R
Rubén González Crespo
Researcher at Universidad Internacional de La Rioja
Publications - 213
Citations - 2634
Rubén González Crespo is an academic researcher from Universidad Internacional de La Rioja. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 22, co-authored 167 publications receiving 1554 citations. Previous affiliations of Rubén González Crespo include University of La Rioja & Technical University of Lisbon.
Papers
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Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction.
TL;DR: In this article, a case study of using composite Monte-Carlo (CMC) that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented.
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Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak
TL;DR: A methodology that embraces these three virtues of data mining from a small dataset is proposed, which aims at fine-tuning the parameters of an individual forecastingmodel for the highest possible accuracy.
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Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT
TL;DR: The Reinforcement Learning techniques Multi Objective Ant Colony Optimization (MOACO) algorithms has been applied to deal with the accurate resource allocation between the end users in the way of creating the cost mapping tables creations and optimal allocation in MEC.
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Metamodel for integration of Internet of Things, Social Networks, the Cloud and Industry 4.0
José Ignacio Rodríguez Molano,Juan Manuel Cueva Lovelle,Carlos Montenegro,J. Javier Rainer Granados,Rubén González Crespo +4 more
TL;DR: This paper analyzes Internet of Things (IoT), its use into manufacturing industry, its foundation principles, available elements and technologies for the man-things-software communication already developed in this area, and proves how important its deployment is.
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Energy enhancement using Multiobjective Ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks
TL;DR: The simulation experiments showcase that the throughput, lifetime and jamming prediction is analyzed and enhances the energy using the MOACO, when compared to the artificial bee colony and genetic algorithm.