Buscar en RiuNet

Listar

Todo RiuNet

Mi cuenta

Acceder

Ayuda RiuNet

Admin. UPV

Listar por palabra clave "Recurrent neural networks"

Mostrando ítems 1-19 de 19

A Set of Benchmarks for Handwritten Text Recognition on Historical Documents

Sánchez Peiró, Joan Andreu; Romero, Verónica; Toselli, Alejandro Héctor; Villegas, Mauricio; Vidal, Enrique (Elsevier, 2019-10)

[EN] Handwritten Text Recognition is a important requirement in order to make visible the contents of the myriads of historical documents residing in public and private archives and libraries world wide. Automatic Handwritten ...
Anomalous Diffusion Characterization using Machine Learning Methods

Garibo Orts, Óscar (Universitat Politècnica de València, 2023-04-18)

[ES] Durante las últimas décadas el uso del aprendizaje automático (machine learning) y de la inteligencia artificial ha mostrado un crecimiento exponencial en muchas áreas de la ciencia. El hecho de que los ordenadores ...
Arquitectura neuronal para predicción de radiación solar en base a variables meteorológicas

Benavides, Llinet; Manso, Miguel (Editorial Universitat Politècnica de València, 2021-10-01)

[EN] Solar forecasting is of great interest due to the growing use of renewable energies as an alternative to the global problems posed by current energy sources. In the last decade important advances have been achieved, ...
Comparison of Bernoulli and Gaussian HMMs using a vertical repositioning technique for off-line handwriting recognition

Doetsch, Patrick; Hamdani, Mahdi; Ney, Hermann; Giménez Pastor, Adrián; Andrés Ferrer, Jesús; Juan Císcar, Alfonso (Institute of Electrical and Electronics Engineers (IEEE), 2012-09-18)

—In this paper a vertical repositioning method based on the center of gravity is investigated for handwriting recognition systems and evaluated on databases containing Arabic and French handwriting. Experiments show ...
Designing demand forecasting models using deep learning

Ortiz Pérez, Ángel (Universitat Politècnica de València, 2023-06-21)

[ES] En este trabajo se explorará el uso de distintos modelos de deep learning para predecir la demanda futura de un producto, lo que puede resultar de gran utilidad para la dirección operativa de una empresa. La elaboración ...
Diagnóstico de fallas mediante una LSTM y una red elástica

Márquez-Vera, M. A.; López-Ortega, O.; Ramos-Velasco, L. E.; Ortega-Mendoza, R. M.; Fernández-Neri, B. J.; Zúñiga-Peña, N. S. (Universitat Politècnica de València, 2021-04-06)

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared ...
Diseño, experimentación y puesta en producción de un sistema de detección de anomalías para la industria

Fornés Gabaldón, Héctor (Universitat Politècnica de València, 2022-10-27)

[ES] El objetivo de este trabajo es desarrollar un sistema de detección de anomalías para la industria, dentro del marco de mi puesto como trabajador en la empresa NUNSYS SA. Se cubrirán todas las fases del proyecto, ...
Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories

Garibo i Orts, Óscar; Baeza-Bosca, Alba; Garcia March, Miguel Angel; Conejero, J. Alberto (IOP Publishing, 2021-12-17)

[EN] Anomalous diffusion occurs at very different scales in nature, from atomic systems to motions in cell organelles, biological tissues or ecology, and also in artificial materials, such as cement. Being able to accurately ...
Forecasting exchange rates using recurrent neural networks

Cuéllar Abril, Juan Julián (Universitat Politècnica de València, 2020-09-18)

[EN] FOREX is a market in which large amounts of resources are invested in order to predict changes in currency pairs as accurately as possible. In this market, machine learning is also having a great impact and numerous ...
Inferring the fractional nature of Wu Baleanu trajectories

Conejero, J. Alberto; Garibo-i-Orts, Óscar; Lizama, Carlos (Springer-Verlag, 2023)

[EN] We infer the parameters of fractional discrete Wu Baleanu time series by using machine learning architectures based on recurrent neural networks. Our results shed light on howclearly one can determine that a given ...
Interactive neural machine translation

Peris Abril, Álvaro; Domingo-Ballester, Miguel; Casacuberta Nolla, Francisco (Elsevier, 2017-01)

Despite the promising results achieved in last years by statistical machine translation, and more precisely, by the neural machine translation systems, this technology is still not error-free. The outputs of a machine ...
MRI Rician Noise Reduction Using Recurrent Convolutional Neural Networks

Gurrola-Ramos, Javier; Alarcon, Teresa; Dalmau, Oscar; Manjón Herrera, José Vicente (Institute of Electrical and Electronics Engineers, 2024)

[EN] Magnetic resonance images are usually corrupted by noise during the acquisition process, which can affect the results of subsequent medical image analysis and diagnosis. This paper presents a denoising recurrent ...
On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market

Aineto, Diego; Iranzo-Sánchez, Javier; Lemus Zúñiga, Lenin Guillermo; Onaindia De La Rivaherrera, Eva; Urchueguía Schölzel, Javier Fermín (MDPI AG, 2019-06-01)

[EN] The mainstream of EU policies is heading towards the conversion of the nowadays electricity consumer into the future electricity prosumer (producer and consumer) in markets in which the production of electricity will ...
Online Learning Techniques for Neural Translation Systems

Montalvá Minguet, Kevin (Universitat Politècnica de València, 2017-02-13)

[EN] Machine Translation systems have been used since their inception by professional translators to speed up and ease their work. Those systems receive professionally edited translations through their use, which could ...
Predicting COVID19 pandemic waves including vaccination data with deep learning

Begga, Ahmed; Garibo-i-Orts, Óscar; de María-García, Sergi; Escolano, Francisco; Lozano, Miguel A.; Oliver, Nuria; Conejero, J. Alberto (Frontiers Media S.A., 2023-12-15)

[EN] IntroductionDuring the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect ...
Recovering discrete delayed fractional equations from trajectories

Conejero, J. Alberto; Garibo-i-Orts, Óscar; Lizama, Carlos (John Wiley & Sons, 2023-03-26)

[EN] We show how machine learning methods can unveil the fractional and delayed nature of discrete dynamical systems. In particular, we study the case of the fractional delayed logistic map. We show that given a trajectory, ...
Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks

Del Agua Teba, Miguel Angel; Giménez Pastor, Adrián; Sanchis Navarro, José Alberto; Civera Saiz, Jorge; Juan, Alfons (Institute of Electrical and Electronics Engineers, 2018)

[EN] In the last years, Deep Bidirectional Recurrent Neural Networks (DBRNN) and DBRNN with Long Short-Term Memory cells (DBLSTM) have outperformed the most accurate classifiers for confidence estimation in automatic speech ...
Translation rescoring through recurrent neural network language models

Peris Abril, Álvaro (Universitat Politècnica de València, 2014-09-23)

This work is framed into the Statistical Machine Translation field, more specifically into the language modeling challenge. In this area, have classically predominated the n-gram approach, but, in the latest years, ...
Travel Behavior Characterization Using Raw Accelerometer Data Collected from Smartphones

Ferrer López, Sheila; Ruiz Sánchez, Tomás (Elsevier, 2014)

In this paper, we compare different algorithms for the recognition of transportation modes based on features extracted from the accelerometer data. The performance and effectiveness of the transportation mode classifiers ...