Envíos recientes

  • Reconstruction of Gene Regulatory Networks with Multi-objective Particle Swarm Optimisers 

    Hurtado, Sandro; García-Nieto, José; Navas-Delgado, Ismael; Nebro-Urbaneja, Antonio Jesus; Aldana-Montes, Jose Francisco (2022-07-05)
    The computational reconstruction of Gene Regulatory Networks (GRNs) from gene expression data has been modelled as a complex optimisation problem, which enables the use of sophisticated search methods to address it. ...
  • Ontology-Driven Approach for KPI Meta-modelling, Selection and Reasoning 

    Roldan-Garcia, Maria del Mar; García-Nieto, José; Maté, Alejandro; Trujillo, Juan; Aldana-Montes, Jose Francisco (2022-09)
    A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives. This requires the exploration of business data through data-driven approaches, while modelling business ...
  • FIMED: Flexible management of biomedical data 

    Hurtado, Sandro; García-Nieto, José; Navas-Delgado, Ismael; Aldana-Montes, Jose Francisco (2022-09-05)
    In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these clinical tools have design limitations, since they are ...
  • Secure Mobile Agents on Embedded Boards: a TPM based solution 

    Muñoz, Antonio; García, Iván (ACM - ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security August 2022 Article No.: 104 Pages 1–7 https://doi.org/10.1145/3538969.3544419ARES 2022, 2022-08-26)
    La seguridad puede considerarse uno de los aspectos esenciales de cualquier sistema informático actual. El panorama actual evoluciona constantemente y aparecen nuevos modelos informáticos al mismo tiempo que surgen diferentes ...
  • Enhanced Perspective Generation by Consensus of NeX neural models 

    Pacheco dos Santos Lima Junior, Marcos Sergio; Fernández Rodríguez, José David; Ortiz-de-lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel; Domínguez-Merino, Enrique (2022-07)
    Neural rendering is a relatively new field of research that aims to produce high quality perspectives of a 3D scene from a reduced set of sample images. This is done with the help of deep artificial neural networks that ...
  • A Service for Flexible Management and Analysis of Heterogeneous Clinical Data 

    García-Nieto, José; Navas-Delgado, Ismael; Hurtado, Sandro (Springer, 2022-06-08)
    Este documento describe FIMED 2.0, un servicio para la gestión flexible y análisis de datos clínicos heterogéneos. Esta herramienta de software permite la gestión flexible de datos clínicos de múltiples ensayos, lo que ...
  • On the Use of Explainable Artificial Intelligence for the Differential Diagnosis of Pigmented Skin Lesions 

    Nematzadeh, Hossein; Navas-Delgado, Ismael; Berciano-Guerrero, Miguel Angel; Garcia Nieto, Jose Manuel; Hurtado, Sandro; [et al.] (Springer, 2022-06-08)
    En los últimos años, la Inteligencia Artificial Explicable (XAI) ha atraído la atención en la analítica de datos, ya que muestra un gran potencial en la interpretación de los resultados de complejos modelos de aprendizaje ...
  • Feature density as an uncertainty estimator method in the binary classification mammography images task for a supervised deep learning model 

    Hernández Vasquez, Marco A; Fuentes Fino, Ricardo Javier; Calderón-Ramírez, Saúl; Domínguez-Merino, Enrique; López-Rubio, Ezequiel; [et al.] (2022)
    Labeled medical datasets may include a limited number of observations for each class, while unlabeled datasets may include observations from patients with pathologies other than those observed in the labeled dataset. This ...
  • Encoding generative adversarial networks for defense against image classification attacks 

    Rodríguez Rodríguez, José Antonio; Pérez Bravo, José María; García González, Jorge; Molina-Cabello, Miguel A.; Thurnhofer-Hemsi, Karl; [et al.] (2022)
    Image classification has undergone a revolution in recent years due to the high performance of new deep learning models. However, severe security issues may impact the performance of these systems. In particular, adversarial ...
  • A novel continual learning approach for competitive neural networks 

    Fernández Rodríguez, José David; Maza Quiroga, Rosa María; Palomo Ferrer, Esteban José; Ortiz-de-lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel (2022)
    Continual learning tries to address the stability-plasticity dilemma to avoid catastrophic forgetting when dealing with non-stationary distributions. Prior works focused on supervised or reinforcement learning, but few ...
  • Analysis of functional connectome pipelines for the diagnosis of autism spectrum disorders 

    Maza Quiroga, Rosa María; López Rodríguez, Domingo; Thurnhofer-Hemsi, Karl; Luque-Baena, Rafael Marcos; Jiménez Valverde, Clara; [et al.] (2022-05)
    This paper explores the effect of using different pipelines to compute connectomes (matrices representing brain connections) and use them to train machine learning models with the goal of diagnosing Autism Spectrum ...
  • Anomalous trajectory detection for automated traffic video surveillance 

    Fernández Rodríguez, Jose David; García-González, Jorge; Benitez-Rochel, Rafaela; Molina Cabello, Miguel Ángel; López-Rubio, Ezequiel; [et al.] (2022)
    Vehicle trajectories extracted from traffic video sequences can be helpful for many purposes. In particular, the analysis of detected anomalous trajectories may enhance drivers’ safety. This work proposes a methodology to ...
  • Applying QoS in FaaS applications: a software product line approach 

    Serrano Gutierrez, Pablo; Ayala Viñas, Inmaculada; Fuentes-Fernández, Lidia (2022)
    A FaaS system offers numerous advantages for the developer of microservices-based systems since they do not have to worry about the infrastructure that supports them or scaling and maintenance tasks. However, it is not ...
  • Energy-efficient Deployment of IoT Applications in Edge-based Infrastructures: A Software Product Line Approach 

    Cañete Valverde, Ángel Jesús; Fuentes-Fernández, Lidia; Amor-Pinilla, Maria Mercedes (2021-09)
    In order to lower latency and reduce energy consumption, Edge Computing proposes offloading some computation intensive tasks usually performed in the Cloud onto nearby devices in the frontier/Edge of the access networks. ...
  • Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences 

    Palomo, Esteban J.; Benito Picazo, Jesús; Domínguez-Merino, Enrique; López-Rubio, Ezequiel; Ortega-Zamorano, Francisco (Springer, 2021-09)
    In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, ...
  • Peer assessments in Engineering: A pilot project 

    Thurnhofer-Hemsi, Karl; Molina-Cabello, Miguel A.; Palomo, Esteban J.; López-Rubio, Ezequiel; Domínguez, Enrique (2021)
    The evaluation methods employed in a course are the most important point for the students, above any other learning aspect. For teachers, this task is arduous when the number of students is high. Traditional evaluation ...
  • Longitudinal study of the learning styles evolution in Engineering degrees 

    Molina-Cabello, Miguel A.; Thurnhofer-Hemsi, Karl; Domínguez, Enrique; López-Rubio, Ezequiel; Palomo, Esteban J. (2021)
    A learning style describes what are the predominant skills for learning tasks. In the context of university education, knowing the learning styles of the students constitutes a great opportunity to improve both teaching ...
  • Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment 

    Molina Cabello, Miguel A.; Thurnhofer Hemsi, Karl; Maza Quiroga, Rosa María; Domínguez, Enrique; López-Rubio, Ezequiel; [et al.] (2021)
    Skin cancer is one of the most prevalent diseases among people. Physicians have a challenge every time they have to determine whether a diseased skin is benign or malign. There exist clinical diagnosis methods (such as ...
  • Improving Uncertainty Estimations for Mammogram Classification using Semi-Supervised Learning 

    Calderón-Ramírez, Saúl; Murillo-Hernández, Diego; Rojas-Salazar, Kevin; Calvo-Valverde, Luis-Alexander; Yang, Shengxiang; [et al.] (2021-07)
    Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning ...
  • Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms 

    Molina Cabello, Miguel A.; Rodríguez Rodríguez, José Antonio; Thurnhofer Hemsi, Karl; López-Rubio, Ezequiel (2021-07)
    One of the most invasive cancer types which affect women is breast cancer. Unfortunately, it exhibits a high mortality rate. Automated histopathological image analysis can help to diagnose the disease. Therefore, computer ...

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