Browsing by Subject "Machine learning"

Now showing items 1-18 of 18

    • An unsupervised Hidden Markov Model-based system for the detection and classification of blue whale ... 
      Buchan, Susannah J.; Mahú Sinclair, Rodrigo; Wuth, Jorge; Balcazar Cabrera, Naysa; Gutiérrez, Laura; Neira, Sergio; Becerra Yoma, Néstor (Taylor & Francis, 2020)
      In this paper, we present an automatic method, without human supervision, for the detection and classification of blue whale vocalizations from passive acoustic monitoring (PAM) data using Hidden Markov Model technology ...
    • Blockchain-based federated learning for intelligent control in Heavy Haul Railway 
      Gaofeng, Hua; Zhu, Li; Wu, Jinsong; Shen, Chunzi; Zhou, Lingyan; Lin, Qingqing (Institute of Electrical and Electronics Engineers (IEEE), 2020)
      Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the ...
    • COMPUTATIONAL STUDY on the RUPTURE RISK in REAL CEREBRAL ANEURYSMS with GEOMETRICAL and FLUID-MECHANICAL ... 
      Aranda, Alfredo; Valencia, Alvaro (World Scientific Publishing Co. Pte Ltd, 2019)
      Fluid-mechanical and morphological parameters are recognized as major factors in the rupture risk of human aneurysms. On the other hand, it is well known that a lot of machine learning tools are available to study a variety ...
    • DMAKit: A user-friendly web platform for bringing state-of-the-art data analysis techniques to non-specific users 
      Medina Ortiz, David; Silva Contreras, Sebastián Jesús; Quiroz, Cristofer; Asenjo de Leuze, Juan; Olivera Nappa, Álvaro (Elsevier, 2020)
      Tremendous advances in different areas of knowledge are producing vast volumes of data, a quantity so large that it has made necessary the development of new computational algorithms. Among the algorithms developed, we ...
    • Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning ... 
      Cartes, C.; López, D.; Salinas, D.; Segovia, C.; Ahumada, C.; Pérez, N.; Valenzuela F., Manuel A.; Lanza, N.; López Solís, R. O.; Perez, V. L.; Zegers, P.; Fuentes, A.; Alarcón, C.; Traipe, L. (Elsevier Ltd, 2019)
      Objective: Because of high variability, tear film osmolarity measures have been questioned in dry eye assessment. Understanding the origin of such variability would aid data interpretation. This study aims to evaluate ...
    • Going deep into schizophrenia with artificial intelligence 
      Cortés Briones, Jose A.; Tapia Rivas, Nicolás Igor; D'Souza, Deepak Cyril; Estévez Valencia, Pablo Antonio (Elsevier, 2022)
      Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity ...
    • Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization 
      Baddoo, Peter J.; Herrmann Priesnitz, Benjamín; McKeon, Beverley J.; Brunton, Steven L. (Royal Soc Chemistry, 2022)
      Research in modern data-driven dynamical systems is typically focused on the three key challenges of high dimensionality, unknown dynamics and nonlinearity. The dynamic mode decomposition (DMD) has emerged as a cornerstone ...
    • Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to ... 
      Wolff Rojas, Patricio; Ríos Pérez, Sebastián; Clavijo, David; Grana, Manuel; Carrasco, Miguel (BMC, 2020)
      Background Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, ...
    • Novel multilayer extreme learning machine as a massive MIMO receiver for millimeter wave communications 
      Carrera, Diego Fernando; Vagas Rosales, César; Zabala Blanco, David; Yungaicela Naula, Noe M; Azurdia Meza, César Augusto; Mohamed, Marey; Ali Dehghan, Firoozabadi (IEEE, 2022)
      Wireless communication systems working in millimeter-wave (mmWave) frequency bands offer higher bandwidths than traditional radio frequency schemes. This technology allows multibeam steering and data multiplexing with the ...
    • Operation scheduling in a solar thermal system: A reinforcement learning-based framework 
      Correa Jullian, Camila; López Droguett, Enrique; Cardemil Iglesias, José (Elsevier, 2020)
      Reinforcement learning (RL) provides an alternative method for designing condition-based decision making in engineering systems. In this study, a simple and flexible RL tabular Q-learning framework is employed to identify ...
    • Predicting EHL film thickness parameters by machine learning approaches 
      Marian, Max; Mursak, Jonas; Bartz, Marcel; Profito, Francisco J.; Rosenkranz, Andreas; Wartzack, Sandro (Springer, 2022)
      Non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters of elastohydrodynamically lubricated (EHL) contacts. In this contribution, we demonstrate ...
    • Predicting nationwide obesity from food sales using machine learning 
      Dunstan, Jocelyn; Aguirre Jerez, Marcela; Bastías García, Magdalena; Nau, Claudia; Glass, Thomas A.; Tobar, Felipe (SAGE, 2020)
      The obesity epidemic progresses everywhere across the globe, and implementing frequent nationwide surveys to measure the percentage of obese population is costly. Conversely, country-level food sales information can be ...
    • Predicting stroke risk with an interpretable classifier 
      Peñafiel, Sergio; Baloian Tataryan, Nelson; Sanson, Horacio; Pino Urtubia, José (IEEE-Inst Electrical Electronics Engineers, 2021)
      Predicting an individual's risk of getting a stroke has been a research subject for many authors worldwide since it is a frequent illness and there is strong evidence that early awareness of having that risk can be bene ...
    • Setting up standards: A methodological proposal for pediatric Triage machine learning model construction ... 
      Wolff Rojas, Patricio; Ríos, Sebastián A.; Graña, Manuel (Elsevier Ltd, 2019)
      © 2019 Elsevier LtdTriage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper ...
    • The use of artificial intelligence in tribology—a perspective 
      Rosenkranz, Andreas; Marian, Max; Profito, Francisco J.; Aragón, Nathan; Shah, Raj (MDPI, 2021)
      Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the ...
    • Triage prediction in pediatric patients with respiratory problems 
      Garmendia, Asier; Rios, Sebastian A.; Lopez-Guede, Jose M.; Graña, Manuel (Elsevier B.V., 2019)
      © 2017 Elsevier B.V. Respiratory diseases have an increasing prevalence in the large urban concentration of the world, due to the apparently unstoppable increase of air pollution from a diversity of sources. Children are ...
    • Unsupervised learning of structure in spectroscopic cubes 
      Araya, M.; Mendoza, M.; Solar, M.; Mardones, D.; Bayo, A. (Elsevier B.V., 2018)
      © 2018 Elsevier B.V. We consider the problem of analyzing the structure of spectroscopic cubes using unsupervised machine learning techniques. We propose representing the target's signal as a homogeneous set of volumes ...
    • Using psychophysiological sensors to assess mental workload during web browsing 
      Jiménez Molina, Ángel; Retamal, Cristian; Lira, Hernan (MDPI, 2018)
      Knowledge of the mental workload induced by a Web page is essential for improving users' browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address this issue, ...