• Variable selection in high-dimensional data: application in a SARS-CoV-2 pneumonia clinical data-set 

  Hayet-Otero, M. (2021-09-15)

  As a result of the COVID-19 pandemic that collapsed hospitals in some countries, numerous studies have been carried out to understand the development of the disease and how it affects patients with different characteristics, ...

• Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques 

  Shirazi, A.; Ceberio, J.; Lozano, J.A. (2022-10-09)

  In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs ...

• Development of machine learning system for airway prediction from facial image with mobile device 

  Mendoza Garcés, F.; García, F.; García, S.; García, B.; Vargas, J.; Lee, D.-J. (2022-06)

  Goals: A reliable prognostic tool for a difficult airway (DA) may enhance patients’ safety during orotracheal intubation by decreasing unanticipated DAs. We aim to examine the applicability of an Artificial Intelligence-Deep ...

• Desarrollo de sistema de Machine Learning para la prediccion de vía aérea a partir de imagen facial con dispositivo movil 

  Mendoza Garcés, F.; Irigoyen Miró, S.; García, F.; González, J.; García, S.; Telletxea, S. (2022-04)

  El manejo de una vía aérea difícil (VAD) representa aún una causa importante de lesiones relacionadas con la anestesia, cuyas complicaciones son potencialmente mortales. El notable interés en la predicción de VAD ha provocado ...

• Impacto cuantitativo de la contaminación en la probabilidad de muerte por neumonía por SARS-CoV-2 

  Bronte Moreno, O.; Urrutia Landa, I.; Martínez-Minaya, J.; García, F.; Lee, D.-J.; Jódar Samper, A.; García Hontoria, P.; Arostegui, I.; Artaraz Ereño, A.; Menéndez Villanueva, R.; Méndez Ocaña, R.; Torres Marti, A.; Cillóniz, C.; Zalacain Jorge, R.; Ruiz Iturriaga, L.A.; Serrano Fernández, L.; España Yandiola, P.P. (2021-11)

  Introducción La evidencia científica disponible señala que la contaminación del aire exterior podría agravar la severidad de la COVID-19 y por ende, incrementar las probabilidades de fallecimiento. Material y métodos Estudio ...

• Predicción de la gravedad de neumonías por SARS-CoV-2 a partir de información clínica y contaminación, mediante inteligencia artificial 

  Bronte Moreno, O.; Urrutia Landa, I.; García, F.; Martínez-Minaya, J.; Uranga Echeverria, A.; Lee, D.-J.; García Hontoria, P.; Jódar Samper, A.; Arostegui, I.; Menéndez Villanueva, R.; Méndez Ocaña, R.; Torres Marti, A.; Cillóniz, C.; Zalacain Jorge, R.; Ruiz Aldaiturriaga, L.A.; Serrano Fernández, L.; España Yandiola, P.P. (2021-11)

  Introducción La contaminación del aire exterior se ha relacionado con mayor gravedad de las infecciones respiratorias. Por tanto, su inclusión en algoritmos predictivos podrían añadir información para pronosticar la ...

• A General Framework Based on Walsh Decomposition for Combinatorial Optimization Problems 

  Unanue, I.; Merino, M.; Lozano, J.A. (2021-01-01)

  In this paper we pursue the use of the Fourier transform for a general analysis of combinatorial optimization problems. While combinatorial optimization problems are defined by means of different notions like weights in a ...

• A mathematical analysis of EDAs with distance-based exponential models 

  Unanue, I.; Merino, M.; Lozano, J.A. (2022-09-01)

  Estimation of Distribution Algorithms have been successfully used to solve permutation-based Combinatorial Optimization Problems. In this case, the algorithms use probabilistic models specifically designed for codifying ...

• Ad-Hoc Explanation for Time Series Classification 

  Abanda, A.; Mori, U.; Lozano, J.A. (2022)

  In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and ...

• Learning a Battery of COVID-19 Mortality Prediction Models by Multi-objective Optimization 

  Martinez, M.; García-Gutierrez, S.; Armañanzas, R.; Díaz, A.; Inza, I.; Lozano, J.A. (2022-07-09)

  The COVID-19 pandemic is continuously evolving with drastically changing epidemiological situations which are approached with different decisions: from the reduction of fatalities to even the selection of patients with the ...

• The role of asymmetric prediction losses in smart charging of electric vehicles 

  Straka, M.; Buzna, L.; Refa, N.; Mazuelas, S. (2022-07-01)

  Climate change prompts humanity to look for decarbonisation opportunities, and a viable option is to supply electric vehicles with renewable energy. The stochastic nature of charging demand and renewable generation requires ...

• Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees 

  Alvarez, V.; Mazuelas, S.; Lozano, J.A. (2022-07)

  The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate ...

• An active adaptation strategy for streaming time series classification based on elastic similarity measures 

  Oregi, I.; Pérez, A.; Del Ser, J.; Lozano, J.A. (2022-05-21)

  In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line ...

• Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables 

  li, Y.; Mazuelas, S.; Shen, Y. (2022-05-23)

  Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address ...

• Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification 

  Li, X.; Mazuelas, S.; Shen, Y. (2021-12-07)

  Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line- of-sight conditions. This paper proposes a ...

• Implementing the Cumulative Difference Plot in the IOHanalyzer 

  Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2022-07)

  The IOHanalyzer is a web-based framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results ...

• A Resource Sharing Game for the Freshness of Status Updates 

  Miguelez, F.; Doncel, J.; Ayesta, U. (2021-09-01)

• Rank aggregation for non-stationary data streams 

  Irurozki, E.; Pérez, A.; Lobo, J.L.; Del Ser, J. (2022)

  The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution ...

• A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data 

  Pérez, D.M.; Rodríguez-Álvarez, M.X.; Boer, M.P; Kronenberg, L.; Hund, A.; Cabrera-Bosquet, L.; Millet, E.J.; van Eeuwijk, F.A. (2022-02-24)

  High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical ...

• A Deep Learning Approach for Generating Soft Range Information from RF Data 

  li, Y.; Mazuelas, S.; Shen, Y. (2022-01-24)

  Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative ...

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