field. Second, an HW event is selected, and then the AM method is applied to the latent space of the trained AE. Thus, the analogy between the fields is searched in the encoded data of the input variable, instead of on the original field. Experiments show that the meaningful features extracted by the AE lead to a better reconstruction of the target field when pressure variables are used as input. In addition, the analysis of the latent space allows for interpreting the results, since HW occurrence can be easily distinguished. Further research can be done on including multiple input variables. ",
booktitle = "Abstracts of the EGU General Assembly 2024",
month = "14th-19th April",
organization = "Viena, Austria",
pages = "EGU24-12600",
title = "{A}utoencoder-based model for improving reconstruction of heat waves using the analogue method",
url = "doi.org/10.5194/egusphere-egu24-12600",
year = "2024",
}


@conference{Multivariate-Autoencoder_2024,
author = "Cosmin M.Marina and Eugenio Lorente-Ramos and Rafael Ayll{\'o}n-Gavil{\'a}n and Pedro Antonio Guti{\'e}rrez and Jorge P{\'e}rez-Aracil and Sancho Salcedo-Sanz",
abstract = "This paper contributes with an alternative to the multivariate Analogue Method (AM) version, using a preprocessing stage carried out by an Autoencoder (AE). The proposed method (MvAE-AM) is applied to reconstruct France’s 2003, Balkans’ 2007 and Russia 2010 mega heat waves. Using divers such as geopotential height of the 500hPA (Z500), mean sea level pressure (MSL), soil moisture (SM), and potential evaporation (PEva), the AE extracts the most relevant information into a smaller univariate latent space. Then, the classic univariate AM is applied to search for similar situations in the past over the latent space, with a minimum distance to the heat wave under evaluation. We have compared the proposed method’s performance with that of a classical multivariate AM (MvAM), showing that the MvAE-AM approach outperforms the MvAM in terms of accuracy (+1.1257C), while reducing the problem’s dimensionality.",
booktitle = "Advances in Artificial Intelligence",
doi = "https://link.springer.com/chapter/10.1007/978-3-031-62799-6_23",
keywords = "Extreme climate events, heat waves, multivariate method, analogue method",
month = "Junio",
organization = "CAEPIA",
pages = "223–232",
title = "{M}ultivariate-{A}utoencoder {F}low-{A}nalogue {M}ethod for {H}eat {W}aves {R}econstruction",
url = "doi.org/10.1007/978-3-031-62799-6_23",
volume = "14640",
year = "2024",
}


@conference{Top-002_gender_equity_2024,
author = "Manuel Rodr{\'i}guez-Per{\'a}lvez and Gloria de la Rosa 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 sodium (GEMA-Na) may save a meaningful number of lives while palliating gender disparities among liver transplant (LT) candidates (PMID 36528041). We aimed to validate its performance in Spain, where waiting time for LT is reduced.",
awards = "(JCR: 26.8)",
booktitle = "Journal of Hepatology",
comments = "(JCR: 26.8)",
doi = "https://doi.org/10.1016/S0168-8278(24)01212-1",
issn = "1600-0641",
month = "Junio",
note = "(JCR: 26.8)",
pages = "S365-S366",
title = "{TOP}-002 {V}alidation of the gender-equity model for liver allocation ({GEMA}) in a nationwide cohort of liver transplant candidates in {S}pain",
url = "doi.org/10.1016/S0168-8278(24)01212-1",
volume = "80",
year = "2024",
}


@conference{OS-024_Gender_equity_2024,
author = "Manuel Rodr{\'i}guez-Per{\'a}lvez and Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Avik Majumdar and Geoff McCaughan and Rhiannon Taylor and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Emmanuel Tsochatzis",
abstract = "Current prioritization models for liver transplantation (LT) are hampered by their linear nature, which does not fully capture the severity of patients with extreme analytical values. We aimed to develop and externally validate the Gender- Equity Model for Liver Allocation built on Artificial Intelligence (GEMA-AI) to predict waiting list outcomes in candidates for LT.",
awards = "(JCR: 26.8)",
booktitle = "Journal of Hepatology",
comments = "(JCR: 26.8)",
doi = "https://doi.org/10.1016/S0168-8278(24)00465-3",
issn = "1600-0641",
month = "Junio",
note = "(JCR: 26.8)",
pages = "S23",
title = "{OS}-024 {T}he gender-equity model for liver allocation built on artificial intelligence ({GEMA}-{AI}) improves outcome predictions among liver transplant candidates",
url = "doi.org/10.1016/S0168-8278(24)00465-3",
volume = "80",
year = "2024",
}


@conference{EnergyFlux_iwinac_2024,
author = "Antonio Manuel G{\'o}mez-Orellana and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and Jorge P{\'e}rez-Aracil and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio",
abstract = "This paper addresses the problem of short-term energy flux prediction. For this purpose, we propose the use of an ordinal classification neural network model optimised using the triangular regularised categorical cross-entropy loss, termed MLP-T. This model is based on a soft labelling strategy, that replaces the crisp 0/1 labels on the loss computation with soft versions encoding the ordinal information. This soft label encoding leverages the inherent ordering between categories to reduce the cost of ordinal classification errors and improve model generalisation performance. Specifically, the soft labels for each target class are derived from triangular probability distributions. To assess the performance of MLP-T, six datasets built from buoy measurements and reanalysis data have been used. MLP-T has been compared to nominal and ordinal classification techniques in terms of four performance metrics. MLP-T achieved an outstanding performance across all datasets and performance metrics, securing the best mean results. Despite the imbalanced nature of the problem, which makes the ordinal classification task notably difficult, MLP-T achieved good results in all classes across all datasets, including the underrepresented classes. Remarkably, MLP-T was the only approach that correctly classified at least one instance of the minority class in all datasets. Furthermore, MLP-T secured the top rank in all cases, confirming its suitability for the problem addressed.",
booktitle = "Bioinspired Systems for Translational Applications: From Robotics to Social Engineering",
doi = "https://link.springer.com/chapter/10.1007/978-3-031-61137-7_26",
issn = "1611-3349",
keywords = "Energy flux, renewable energy, ordinal classification, unimodal distributions",
month = "Mayo",
organization = "IWINAC",
pages = "283–292",
title = "{E}nergy {F}lux {P}rediction {U}sing an {O}rdinal {S}oft {L}abelling {S}trategy",
url = "doi.org/10.1007/978-3-031-61137-7_26",
volume = "14675",
year = "2024",
}


@conference{Medium_wind_speed_iwinac_2024,
author = "Antonio Manuel G{\'o}mez-Orellana and V{\'i}ctor Manuel Vargas 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 = "Renewable energies, particularly wind energy, have gain significant attention due to their clean and inexhaustible nature. Despite their commendable efficiency and minimal environmental impact, wind energy faces challenges such as stochasticity and intermittence. Machine learning methods offer a promising avenue for mitigating these challenges, particularly through wind speed prediction, which is crucial for optimising wind turbine performance. One important aspect to consider, regardless of the methodology employed and the approach used to tackle the wind speed prediction problem, is the prediction horizon. Most of the works in the literature have been designed to deal with a single prediction horizon. However, in this study, we propose a multi-task learning framework capable of simultaneously handling various prediction horizons. For this purpose, Artificial Neural Networks (ANNs) are considered, specifically a multilayer perceptron. Our study focuses on medium- and long-term prediction horizons (6 h, 12 h, and 24 h ahead), using wind speed data collected over ten years from a Spanish wind farm, along with ERA5 reanalysis variables that serve as input for the wind speed prediction. The results obtained indicate that the proposed multi-task model performing the three prediction horizons simultaneously can achieve comparable performance to corresponding single-task models while offering simplicity in terms of lower complexity, which includes the number of neurons and links, as well as computational resources.",
booktitle = "Bioinspired Systems for Translational Applications: From Robotics to Social Engineering",
doi = "https://link.springer.com/chapter/10.1007/978-3-031-61137-7_27",
issn = "1611-3349",
keywords = "Wind speed, renewable energy, multitask paradigm, medium and long term prediction",
month = "Mayo",
organization = "IWINAC",
pages = "293–302",
title = "{M}edium- and {L}ong-{T}erm {W}ind {S}peed {P}rediction {U}sing the {M}ulti-task {L}earning {P}aradigm",
url = "doi.org/10.1007/978-3-031-61137-7_27",
volume = "14675",
year = "2024",
}


@conference{Data_Augmentation_caepia_2024,
author = "Marta Vega-Bayo and Antonio Manuel G{\'o}mez-Orellana and V{\'i}ctor Manuel Vargas and David Guijo-Rubio and Laura Cornejo-Bueno and Jorge P{\'e}rez-Aracil and Sancho Salcedo-Sanz",
abstract = "Predicting extreme winds (i.e. winds speed equal to or greater than 25 m/s), is essential to predict wind power and accomplish safe and efficient management of wind farms. Although feasible, predicting extreme wind with supervised classifiers and deep learning models is particularly difficult because of the low frequency of these events, which leads to highly unbalanced training datasets. To tackle this challenge, in this paper different traditional data augmentation techniques, such as random oversampling, SMOTE, time series data warping and multidimensional data warping, are used to generate synthetic samples of extreme wind and its predictors, such as previous samples of wind speed and meteorological variables of the surroundings. Results show that using data augmentation techniques with the right oversampling ratio leads to improvement in extreme wind prediction with most machine learning and deep learning models tested. In this paper, advanced data augmentation techniques, such as Variational Autoencoders (VAE), are also applied and evaluated when inputs are time series.",
booktitle = "Bioinspired Systems for Translational Applications: From Robotics to Social Engineering",
doi = "https://link.springer.com/chapter/10.1007/978-3-031-61137-7_28",
issn = "1611-3349",
keywords = "Extreme wind speed classification, data augmentation, machine learning, deep learning",
month = "Mayo",
organization = "IWINAC",
pages = "303–313",
title = "{D}ata {A}ugmentation {T}echniques for {E}xtreme {W}ind {P}rediction {I}mprovement",
url = "doi.org/10.1007/978-3-031-61137-7_28",
volume = "14675",
year = "2024",
}


@conference{O-Hydra_caepia_2024,
author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "Time Series Ordinal Classification (TSOC) is a yet unexplored field with a substantial projection in following years given its applicability to numerous real-world problems and the possibility to obtain more consistent prediction than nominal Time Series Classification (TSC). Specifically, TSOC involves time series data along with an ordinal categorical output. That is, there is a natural order relationship among the labels associated with the time series. TSOC is a subfield of nominal TSC, with the main distinction being that TSOC exploits the ordinality of the labels to boost the performance. Two categories within the TSC taxonomy are dictionary-based and convolution-based methodologies, each representing competing approaches presented in the literature. In this study, we adapt the Hybrid Dictionary-Rocket Architecture (Hydra) approach, which incorporates elements from the two previous categories, to TSOC, resulting in O-Hydra. For the experiments, we have included a collection of 21 ordinal problems sourced from two well-known archives. O-Hydra has been benchmarked against its nominal counterpart, Hydra, as well as against two state-of-the-art approaches in the two previous categories, TDE and ROCKET, including their ordinal counterparts, O-TDE and O-ROCKET, respectively. The results achieved by the ordinal versions significantly outperformed those of current nominal TSC techniques. This underscores the significance of incorporating the label ordering when addressing such problems.",
awards = "3er premio en congreso CAEPIA-TAMIDA",
booktitle = "Advances in Artificial Intelligence ",
doi = "https://link.springer.com/chapter/10.1007/978-3-031-62799-6_6",
issn = "1611-3349",
keywords = "Time series classification, dictionary based, convolution based, ordinal classification",
month = "Junio",
organization = "CAEPIA",
pages = "50–60",
title = "{O}-{H}ydra: {A} {H}ybrid {C}onvolutional and {D}ictionary-{B}ased {A}pproach to {T}ime {S}eries {O}rdinal {C}lassification",
url = "doi.org/10.1007/978-3-031-62799-6_6",
volume = "14640",
year = "2024",
}


@conference{AgeEstimation_caepia_2024,
author = "V{\'i}ctor Manuel Vargas and Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Francisco B{\'e}rchez-Moreno and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "This work explores the use of diverse soft labelling approaches recently proposed in the literature to address four distinct problems in age estimation. This kind of challenge can be considered an ordinal classification problem in machine learning or deep learning areas, as it exhibits a natural order among categories, reflecting the underlying age ranges defining each category. Soft labelling represents a machine learning approach in which, instead of assigning a single label to each instance in the dataset, a probability distribution across a range of labels is allocated. Soft labelling approaches prove particularly effective for age estimation due to the inherent uncertainty and continuity in age progression, which makes accurate age estimation from physical appearance difficult. Unlike categorical labels, age is a continuous variable that evolves over time. Thus, unlike hard labelling, soft labelling more effectively acknowledges the continuity and uncertainty inherent in age estimation. The experiments conducted in this study facilitate the comparison of soft labelling approaches against the nominal baseline. Results demonstrate superior performance of soft labelling approaches. Moreover, the statistical analysis reveals that use of a beta distribution to define soft labels yields the best results.",
awards = "1er premio en congreso CAEPIA-TAMIDA",
booktitle = "Advances in Artificial Intelligence ",
doi = "https://link.springer.com/chapter/10.1007/978-3-031-62799-6_5",
issn = "1611-3349",
keywords = "Age estimation, soft labelling, ordinal classification",
month = "Junio",
organization = "CAEPIA",
pages = "40--49",
title = "{A}ge {E}stimation {U}sing {S}oft {L}abelling {O}rdinal {C}lassification {A}pproaches",
url = "doi.org/10.1007/978-3-031-62799-6_5",
volume = "14640",
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",
}


@inbook{Capitulo-TimeSeriesML,
author = "Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio",
editor = "CRC Press",
isbn = "9781003104858",
pages = "161-176",
title = "{M}achine {L}earning {A}pplications in {R}eal-{W}orld {T}ime {S}eries {P}roblems ",
url = "https://www.taylorfrancis.com/chapters/edit/10.1201/9781003104858-10/machine-learning-applications-real-world-time-series-problems-antonio-manuel-dur%C3%A1n-rosal-david-guijo-rubio",
volume = "1",
year = "2023",
}


@conference{BarycentreAveragingTimeSeries_2023,
author = "Christopher Holder and David Guijo-Rubio and Anthony Bagnall",
abstract = "Distance functions play a core role in many time series machine learning algorithms for tasks such as clustering, classification and regression. Time series often require bespoke distance functions because small offsets in time can lead to large distances between series that are conceptually similar. Elastic distances compensate for misalignment by creating a path through a cost matrix by warping and/or editing time series. Time series are most commonly clustered with partitional algorithms such as k-means and k-medoids using elastic distance measures such as Dynamic Time Warping (DTW). The distance is used to assign cases to the closest cluster representative. k-means requires the averaging of time series to find these representative centroids. If DTW is used to assign membership, but the arithmetic mean is used to find centroids, k-means performance degrades significantly. An averaging technique specific to DTW, called DTW Barycentre Averaging (DBA), overcomes the averaging problem however, can only be used with DTW. As such alternative distance functions such as Move-Split-Merge (MSM) are forced to use the arithmetic mean to compute new centroids and suffer similar degraded performance as k-means-DTW without DBA. To address this we propose a averaging method for MSM distance, MSM Barycentre Averaging (MBA) and show that when used to find centroids it significantly improves MSM based k-means and is better than commonly used alternatives.",
booktitle = "15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
doi = "https://www.scitepress.org/PublicationsDetail.aspx?ID=h27zf/TeD4k={\&}t=1",
issn = "2184-3228",
keywords = "Time Series Distances, Time Series Clustering, Move Split Merge, Barycentre Averaging, Dynamic Barycentre Averaging, MSM Barycentre Averaging, DBA, MBA",
organization = "Rome, Italy",
pages = "51--62",
title = " {B}arycentre {A}veraging for the {M}ove-{S}plit-{M}erge {T}ime {S}eries {D}istance {M}easure ",
url = "www.scitepress.org/PublicationsDetail.aspx?ID=h27zf/TeD4k={\&}t=1",
volume = "1",
year = "2023",
}


@conference{GEMA-congress-2023,
author = "Manuel Luis Rodr{\'i}guez Per{\'a}lvarez and Antonio Manuel G{\'o}mez-Orellana and Avik Majumdar and Mar{\'i}a Dolores Ayll{\'o}n and Pedro Antonio Guti{\'e}rrez and Pilar Barrera Baena and David Guijo-Rubio and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Manuel de la Mata and Emmanuel Tsochatzis",
abstract = "Los modelos de priorizaci{\'o}n en lista de espera de trasplante hep{\'a}tico (TH) han sido entrenados para predecir mortalidad en lista a 90 d{\'i}as. Sin embargo, muchos centros tienen listas de espera m{\'a}s cortas, lo cual podr{\'i}a cuestionar su utilidad.",
booktitle = "49.º Congreso Anual de la Asociaci{\'o}n Espa{\~n}ola para el Estudio del H{\'i}gado",
doi = "https://www.elsevier.es/es-revista-gastroenterologia-hepatologia-14-sumario-vol-46-num-s2-X0210570523X00S20?local=true",
title = "{UTILIDAD} {DEL} {GENDER}-{EQUITY} {MODEL} {FOR} {LIVER} {ALLOCATION} ({GEMA}) {EN} {UN} {CONTEXTO} {DE} {ACORTAMIENTO} {DE} {LA} {LISTA} {DE} {ESPERA} {DE} {TRASPLANTE} {HEP}{\'{A}}{TICO}",
url = "www.elsevier.es/es-revista-gastroenterologia-hepatologia-14-sumario-vol-46-num-s2-X0210570523X00S20?local=true",
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",
}


@conference{R. Calleja2023,
author = "R. Calleja and Marcos Rivera and Amelia J. Hessheimer and Beatriz Dom{\'i}nguez-Gil and David Guijo-Rubio and Constantino Fontdevila and Mikel Gastaca-Mateo and Manuel C{\'o}mez and Pablo Ram{\'i}rez-Romero and Rafael L{\'o}pez-Andujar and L{\'a}nder Atutxa and Julio Santoyo and Miguel A. G{\'o}mez-Bravo and Jes{\'u}s M. Villar-del-Moral and Carolina Gonz{\'a}lez-Abos and B{\'a}rbara Vidal and Laura Llad{\'o} and Jos{\'e} Rold{\'a}n and Carlos Jim{\'e}nez-Romero and V{\'i}ctor S{\'a}nchez-Turri{\'o}n and Gonzalo Rodr{\'i}guez-Laiz and Jos{\'e} A. L{\'o}pez Baena and Ram{\'o}n Charco-Torra and Evaristo Varo and Fernando Rotellar and Manuel Barrera and Juan C. Rodr{\'i}guez and Gerardo Blanco-Fern{\'a}ndez and Javier Nu{\~n}o and David Pacheco S{\'a}nchez and Elisabeth Coll and Gloria de la Rosa and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Javier Brice{\~n}o",
booktitle = "29o Congreso de la Sociedad Espa{\~n}ola de Trasplante Hep{\'a}tico (SETH)",
month = "Noviembre",
pages = "9",
title = "{E}mparejamiento donante-receptor durante la donaci{\'o}n en asistolia controlada con perfusi{\'o}n regional normot{\'e}rmica: papel de los clasificadores de machine learning como modelos predictivos de la supervivencia del injerto",
url = "https://sethepatico.org/seth2023/presentaciones/index.htm",
year = "2023",
}


@conference{262023,
author = "Antonio Manuel G{\'o}mez-Orellana and Manuel L. Rodr{\'i}guez-Per{\'a}lvarez and David Guijo-Rubio and Marta Guerrero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
booktitle = "Ciencia Violeta. I Encuentro Cient{\'i}fico sobre Investigaci{\'o}n con Perspectiva de G{\'e}nero",
month = "Febrero",
pages = "1-5",
title = "{C}orrecci{\'o}n de la disparidad de g{\'e}nero en el acceso al trasplante hep{\'a}tico",
year = "2023",
}


@conference{GEMA-congress_2023,
author = "Manuel L. Rodr{\'i}guez Per{\'a}lvarez and Gloria de la Rosa and Antonio Manuel G{\'o}mez-Orellana and Mª Victoria Aguilera and Teresa Pascual Vicente and Sheila Pereira and Mar{\'i}a Luisa Ortiz and Giulia Pagano and Francisco Suarez and Roc{\'i}o Gonz{\'a}lez Grande and Alba Cachero and Santiago Tom{\'e} and M{\'o}nica Barreales and Rosa Mart{\'i}n Mateos and Sonia Pascual and Mario Romero and Itxarone Bilbao and Carmen Alonson Mart{\'i}n and Elena Ot{\'o}n and Luisa Gonz{\'a}lez Di{\'e}guez and Mar{\'i}a Dolores Espinosa and Ana Arias Milla and Gerardo Blanco Fern{\'a}ndez and Sara Lorente and Antonio Cuadrado Lav{\'i}n and Amaya Red{\'i}n Garc{\'i}a and Clara S{\'a}nchez Cano and Carmen Cepeda and Jos{\'e} Antonio Pons and Jordi Colmenero and Alejandra Otero and Nerea Hern{\'a}ndez Aretxabaleta and Sarai Romero Moreno and Mar{\'i}a Rodr{\'i}guez Soler and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Mikel Gastaca",
booktitle = "48.º Congreso Anual de la Asociaci{\'o}n Espa{\~n}ola para el Estudio del H{\'i}gado",
month = "Marzo",
number = "S2",
organization = "Asociaci{\'o}n Espa{\~n}ola para el Estudio del H{\'i}gado",
pages = "128--129",
title = "{UTILIDAD} {DEL} {GENDER}-{EQUITY} {MODEL} {FOR} {LIVER} {ALLOCATION} ({GEMA}) {EN} {UN} {CONTEXTO} {DE} {ACORTAMIENTO} {DE} {LA} {LISTA} {DE} {ESPERA} {DE} {TRASPLANTE} {HEP}{\'{A}}{TICO}",
url = "elsevier.es/es-revista-gastroenterologia-hepatologia-14-sumario-vol-46-num-s2-X0210570523X00S20?local=true",
volume = "46",
year = "2023",
}


@conference{ClusteringTimeSeries_kMedoids_2023,
author = "Christopher Holder and David Guijo-Rubio and Anthony Bagnall",
abstract = "Time Series Clustering (TSCL) involves grouping unlabelled time series into homogeneous groups. A popular approach to TSCL is to use the partitional clustering algorithms k-means or k-medoids in conjunction with an elastic distance function such as Dynamic Time Warping (DTW). We explore TSCL using nine different elastic distance measures. Both partitional algorithms characterise clusters with an exemplar series, but use different techniques to do so: k-means uses an averaging algorithm to find an exemplar, whereas k-medoids chooses a training case (medoid). Traditionally, the arithmetic mean of a collection of time series was used with k-means. However, this ignores any offset. In 2011, an averaging technique specific to DTW, called DTW Barycentre Averaging (DBA), was proposed. Since, k-means with DBA has been the algorithm of choice for the majority of partition-based TSCL and much of the research using medoids-based approaches for TSCL stopped. We revisit k-medoids based TSCL with a range of elastic distance measures. Our results show k-medoids approaches are significantly better than k-means on a standard test suite, independent of the elastic distance measure used. We also compare the most commonly used alternating k-medoids approach against the Partition Around Medoids (PAM) algorithm. PAM significantly outperforms the default k-medoids for all nine elastic measures used. Additionally, we evaluate six variants of PAM designed to speed up TSCL. Finally, we show PAM with the best elastic distance measure is significantly better than popular alternative TSCL algorithms, including the k-means DBA approach, and competitive with the best deep learning algorithms.",
booktitle = "Advanced Analytics and Learning on Temporal Data",
doi = "10.1007/978-3-031-49896-1_4",
keywords = "Time Series, Clustering, k-means, k-medoids, PAM, UCR archive",
month = "Diciembre",
organization = "AALTD",
pages = "39--55",
title = "{C}lustering {T}ime {S}eries with k-{M}edoids {B}ased {A}lgorithms",
url = "link.springer.com/chapter/10.1007/978-3-031-49896-1_4",
volume = "14343",
year = "2023",
}


@conference{GEMA-Na-Congress-2023,
author = "M. Rodr{\'i}guez-Per{\'a}lvarez and Antonio Manuel G{\'o}mez-Orellana and A. Majumdar and M. Bailey and G. McCaughan and P. Gow and M. Guerrero and R. Taylor and David Guijo-Rubio and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and E. Tsochatzis",
abstract = "Models for liver transplant (LT) allocation have been trained to predict mortality or delisting for sickness at 90 days. Their performance in a context of waiting list shortening is uncertain.",
awards = "(JCR: 5.5)",
booktitle = "ILTS 2023 Joint International Congress of ILTS, ELITA and LICAGE, May 3-6, 2023.",
comments = "(JCR: 5.5)",
doi = "https://doi.org/10.1097/01.tp.0000978836.44371.fe",
month = "Septiembre",
note = "(JCR: 5.5)",
number = "107",
organization = "Transplantation",
pages = "195",
title = "{P}erformance of the gender-equity model for liver allocation ({GEMA}-{N}a) within the first 30 and 60 days of listing",
url = "doi.org/10.1097/01.tp.0000978836.44371.fe",
year = "2023",
}


@conference{Dictionary-Based_TimeSeries_2023,
author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-called dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the current state-of-the-art dictionary-based TSC approach. In many TSC problems we find a natural ordering in the labels associated with the time series. This characteristic is referred to as ordinality, and can be exploited to improve the methods performance. The area dealing with ordinal time series is the Time Series Ordinal Classification (TSOC) field, which is yet unexplored. In this work, we present an ordinal adaptation of the TDE algorithm, known as ordinal TDE (O-TDE). For this, a comprehensive comparison using a set of 18 TSOC problems is performed. Experiments conducted show the improvement achieved by the ordinal dictionary-based approach in comparison to four other existing nominal dictionary-based techniques.",
booktitle = "IWANN 2023: Advances in Computational Intelligence",
doi = "10.1007/978-3-031-43078-7_44",
keywords = "Time Series, Dictionary-Based Approaches, Ordinal Classification",
month = "19th-21th June, 2023",
organization = "Ponta Delgada, Portugal",
pages = "541--552",
publisher = "Springer",
series = "Lecture Notes in Computer Science (LNCS)",
title = "{A} {D}ictionary-{B}ased {A}pproach to {T}ime {S}eries {O}rdinal {C}lassification",
url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_44",
volume = "14135",
year = "2023",
}


@conference{EvaluatingPerformance_ExplanationMethods_2023,
author = "Javier Barbero-G{\'o}mez and Ricardo Cruz and Jaime S. Cardoso and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "This paper introduces an evaluation procedure to validate the efficacy of explanation methods for Convolutional Neural Network (CNN) models in ordinal regression tasks. Two ordinal methods are contrasted against a baseline using cross-entropy, across four datasets. A statistical analysis demonstrates that attribution methods, such as Grad-CAM and IBA, perform significantly better when used with ordinal regression CNN models compared to a baseline approach in most ordinal and nominal metrics. The study suggests that incorporating ordinal information into the attribution map construction process may improve the explanations further.",
booktitle = "IWANN 2023: Advances in Computational Intelligence",
doi = "10.1007/978-3-031-43078-7_43",
keywords = "Convolutional Neural Networks, Interpretability, Ordinal Regression",
month = "19th-21th June, 2023",
organization = "Ponta Delgada, Portugal",
pages = "529--540",
publisher = "Springer",
series = "Lecture Notes in Computer Science (LNCS)",
title = "{E}valuating the {P}erformance of {E}xplanation {M}ethods on {O}rdinal {R}egression {CNN} {M}odels",
url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_43",
volume = "14135",
year = "2023",
}


@conference{OrdinalClassification_DR_2023,
author = "M. Rivera-Gavil{\'a}n and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and J. Brice{\~n}o and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio",
abstract = "This paper tackles the Donor-Recipient (D-R) matching for Liver Transplantation (LT). Typically, D-R matching is performed following the knowledge of a team of experts guided by the use of a prioritisation system. One of the most extended, the Model for End-stage Liver Disease (MELD), aims to decrease the mortality in the waiting list. However, it does not take into account the result of the transplant. In this sense, with the aim of developing a system able to bear in mind the survival benefit, we propose to treat the problem as an ordinal classification one. The organ survival will be predicted at four different thresholds. The results achieved demonstrate that ordinal classifiers are capable of outperforming nominal approaches in the state-of-the-art. Finally, this methodology can help experts to make more informed decisions about the appropriateness of assigning a recipient for a specific donor, maximising the probability of post-transplant survival in LT.",
booktitle = "IWANN 2023: Advances in Computational Intelligence",
doi = "10.1007/978-3-031-43078-7_42",
keywords = "Donor-Recipient Matching, Liver Transplantation, Ordinal Classification, Ordinal Binary Decomposition",
month = "19th-21th June, 2023",
organization = "Ponta Delgada, Portugal",
pages = "517–528",
publisher = "Springer",
series = "Lecture Notes in Computer Science (LNCS)",
title = "{O}rdinal classification approach for donor-recipient matching in liver transplantation with circulatory death donors",
url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_42",
volume = "14135",
year = "2023",
}


@conference{GramarianAngular_iwan_2023,
author = "V{\'i}ctor Manuel Vargas and Rafael Ayll{\'o}n-Gavil{\'a}n and Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio",
abstract = "This work presents a novel ordinal Deep Learning (DL) approach to Time Series Ordinal Classification (TSOC) field. TSOC consists in classifying time series with labels showing a natural order between them. This particular property of the output variable should be exploited to boost the performance for a given problem. This paper presents a novel DL approach in which time series are encoded as 3-channels images using Gramian Angular Field and Markov Transition Field. A soft labelling approach, which considers the probabilities generated by a unimodal distribution for obtaining soft labels that replace crisp labels in the loss function, is applied to a ResNet18 model. Specifically, beta and triangular distributions have been applied. They have been compared against three state-of-the-art deep learners in the Time Series Classification (TSC) field using 13 univariate and multivariate time series datasets. The approach considering the triangular distribution (O-GAMTFT) outperforms all the techniques benchmarked.",
booktitle = "IWANN 2023: Advances in Computational Intelligence",
doi = "10.1007/978-3-031-43078-7_41",
keywords = "Gramarian Angular Fields, Markov Transition Fields, Time Series Ordinal Classification, Soft Labelling",
month = "19th-21th June, 2023",
organization = "Ponta Delgada, Portugal",
pages = "505–-516",
publisher = "Springer",
series = "Lecture Notes in Computer Science (LNCS)",
title = "{G}ramian {A}ngular and {M}arkov {T}ransition {F}ields applied to {T}ime {S}eries {O}rdinal {C}lassification",
url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_41",
volume = "14135",
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",
}


@conference{VictorVargasATM-2022,
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 Lorenzo Bianchini and Alessandra Capriotti and Rosario Capparuccia and Emanuele Frontoni",
abstract = "One of the main relevant topics of Industry 4.0 is related to the prediction of Remaining Useful Life (RUL) of machines. In this context, the Smart Manufacturing Machine with Predictive Lifetime Electronic maintenance (SIMPLE) project aims to promote collaborations among different companies in the scenario of predictive maintenance. One of the topics of the SIMPLE project is related to the prediction of RUL of automated teller machines (ATMs). This represents a key task as these machines are subject to different types of failure. However the main challenges in this field lie in: i) collecting a representative dataset, ii) correctly annotating the observations and iii) handling the imbalanced nature of the dataset. To overcome this problem, in this work we present a feature extraction strategy and a machine learning (ML) based solution for solving RUL estimation for ATM devices. We prove the effectiveness of our approach with respect to other state-of-the-art ML approaches widely employed for solving the RUL task. In addition, we propose the design of a predictive maintenance platform to integrate our ML model for the SIMPLE project.",
booktitle = "Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)",
doi = "10.1007/978-3-031-18050-7_23",
isbn = "978-3-031-18049-1",
month = "September",
organization = "Salamanca, Spain",
pages = "239--249",
series = "Lecture Notes in Networks and Systems",
title = "{P}redictive {M}aintenance of {ATM} machines by modelling {R}emaining {U}seful {L}ife with {M}achine {L}earning techniques",
url = "doi.org/10.1007/978-3-031-18050-7_23",
volume = "531",
year = "2022",
}


@conference{AyllonGavilan2022,
author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "This work analyzes the performance of several state-of-the-art Time Series Classification (TSC) techniques in the cryptocurrency returns modeling field. The data used in this study comprehends the close price of $6$ of the principal cryptocurrencies, collected with a frequency of $5$ minutes from January $1$st to September $21$th of $2021$. The aim of this work is twofold: 1) to study the weak form of the Efficient Market Hypothesis (EMH) and 2) to examine the veracity behind the theory of the Random Walk Model (RWM). For this, two datasets are built. The first uses autoregressive values, whereas the second dataset is constructed by introducing randomized past values from the time series. Then, a comparison of the performances achieved by the different TSC techniques is carried out. Results obtained show a pronounced difference in terms of performance obtained by all the TSC models when applied to the original dataset against the randomized one. The results achieved by the models applied to the original dataset are significantly better in terms of Area Under ROC Curve (AUC) and Recall. Therefore, the EMH is refused in its weak form, and indisputable evidence against the RWM in a high-frequency scope is provided.",
booktitle = "Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)",
doi = "10.1007/978-3-031-18050-7_14",
isbn = "978-3-031-18049-1",
month = "September",
organization = "Salamanca, Spain",
pages = "146--155",
series = "Lecture Notes in Networks and Systems",
title = "{A}ssessing the {E}fficient {M}arket {H}ypothesis for {C}ryptocurrencies with {H}igh-{F}requency {D}ata using {T}ime {S}eries {C}lassification",
url = "doi.org/10.1007/978-3-031-18050-7_14",
volume = "531",
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",
}


@conference{Tacrolimus_Liver_Transplantation_2022,
author = "Manuel Rodr{\'i}guez-Per{\'a}lvez and Gonzalo Crespo and Jes{\'u}s Rivera and Antonio Gonz{\'a}lez Rodr{\'i}guez and Estefan{\'i}a Berge Garrido and Mikel Gastaca and Patricia Ruiz and Anna Curell and Cristina Dopazo and Ainhoa Fern{\'a}ndez-Yunquera and Fernando Diaz and Ana S{\'a}nchez Mart{\'i}nez and Mar{\'i}a Luisa Ortiz and Marina Berenguer and Tommaso Di Maira and Jose Ignacio Herrero and Mercedes I{\~n}arrairaegui and Carolina Almohalla and Esteban Fuentes Valenzuela and Sara Lorente Perez and Cristina Borao and Fernando Casafont and Emilio Fabrega and Sonia Pascual and Patricio M{\'a}s-Serrano and Maria Angeles Lopez Garrido and Flor Nogueras L{\'o}pez and Rocio Gonz{\'a}lez-Grande and Javier Zamora and Rafael Alejandre and Antonio Manuel G{\'o}mez-Orellana and Carmen Bernal and Miguel {\'A}ngel G{\'o}mez Bravo",
awards = "JCR(2022): 25.7, Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.",
booktitle = "Abstracts of The International Liver CongressTM 2022",
comments = "JCR(2022): 25.7, Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.",
doi = "10.1016/S0168-8278(22)00614-6",
editor = "Journal of Hepatology",
issn = "0168-8278",
month = "June",
note = "JCR(2022): 25.7, Position: 4/93 (Q1) Category: GASTROENTEROLOGY {\&} HEPATOLOGY.",
organization = "London, United Kingdom",
pages = "114",
title = "{C}umulative exposure to tacrolimus and incidence of cancer after liver transplantation",
url = "https://www.sciencedirect.com/science/article/pii/S0168827822006146",
volume = "77",
year = "2022",
}


@conference{GEMA_Liver_transplant_2022,
author = "Manuel Rodr{\'i}guez-Per{\'a}lvarez and Antonio Manuel G{\'o}mez-Orellana and Avik Majumdar and Geoff McCaughan and Paul Gow and David Guijo-Rubio and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Michael Bailey and Emmanuel 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 (LT). We derived and validated a new model that replaced creatinine with the Royal Free glomerular filtration rate (PMID: 27779785) within the MELD and MELD-Na formulas",
awards = "(JCR: 26.8)",
booktitle = "Abstracts of The International Liver CongressTM 2022",
comments = "(JCR: 26.8)",
doi = "10.1016/S0168-8278(22)00432-9",
editor = "Journal of Hepatology",
issn = "0168-8278",
month = "June",
note = "(JCR: 26.8)",
organization = "London, United Kingdom",
pages = "3-4",
title = "{D}evelopment and validation of the gender-equity model for liver allocation ({GEMA}) to prioritize liver transplant candidates",
url = "www.sciencedirect.com/science/article/pii/S0168827822004329",
volume = "77",
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",
}


@conference{COVID19-MDiaz2022,
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 = "In this paper, an approach based on a time series clustering technique is presented by extracting relevant features from the original temporal data. A curve characterization is applied to the daily contagion rates of the 34 sanitary districts of Andalusia, Spain. By determining the maximum incidence instant and two inflection points for each wave, an outbreak curve can be described by six intensity features, defining its initial and final phases. These features are used to derive different groups using state-of-the-art clustering techniques. The experimentation carried out indicates that {\$}{\$}k=3{\$}{\$}k=3is the optimum number of descriptive groups of intensities. According to the resulting clusters for each wave, the pandemic behavior in Andalusia can be visualised over time, showing the most affected districts in the pandemic period considered. Additionally, in order to perform a pandemic overview of the whole period, the approach is also applied to joint information of all the considered periods",
booktitle = "Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence (Proceedings of the 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022)",
doi = "10.1007/978-3-031-06527-9_46",
isbn = "978-3-031-06527-9",
keywords = "COVID-19 contagions, clustering, curve characterization",
month = "May",
number = "Part II",
organization = "Tenerife, Spain",
pages = "462--471",
publisher = "Springer",
series = "Lecture Notes in Computer Science (LNCS)",
title = "{C}lustering of {COVID}-19 {T}ime {S}eries {I}ncidence {I}ntensity in {A}ndalusia, {S}pain",
url = "doi.org/10.1007/978-3-031-06527-9_46",
volume = "13259",
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",
}


@conference{222022,
author = "David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Javier Barbero-G{\'o}mez and Jose V. Die and Pablo Gonz{\'a}lez-Moreno",
abstract = "Programming has traditionally been an engineering competence, but recently it is acquiring significant importance in several areas, such as Life Sciences, where it is considered to be essential for problem solving based on data analysis. Therefore, students in these areas need to improve their programming skills related to the data analysis process. Similarly, engineering students with proven technical ability may lack the biological background which is likewise fundamental for problem-solving. Using hackathon and teamwork-based tools, students from both disciplines were challenged with a series of problems in the area of Life Sciences. To solve these problems, we established work teams that were trained before the beginning of the competition. Their results were assessed in relation to their approach in obtaining the data, performing the analysis and finally interpreting and presenting the results to solve the challenges. The project succeeded, meaning students solved the proposed problems and achieved the goals of the activity. This would have been difficult to address with teams made from the same field of study. The hackathon succeeded in generating a shared learning and a multidisciplinary experience for their professional training, being highly rewarding for both students and faculty members.",
booktitle = "Proceedings of the 13th International Conference on European Transnational Education (ICEUTE 2022)",
doi = "10.1007/978-3-031-18409-3_23",
month = "5th - 7th September",
pages = "236--246",
publisher = "Springer",
series = "Lecture Notes in Networks and Systems",
title = "{H}ackathon in teaching: applying machine learning to {L}ife {S}ciences tasks",
url = "doi.org/10.1007/978-3-031-18409-3_23",
volume = "532",
year = "2022",
}


@conference{182022,
author = "Antonio Manuel Dur{\'a}n-Rosal and David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and Juan Carlos Fern{\'a}ndez",
abstract = "Machine learning (ML) is the field of science that combines knowledge from artificial intelligence, statistics and mathematics intending to give computers the ability to learn from data without being explicitly programmed to do so. It falls under the umbrella of Data Science and is usually developed by Computer Engineers becoming what is known as Data Scientists. Developing the necessary competences in this field is not a trivial task, and applying innovative methodologies such as gamification can smooth the initial learning curve. In this context, communities offering platforms for open competitions such as Kaggle can be used as a motivating element. The main objective of this work is to gamify the classroom with the idea of providing students with valuable hands-on experience by means of addressing a real problem, as well as the possibility to cooperate and compete simultaneously to acquire ML competences. The innovative teaching experience carried out during two years meant a great motivation, an improvement of the learning capacity and a continuous recycling of knowledge to which Computer Engineers are faced to.",
booktitle = "Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022), 13th International Conference on EUropean Transnational Education (ICEUTE 2022)",
doi = "10.1007/978-3-031-18409-3_22",
isbn = "978-3-031-18409-3",
month = "5th - 7th September ",
organization = "Universidad de Salamanca (Salamanca, Espa{\~n}a)",
pages = "224-235",
publisher = "Springer",
series = "Lecture Notes in Networks and Systems",
title = "{G}amifying the classroom for the acquisition of skills associated with {M}achine {L}earning: a two-year case study",
url = "doi.org/10.1007/978-3-031-18409-3_22",
volume = "532",
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",
}


@phdthesis{DGRThesis,
author = "David Guijo-Rubio",
address = "P.A. Guti{\'e}rrez, C. Herv{\'a}s-Mart{\'i}nez",
awards = "Menci{\'o}n de calidad
Menci{\'o}n internacional",
comments = "Menci{\'o}n de calidad
Menci{\'o}n internacional
Compendio de art{\'i}culos",
institution = "Universidad de C{\'o}rdoba",
month = "June",
note = "Sobresaliente - CUM LAUDE",
title = "{C}lustering, prediction and ordinal classification of time series using machine learning techniques: applications",
url = "https://helvia.uco.es/xmlui/handle/10396/21445",
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",
}


@conference{BAmiri2021,
author = "B. Amiri and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and K. Dahmani and R. Diz{\`e}ne",
booktitle = "The First International Conference on Renewable Energy Advanced Technologies and Applications (ICREATA'21)",
isbn = "978-9931-9819-0-9",
keywords = "Algerian Sahara, Artificial Neural Networks, Estimation, Solar irradiation, Tilted plane",
month = "25th-27th October",
organization = "Research Unit for Renewable Energies in Saharan Region, Adrar",
pages = "116-117",
title = "{M}ultilayer {P}erceptron and {C}ascade {F}orward {N}eural {N}etwork for {S}hort-term {T}ilted {I}rradiation {E}stimation in {A}lgerian {S}ahara",
year = "2021",
}


@conference{BarberoECOCOrdinalIWANN,
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 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, such as adapting the classic Proportional Odds Model to deep architectures. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. In this work, we present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC) and show how it can improve performance over previously proposed methods.",
booktitle = "2021 International Work-conference on Artificial Neural Networks (IWANN 2021)",
doi = "10.1007/978-3-030-85099-9_1",
isbn = "978-3-030-85029-6",
issn = "0302-9743",
keywords = "Artificial Neural Networks, Ordinal Classification",
month = "16nd-18th June",
number = "Part II",
organization = "Online",
pages = "3-13",
publisher = "Springer",
series = " Lecture Notes in Computer Science (LNCS)",
title = "{E}rror-correcting output codes in the framework of deep ordinal classification",
url = "doi.org/10.1007/978-3-030-85099-9_1",
volume = "12862",
year = "2021",
}


@conference{GuijoCAEPIA2021,
author = "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",
abstract = "Time Series Ordinal Classification (TSOC) is yet an unexplored field of machine learning consisting in the classification of time series whose labels follow a natural order relationship between them. In this context, a well-known approach for time series nominal classification was previously used: the Shapelet Transform (ST). The exploitation of the ordinal information was included in two steps of the ST algorithm: 1) by using the Pearson's determination coefficient (R²) for computing the quality of the shapelets, which favours shapelets with better ordering, and 2) by applying an ordinal classifier instead of a nominal one to the transformed dataset. For this, the distance between labels was represented by the absolute value of the difference between the corresponding ranks, i.e. by the L1 norm. In this paper, we study the behaviour of different Lp norms for representing class distances in ordinal regression, evaluating 9 different Lp norms with 7 ordinal time series datasets from the UEA-UCR time series classification repository and 10 different ordinal classifiers. The results achieved demonstrate that the Pearson's determination coefficient using the L1.9 norm in the computation of the difference between the shapelet and the time series labels achieves a significantly better performance when compared to the rest of the approaches, in terms of both Correct Classification Rate (CCR) and Average Mean Absolute Error (AMAE). ",
booktitle = "Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA)",
doi = "10.1007/978-3-030-85713-4_5",
isbn = "978-3-030-85712-7",
issn = "0302-9743",
keywords = "Lp norms, TSOC, time series, L2, L1, ordinal classification, ",
month = "22nd-24th September",
organization = "Malaga, Spain",
pages = "44-53",
publisher = "Springer",
series = " Lecture Notes in Artificial Intelligence (LNAI)",
title = "{S}tudying the effect of different {L}p norms in the context of {T}ime {S}eries {O}rdinal {C}lassification",
url = "doi.org/10.1007/978-3-030-85713-4_5",
volume = "12882",
year = "2021",
}


@conference{ReluVictorCAEPIA2021,
author = "V{\'i}ctor Manuel Vargas and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "Activation functions are used in neural networks as a tool to introduce non-linear transformations into the model and, thus, enhance its representation capabilities. They also determine the output range of the hidden layers and the final output.  Traditionally, artificial neural networks mainly used the sigmoid activation function as the depth of the network was limited. Nevertheless, this function tends to saturate the gradients when the number of hidden layers increases. For that reason, in the last years, most of the works published related to deep learning and convolutional networks use the Rectified Linear Unit (ReLU), given that it provides good convergence properties and speeds up the training process thanks to the simplicity of its derivative. However, this function has some known drawbacks that gave rise to new proposals of alternatives activation functions based on ReLU. In this work, we describe, analyse and compare different recently proposed alternatives to test whether these functions improve the performance of deep learning models regarding the standard ReLU.",
booktitle = "Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA)",
doi = "10.1007/978-3-030-85713-4_4",
isbn = "978-3-030-85712-7",
issn = "0302-9743",
keywords = "analysis activations, RELU, RELU activations, deep learning",
month = "22nd-24th September",
organization = "Malaga, Spain",
pages = "33-43",
publisher = "Springer",
series = " Lecture Notes in Artificial Intelligence (LNAI)",
title = "{R}e{LU}-based activations: analysis and experimental study for deep learning",
url = "doi.org/10.1007/978-3-030-85713-4_4",
volume = "12882",
year = "2021",
}


@conference{FuzzyORCA2021,
author = "Francisco Javier Rodriguez-Lozano and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Jose Manuel Soto-Hidalgo and Juan Carlos G{\'a}mez-Granados",
abstract = "Classification and regression techniques are two of the main tasks considered by the Machine Learning area. They mainly depend on the target variable to predict. In this context, ordinal classification represents an intermediate task, which is focused on the prediction of nominal variables where the categories follow a specific intrinsic order given by the problem. Nevertheless, the integration of different algorithms able to solve ordinal classification problems is often unavailable in most of existing Machine Learning software, which hinders the use of new approaches. Therefore, this paper focuses on the incorporation of an ordinal classification algorithm (NSLVOrd) in one of the most complete ordinal regression frameworks, 'Ordinal Regression and Classification Algorithms framework (ORCA)' by using both fuzzy rules and the JFML library. The use of NSLVOrd in the ORCA tool as well as a case study with a real database are shown where the obtained results are promising.",
booktitle = "Proceedings of the IEEE International Conference on Fuzzy Systems (Fuzz-IEEE2021)",
doi = "10.1109/FUZZ45933.2021.9494526",
editor = "IEEE",
isbn = "978-1-6654-4407-1",
issn = "1558-4739",
keywords = "ORCA, Fuzzy ORCA, Fuzzy, JFML Library, NSLVOrd",
month = "11th-14th July",
organization = " Luxembourg, Luxembourg",
publisher = "IEEE Press",
title = "{E}nhancing the {ORCA} framework with a new {F}uzzy {R}ule {B}ase {S}ystem implementation compatible with the {JFML} library",
url = "doi.org/10.1109/FUZZ45933.2021.9494526",
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",
}