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Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks
García, F.
; Lee, D.-J.; Mendoza-Garcés, F. J.; Irigoyen-Miró, S.; Legarreta-Olabarrieta, M. J.; García-Gutiérrez, S.; Arostegui, I. (2023-04)
; Lee, D.-J.; Mendoza-Garcés, F. J.; Irigoyen-Miró, S.; Legarreta-Olabarrieta, M. J.; García-Gutiérrez, S.; Arostegui, I. (2023-04)
Background:A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients’ morphology.
Objective:To ...
Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques
Hayet-Otero, M.; García, F.; Lee, D.-J.; Martínez-Minaya, J.; España Yandiola, P.P.; Urrutia Landa, I.; Nieves Ermecheo, M.; Quintana, J.M.; Menéndez, R.; Torres, A.; Zalacain Jorge, R.; Arostegui, I. (2023)
; Lee, D.-J.; Martínez-Minaya, J.; España Yandiola, P.P.; Urrutia Landa, I.; Nieves Ermecheo, M.; Quintana, J.M.; Menéndez, R.; Torres, A.; Zalacain Jorge, R.; Arostegui, I. (2023)
With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse ...
Learning the progression patterns of treatments using a probabilistic generative model
Zaballa, O.; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2022-12-15)
; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2022-12-15)
Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model ...
Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach
Boutaib, S.; Elarbi, M.; Bechikh, S.; Coello, C.A.; Said, L. B. (2022-11-01)
; Said, L. B. (2022-11-01)
Context: Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap: Existing works did not take into account the issue of ...
Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering
del Barrio, E.; Inouzhe, H.; Loubes, J.-M. (2022-10-20)
; Loubes, J.-M. (2022-10-20)
We consider the problem of diversity enhancing clustering, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc. In the ...
Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques
Shirazi, A.; Ceberio, J.; Lozano, J.A. (2022-10-09)
; 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 ...
A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization
Li, L.; Li, Y.; Lin, Q.; Ming, Z.; Coello, C.A. (2022-10-01)
(2022-10-01)
Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection ...
A mathematical analysis of EDAs with distance-based exponential models
Unanue, I.; Merino, M.; Lozano, J.A. (2022-09-01)
; 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 ...
On the Construction of Pareto-Compliant Combined Indicators
Falcón-Cardona, J.G.; Emmerich, M.; Coello, C.A. (2022-08-12)
(2022-08-12)
The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect ...
Multiple source transfer learning for dynamic multiobjective optimization
Ye, Y.; Lin, Q.; Ma, L.; Wong, K. C.; Gong, M.; Coello, C.A. (2022-08-01)
(2022-08-01)
Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs ...