@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 = "1-18", 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{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 = "1-14", title = "{EBANO}: {A} novel {E}nsemble {BA}sed on u{N}imodal {O}rdinal classifiers for the prediction of significant wave height", url = "https://www.sciencedirect.com/science/article/pii/S0950705124008578?via%3Dihub", volume = "300", year = "2024", } @article{312024, author = "Alejandro Morales-Mart{\'i}n and Francisco-Javier Mesas-Carrascosa and Pedro Antonio Guti{\'e}rrez and Fernando-Juan P{\'e}rez-Porras and V{\'i}ctor Manuel Vargas and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "first_page settings Order Article Reprints Open AccessArticle Deep Ordinal Classification in Forest Areas Using Light Detection and Ranging Point Clouds by Alejandro Morales-Mart{\'i}n 1,* [ORCID] , Francisco-Javier Mesas-Carrascosa 2 [ORCID] , Pedro Antonio Guti{\'e}rrez 1 [ORCID] , Fernando-Juan P{\'e}rez-Porras 2 [ORCID] , V{\'i}ctor Manuel Vargas 1 [ORCID] and C{\'e}sar Herv{\'a}s-Mart{\'i}nez 1 [ORCID] 1 Department of Computer Science and Numerical Analysis, University of C{\'o}rdoba, Campus de Rabanales, 14071 C{\'o}rdoba, Spain 2 Department of Graphic Engineering and Geomatics, University of C{\'o}rdoba, Campus de Rabanales, 14071 C{\'o}rdoba, Spain * Author to whom correspondence should be addressed. Sensors 2024, 24(7), 2168; https://doi.org/10.3390/s24072168 Submission received: 21 February 2024 / Revised: 20 March 2024 / Accepted: 26 March 2024 / Published: 28 March 2024 (This article belongs to the Special Issue Remote Sensing for Spatial Information Extraction and Process) Download keyboard_arrow_down Browse Figures Review Reports Versions Notes Abstract Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing an ordinal classification model in forest areas where LiDAR point clouds can be classified into four distinct ordinal classes: ground, low vegetation, medium vegetation, and high vegetation. To do so, an effective soft labeling technique based on a novel proposed generalized exponential function (CE-GE) is applied to the PointNet network architecture. Statistical analyses based on Kolmogorov–Smirnov and Student’s t-test reveal that the CE-GE method achieves the best results for all the evaluation metrics compared to other methodologies. Regarding the confusion matrices of the best alternative conceived and the standard categorical cross-entropy method, the smoothed ordinal classification obtains a more consistent classification compared to the nominal approach. Thus, the proposed methodology significantly improves the point-by-point classification of PointNet, reducing the errors in distinguishing between the middle classes (low vegetation and medium vegetation).", awards = "JCR(2023): 3.4, Position: 24/76 (Q2) Category: INSTRUMENTS {\&} INSTRUMENTATION", comments = "JCR(2023): 3.4, Position: 24/76 (Q2) Category: INSTRUMENTS {\&} INSTRUMENTATION", doi = "10.3390/s24072168", issn = "1424-8220", journal = "Sensors", keywords = "LiDAR point cloud, Deep Learning, ordinal classification, soft labeling", note = "JCR(2023): 3.4, Position: 24/76 (Q2) Category: INSTRUMENTS {\&} INSTRUMENTATION", number = "7", pages = "1-18", title = "{D}eep {O}rdinal {C}lassification in {F}orest {A}reas {U}sing {L}ight {D}etection and {R}anging {P}oint {C}louds", url = "www.mdpi.com/1424-8220/24/7/2168", volume = "24", year = "2024", } @conference{GEMA-congress_2024, 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", 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}", 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", issn = "1611-3349", keywords = "Extreme climate events, heat waves, multivariate method, analogue method", month = "Junio", 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.", booktitle = "Journal of Hepatology", doi = "https://doi.org/10.1016/S0168-8278(24)01212-1", issn = "1600-0641", month = "Junio", 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.", booktitle = "Journal of Hepatology", doi = "https://doi.org/10.1016/S0168-8278(24)00465-3", issn = "1600-0641", month = "Junio", 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", 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", 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", 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", 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 = "https://doi.org/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 = "https://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", 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", }