@article{GEMA-AI-2025,
author = "Antonio Manuel G{\'o}mez-Orellana and Manuel Rodr{\'i}guez-Per{\'a}lvarez and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Avik Majumdar and McCaughan, Geoffrey W. and Rhiannon Taylor and Emmanuel Tsochatzis and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "Background {\&} Aims
We aimed to develop and validate an artificial intelligence score (GEMA-AI) to predict liver transplant (LT) waiting list outcomes using the same input variables contained in existing models.

Methods
Cohort study including adult LT candidates enlisted in the United Kingdom (2010-2020) for model training and internal validation, and in Australia (1998-2020) for external validation. GEMA-AI combined international normalized ratio, bilirubin, sodium, and the Royal Free Glomerular Filtration Rate in an explainable Artificial Neural Network. GEMA-AI was compared with GEMA-Na, MELD 3.0, and MELD-Na for waiting list prioritization.

Results
The study included 9,320 patients: training cohort n=5,762, internal validation cohort n=1,920, and external validation cohort n=1,638. The prevalence of 90-days mortality or delisting for sickness ranged 5.3%-6% across different cohorts. GEMA-AI showed better discrimination than GEMA-Na, MELD-Na and MELD 3.0 in the internal and external validation cohorts, with a more pronounced benefit in women and in patients showing at least one extreme analytical value. Accounting for identical input variables, the transition from a linear to a non-linear score (from GEMA-Na to GEMA-AI) resulted in a differential prioritization of 6.4% of patients within the first 90 days and would potentially save one in 59 deaths overall, and one in 13 deaths among women. Results did not substantially change when ascites was not included in the models.

Conclusions
The use of explainable machine learning models may be preferred over conventional regression-based models for waiting list prioritization in LT. GEMA-AI made more accurate predictions of waiting list outcomes, particularly for the sickest patients.",
awards = "JCR (2024): 11.6, Position: 9/143 (Q1), Category: GASTROENTEROLOGY {\&} HEPATOLOGY",
comments = "JCR (2024): 11.6, Position: 9/143 (Q1), Category: GASTROENTEROLOGY {\&} HEPATOLOGY",
doi = "10.1016/j.cgh.2024.12.010",
issn = "1542-3565",
journal = "Clinical Gastroenterology and Hepatology",
note = "JCR (20224): 11.6, Position: 9/143 (Q1), Category: GASTROENTEROLOGY {\&} HEPATOLOGY",
title = "{G}ender-{E}quity {M}odel for {L}iver {A}llocation using {A}rtificial {I}ntelligence ({GEMA}-{AI}) for waiting list liver transplant prioritization",
url = "doi.org/10.1016/j.cgh.2024.12.010",
volume = "Accepted",
year = "2025",
}


@article{PaperMutua2025,
author = "Rafael Calleja-Lozano and Marcos Rivera-Gavil{\'a}n and David Guijo-Rubio and Hessheimer, Amelia J. and Gloria De la Rosa and Mikel Gastaca and Alejandra Otero and Pablo Ramirez and Andrea Bosc{\`a}-Robledo and Julio Santoyo-Santoyo and Mar{\'i}n-G{\'o}mez, Luis Miguel and Del Villar Moral, Jes{\'u}s and Yiliam Fundora and Laura Llad{\'o} and Carmelo Loinaz and Jim{\'e}nez-Garrido, Manuel C. and Gonzalo Rodr{\'i}guez-La{\'i}z and L{\'o}pez-Baena, Jos{\'e} A. and Ram{\'o}n Charco and Evaristo Varo and Fernando Rotellar and Ayaya Alonso and Rodr{\'i}guez-Sanjuan, Juan C. and Gerardo Blanco and Javier Nu{\~n}o and David Pacheco and Elisabeth Coll and Beatriz Dom{\'i}nguez-Gil and Constantino Fontdevila and Ayll{\'o}n, Mar{\'i}a Dolores and Manuel Dur{\'a}n and Rub{\'e}n Ciria and Pedro Antonio Guti{\'e}rrez and Antonio Manuel G{\'o}mez-Orellana and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Javier Brice{\~n}o",
awards = "JCR (2024): 5.0, Position: 15/312 (Q1D1), Category: SURGERY",
comments = "JCR (2024): 5.0, Position: 15/312 (Q1D1), Category: SURGERY",
doi = "10.1097/tp.0000000000005312",
issn = "0041-1337",
journal = "Transplantation",
note = "JCR (2024): 5.0, Position: 15/312 (Q1D1), Category: SURGERY",
number = "7",
pages = "e362-e370",
title = "{M}achine learning algorithms in controlled donation after circulatory death under normothermic regional perfusion: {A} graft survival prediction model",
url = "doi.org/10.1097/tp.0000000000005312",
volume = "109",
year = "2025",
}


@article{CNNJaviBarbero2025,
author = "Javier Barbero-G{\'o}mez and Cruz, Ricardo P.M. and Cardoso, Jaime S. and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "The use of Convolutional Neural Network (CNN) models for image classification tasks has gained significant popularity. However, the lack of interpretability in CNN models poses challenges for debugging and validation. To address this issue, various explanation methods have been developed to provide insights into CNN models. This paper focuses on the validity of these explanation methods for ordinal regression tasks, where the classes have a predefined order relationship. Different modifications are proposed for two explanation methods to exploit the ordinal relationships between classes: Grad-CAM based on Ordinal Binary Decomposition (GradOBD-CAM) and Ordinal Information Bottleneck Analysis (OIBA). The performance of these modified methods is compared to existing popular alternatives. Experimental results demonstrate that GradOBD-CAM outperforms other methods in terms of interpretability for three out of four datasets, while OIBA achieves superior performance compared to IBA.",
awards = "JCR(2024): 6.5, Position: 37/204 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
comments = "JCR(2024): 6.5, Position: 37/204 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
doi = "10.1016/j.neucom.2024.128878",
issn = "0925-2312",
journal = "Neurocomputing",
keywords = "convolutional neural networks, explanation methods, ordinal regression",
note = "JCR(2024): 6.5, Position: 37/204 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
pages = "128878",
title = "{CNN} {E}xplanation {M}ethods for {O}rdinal {R}egression {T}asks",
url = "doi.org/10.1016/j.neucom.2024.128878",
volume = "615",
year = "2025",
}


@article{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(2024): 8.9, Position: 8/147 (Q1D1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS",
comments = "JCR(2024): 8.9, Position: 8/147 (Q1D1) 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",
note = "JCR(2024): 8.9, Position: 8/147 (Q1D1) Category: COMPUTER SCIENCE, THEORY {\&} METHODS",
number = "3",
pages = "4765 - -4778",
title = "{L}earning {O}rdinal–{H}ierarchical {C}onstraints for {D}eep {L}earning {C}lassifie",
url = "ieeexplore.ieee.org/document/10432994",
volume = "36",
year = "2025",
}


@article{SMSwindpredictionrafaelayllon2025,
author = "Rafael Ayll{\'o}n-Gavil{\'a}n and Antonio Manuel G{\'o}mez-Orellana and V{\'i}ctor Manuel Vargas and David Guijo-Rubio and Jorge P{\'e}rez-Aracil and Sancho Salcedo-Sanz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "In this paper, we present the MUSONet model, which leverages information from different sources (in this case, wind farms) to perform a multi-step wind speed prediction. The main goal of this approach is improving the global prediction accuracy, specifically at longer prediction horizons. Thus, the proposed model is able to simultaneously predict the wind speed at three different prediction horizons (6h, 12h, and 24h), across three different wind farms located in Spain. We also evaluate the performance of the presented methodology by considering three different activation functions for hidden neurons in the neural network: Sigmoid, ReLU, and ELUs+2L. The results show that the proposed multi-source approach improves the performance of the single-source counterpart for the longer prediction horizons (12h and 24h). In addition, the proposed multi-source method reduces by over 30% the number of parameters compared to three single-source models (in this case, one model per wind farm), resulting in a simpler solution for the problem addressed and requiring much lower computational resources.",
awards = "JCR(2024): 5.3, Position: 22/175 (Q1) Category: ENGINEERING, MULTIDISCIPLINARY",
comments = "JCR(2024): 5.3, Position: 22/175 (Q1) Category: ENGINEERING, MULTIDISCIPLINARY",
doi = "10.1177/10692509251337224",
issn = "1069-2509",
journal = "Integrated Computer-Aided Engineering",
keywords = "Multi-task algorithms, deep neural networks, wind speed prediction, wind farms",
month = "Mayo",
note = "JCR(2024): 5.3, Position: 22/175 (Q1) Category: ENGINEERING, MULTIDISCIPLINARY",
pages = "1--13",
title = "{S}imultaneous multi-step wind speed prediction on multiple farms using multi-task deep learning",
url = "doi.org/10.1177/10692509251337224",
volume = "Accepted",
year = "2025",
}


@article{dlordinal_2025,
author = "Francisco B{\'e}rchez-Moreno and Rafael Ayll{\'o}n-Gavil{\'a}n and V{\'i}ctor Manuel Vargas and David Guijo-Rubio and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Juan Carlos Fern{\'a}ndez and Pedro Antonio Guti{\'e}rrez",
abstract = "dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal",
awards = "JCR(2024): 6.5, Position: 37/204 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
comments = "JCR(2024): 6.5, Position: 37/204 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
doi = "10.1016/j.neucom.2024.129305",
issn = "1872-8286",
journal = "Neurocomputing",
keywords = "Deep learning, ordinal classification, ordinal regression, python, pyTorch, soft labelling",
month = "Marzo",
note = "JCR(2024): 6.5, Position: 37/204 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
pages = "129305",
title = "{D}lordinal: {A} {P}ython package for deep ordinal classification",
url = "doi.org/10.1016/j.neucom.2024.129305",
volume = "622",
year = "2025",
}


@article{RAFACyb2025,
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(2024): 10.5, Position: 1/31 (Q1D1) Category: COMPUTER SCIENCE, CYBERNETICS",
comments = "JCR(2024): 10.5, Position: 1/31 (Q1D1) Category: COMPUTER SCIENCE, CYBERNETICS",
doi = "10.1109/TCYB.2024.3498100",
issn = "2168-2267",
journal = "IEEE Transactions on Cybernetics",
keywords = "time series machine learning, time series analysis, time series classification, ordinal classification",
month = "Febrero",
note = "JCR(2024): 10.5, Position: 1/31 (Q1D1) Category: COMPUTER SCIENCE, CYBERNETICS",
number = "2",
pages = "537--539",
title = "{C}onvolutional and {D}eep {L}earning based techniques for {T}ime {S}eries {O}rdinal {C}lassification",
url = "ieeexplore.ieee.org/document/10769513",
volume = "55",
year = "2025",
}


@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(2024): 4.7, Position: 58/204 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
comments = "JCR(2024): 4.7, Position: 58/204 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
doi = "10.1007/s00146-023-01861-4",
issn = "1435-5655",
journal = "AI {\&} SOCIETY",
month = "Febrero",
note = "JCR(2024): 4.7, Position: 58/204 (Q2), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
pages = "235--237",
title = "{AI} research assistants, intrinsic values, and the science we want",
url = "link.springer.com/article/10.1007/s00146-023-01861-4",
volume = "40",
year = "2025",
}


@article{aPOR2024,
author = "C{\'e}sar Pel{\'a}ez-Rodr{\'i}guez and Jorge P{\'e}rez-Aracil and Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Sancho Salcedo-Sanz",
abstract = "Providing an accurate prediction of Significant Wave Height (SWH), and specially of extreme SWH events, is crucial for coastal engineering activities and holds major implications in several sectors as offshore renewable energy. With the aim of overcoming the challenge of skewness and imbalance associated with the prediction of these extreme SWH events, a fuzzy-based cascade ensemble of regression models is proposed. This methodology allows to remarkably improve the predictive performance on the extreme SWH values, by using different models specialised in different ranges on the target domain. The method’s explainability is enhanced by analysing the contribution of each model, aiding in identifying those predictor variables more characteristic for the detection of extreme SWH events. The methodology has been validated tackling a long-term SWH prediction problem, considering two case studies over the southwest coast of the United States of America. Both reanalysis data, providing information on various meteorological factors, and SWH measurements, obtained from the nearby stations and the station under examination, have been considered. The goodness of the proposed approach has been validated by comparing its performance against several machine learning and deep learning regression techniques, leading to the conclusion that fuzzy ensemble models perform much better in the prediction of extreme events, at the cost of a slight deterioration in the rest of the samples. The study contributes to advancing the SWH prediction field, specially, to understanding the behaviour behind extreme SWH events, critical for various sectors reliant on oceanic conditions.",
awards = "JCR (2024): 4.4, Position: 6/65 (Q1D1), Category: OCEANOGRAPHY",
comments = "JCR (2024): 4.4, Position: 6/65 (Q1D1), 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 (2024): 4.4, Position: 6/65 (Q1D1), 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 (2024): 2.9, Position: 54/312 (Q1), Category: SURGERY",
comments = "JCR (2024): 2.9, Position: 54/312 (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 (2024): 2.9, Position: 54/312 (Q1), Category: SURGERY",
number = "11",
pages = "108642",
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 (2024): 3.2, Position: 82/258 (Q2), Category: GEOSCIENCES, MULTIDISCIPLINARY",
comments = "JCR (2024): 3.2, Position: 82/258 (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 (2024): 3.2, Position: 82/258 (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 (2024): 8.0, Position: 5/175 (Q1D1), Category: ENGINEERING, MULTIDISCIPLINARY.",
comments = "JCR (2024): 8.0, Position: 5/175 (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 (2024): 8.0, Position: 5/175 (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{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(2024): 4.8, Position: 19/135 (Q1) Category: MULTIDISCIPLINARY SCIENCES",
comments = "JCR(2024): 4.8, Position: 19/135 (Q1) Category: MULTIDISCIPLINARY SCIENCES",
doi = "10.1111/nyas.15243",
issn = "0077-8923",
journal = "Annals of the New York Academy of Sciences",
note = "JCR(2024): 4.8, Position: 19/135 (Q1) Category: MULTIDISCIPLINARY SCIENCES",
number = "1",
pages = "230--242",
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",
volume = "1541",
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. ",
awards = "JCR(2024): 5.2, Position: 18/89 (Q1), Category: AUTOMATION {\&} CONTROL SYSTEMS",
comments = "JCR(2024): 5.2, Position: 18/89 (Q1), Category: AUTOMATION {\&} CONTROL SYSTEMS",
issn = "1532-4435",
journal = "Journal of Machine Learning Research",
keywords = "Python, Open source, Time series, Machine learning, Data mining, Forecasting, Classification, Extrinsic regression, Clustering",
note = "JCR(2024): 5.2, Position: 18/89 (Q1), Category: AUTOMATION {\&} CONTROL SYSTEMS",
number = "289",
pages = "1-10",
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 (2024): 7.2, Position: 27/197 (Q1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
comments = "JCR (2024): 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 (2024): 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{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 (2024): 4.3, Position: 65/258 (Q2), Category: COMPUTER SCIENCE, INFORMATION SYSTEMS",
comments = "JCR (2024): 4.3, Position: 65/258 (Q2), Category: COMPUTER SCIENCE, INFORMATION SYSTEMS",
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",
note = "JCR (2024): 4.3, Position: 65/258 (Q2), Category: COMPUTER SCIENCE, INFORMATION SYSTEMS",
pages = "2141--2185",
title = "{U}nsupervised feature based algorithms for time series extrinsic regression",
url = "link.springer.com/article/10.1007/s10618-024-01027-w",
volume = "38",
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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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(2024): 3.5, Position: 24/79 (Q2) Category: INSTRUMENTS {\&} INSTRUMENTATION",
comments = "JCR(2024): 3.5, Position: 24/79 (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(2024): 3.5, Position: 24/79 (Q2) Category: INSTRUMENTS {\&} INSTRUMENTATION",
number = "7",
pages = "1-18",
title = "{D}eep {O}rdinal {C}lassification in {F}orest {A}reas {U}sing {L}ight {D}etection and {R}anging {P}oint {C}louds",
url = "www.mdpi.com/1424-8220/24/7/2168",
volume = "24",
year = "2024",
}


@article{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(2024): 15.5, Position: 4/204 (Q1D1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
comments = "JCR(2024): 15.5, Position: 4/204 (Q1D1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
doi = "10.1016/j.inffus.2024.102299",
issn = "1566-2535",
journal = "Information Fusion",
keywords = "Deep Learning, Dropout, Ordinal Classification, Ordinal Regression, Convolutional Neural Networks",
note = "JCR(2024): 15.5, Position: 4/204 (Q1D1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
pages = "102299",
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",
}


@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 David Guijo-Rubio 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(2024): 10.0, Position: 13/333 (Q1D1) Category: MEDICINE, GENERAL {\&} INTERNAL",
booktitle = "eClinalMedicine",
comments = "JCR(2024): 10.0, Position: 13/333 (Q1D1) Category: MEDICINE, GENERAL {\&} INTERNAL",
doi = "10.1016/j.eclinm.2024.102737",
issn = "2589-5370",
journal = "eClinicalMedicine",
month = "Agosto",
note = "JCR(2024): 10.0, Position: 13/333 (Q1D1) 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",
}