Recurrent neural networks
Imputation method based on recurrent neural networks for the internet of things
... on Recurrent Neural Networks, a family of supervised learning methods which have excel at exploiting patterns in sequential data and intrinsic association between the variables of ...
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Artificial recurrent neural networks for the distributed control of electrical grids with photovoltaic electricity
... • European Union (EU). Meanwhile, the EU through the SmartGrids Technology Platform published its vision of the SG strategy for Europe’s electricity networks in 2006 (European Commission, 2006). The vision of the ...
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On the use of phone-gram units in recurrent neural networks for language identification
... In our system, we will combine traditional language models [5] with more recent recurrent-based language models [6] trained with the output of three different ASR phone recognizers, and fused with an acoustic ...
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Deep neural network approaches for spanish Sentiment analysis of short texts
... Abstract. Sentiment Analysis has been extensively researched in the last years. While important theoretical and practical results have been obtained, there is still room for improvement. In particular, when short ...
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TítuloDeep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications
... Feedforward Neural Networks (DFNN), Deep Belief Networks (DBN), Deep AutoEncoder Networks, Deep Boltzmann Machines (DBM), Deep Convolutional Neural Networks (DCNN) and Deep ...
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Anomalous behaviour detection in video surveillance scenes
... Long-term Recurrent Convolutional Network (LRCN) architecture proposed by ...Convolutional Neural Networks (CNN) in visual features extraction and the strength of learning sequence information of ...
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Fast simulation of animal locomotion: lamprey swimming
... groups neural models of the lamprey into three classes: biophysical, connectionist and ...the neural controller as a chain of oscillators, the focus being on examining the couplings between ...dynamical ...
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Un experimento para crear conciencia en las personas acerca de los ataques de Ingeniería Social
... Un enfoque para detectar si una página es de falsa o no, por medio de un mecanismo de Deep Learning no supervisado, es el que se usa en (Zhao, Wang, Ma, & Cheng, 2019). En este trabajo, los investigadores usan gated ...
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Artificial neural networks applied to forecasting time series
... used neural network in time series forecasting has been the MLP (Multilayer Perceptron) (Bishop, ...other neural network models with respect to the MLP model in this type of task (Liu & Quek, ...
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Deep learning applied to cryptocurrencies prices one step forecast
... multilayer networks to recurrent networks, it is helpful to revise one of the_early ideas found in machine learning_and statistical models of ...a recurrent neural network shares ...
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Analysis and implementation of multiagent deep reinforcement learning algorithms for natural disaster monitoring with swarms of drones
... DQN tackles the problem of instability inherent to DRL in two ways. Firstly by random- izing the samples used in training through a mechanism known as experience replay, in order to reduce the effect of the correlation ...
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Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks
... Neural networks have already been used for a wide variety of tasks in medicine, from medical imaging, signal processing to biomedical ...convolutional neural networks, which are an evolution ...
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Glucose-Insulin regulator for type 1 diabetes using high order neural networks
... artificial neural networks (ANN) is ...discrete recurrent high order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas’ beta-cells ...
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Data Mining with Enhanced Neural Networks-CMMSE
... Abstract This paper presents a new method to extract knowledge from existing data sets, that is, to extract symbolic rules using the weights of an Artificial Neural Network. The method has been applied to a ...
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Deep Neural Networks to find genetics signatures
... Deep Neural Networks to find genetics signatures and is focused on the development of a bioinformatic tool oriented to identification of relationships between an attribute set and concret factor of interest ...
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Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.
... The most usual connection type in neural networks is the axo-dendritic connection. This connection is based on the fact that the axon of an afferent neuron is connected to another neuron via a synapse on a ...
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Path generation for a mobile robot using neural networks
... In this paper a methodology for navigation of a mobile robot in structured environments is presented. This methodology uses backpropagation neural network- to generate the optimal trajectory based of previously ...
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Using Neural Networks to Simulate the Alzheimer's Disease
... The calcium dysregulation hypothesis (CDH) is referred to a broad category of calcium- dependent neural processes related to the gradual impairment of cognitive abilities in AD, especially those related to ...
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Content Based Image Retrieval by Convolutional Neural Networks
... convolutional neural networks has been presented in this ...trained neural network in order to obtain the probabilities that an image represents an object ...
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TítuloShallow Recurrent Neural Network for Personality Recognition in Source Code
... our neural network—in a one-by- one fashion until we see no more significant improvement—, and also by introducing a new training criterion that consid- ers the correlation between ...
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