Neural networks (Computer)
Top PDF Neural networks (Computer):
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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Detecting Simulated Attacks in Computer Networks Using Resilient Propagation Artificial Neural Networks
... the Neural Network. Meanwhile, the Neural Network is to detect simulated ...the Neural Network. These eight neural networks are built using the feedforward algorithm and trained using ...
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Artificial neural networks applied to forecasting time series
... cial neural networks (ANN) have aroused great interest in fi elds as diverse as biology, psychology, medicine, economics, mathematics, statistics and computer ...
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Pattern recognition in medical images using neural networks
... The usual practice is to look for a pattern recognition system which is able to learn in an adaptive way from the experiences of several discriminants, each of them corresponding to a specific purpose. In an automatic ...
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Content Based Image Retrieval by Convolutional Neural Networks
... Self-Organizing Neural Networks for Information Tech- nologies; and TIC-657, project name Self-organizing systems and robust estima- tors for video ...
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A neural networks benchmark for image classification
... The story of CV is short, as it started in the 60's, with the invention of the perceptron (Rosenblatt, 1962 [2] ). A perceptron is, roughly speaking, a simple linear classifier. Similarly to a transistor, a perceptron ...
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Road pollution estimation using static cameras and neural networks
... 53465-R, project name Video surveillance by active search of anomalous events and TIN2016-75097-P. It is also par- tially supported by the Autonomous Government of Andalu- sia (Spain) under projects TIC-6213, project ...
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Facial action unit detection with convolutional neural networks
... Convolutional Neural Networks (CNNs) are a branch of machine learning (supervised classification) based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph ...
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Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.
... new neural network architecture that can be compared to a Taylor analysis when dealing with ...Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the ...
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Time series modeling and synchronization using neural networks
... years, neural networks have found interesting applications in the field of time series modeling and ...of neural networks when comparted with chaotic ...and neural approximate models. ...
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A parallel approach for backpropagation learning of neural networks
... backpropagation neural networks using a pattern partitioning scheme with a set-training ...small neural net selected for the testing case a substantial acceleration ranging from 4 to more than ten ...
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Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks
... artificial neural networks for their iden- tification as a red blood cell is ...of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals ...
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A Brief Introduction to Neural Networks
... of neural networks, it would be use- ful to briefly discuss the biology of neu- ral networks and the cognition of living organisms – the reader may skip the fol- lowing chapter without missing any ...
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Acoustic Fingerprint Recognition Using Artificial Neural Networks
... The technological and scientific advances continue developing but despite the high value that acoustic fingerprints vessels have, publications regarding these applications are limited. The researches mentioned in this ...
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Data Mining with Enhanced Neural Networks-CMMSE
... four networks have been ...4 neural networks were constructed: one for each set of patterns S 1 · · · S 4 obtained, which outputs are I 1 · · · I 4 , ...
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Using Neural Networks to Simulate the Alzheimer's Disease
... In this simulation, a small artificial neural network of 30 sparsely connected neurons was studied. The synaptic weights of each one of these neurons were modified according to the presynaptic learning rule Eq. ...
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Potential of neural networks for structural damage localization
... In recent years, several authors (e.g., [1-3]) have concluded that structural damage detection is a problem of pattern recognition, in which a classification is made as function of physical properties of a system. Within ...
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Distant galaxies analysis with deep neural networks
... • Design and training of 4 different neural networks • Prediction of Redshift, Stellarity, Stellar Mass and Spectral Type of galaxies • Data from Alhambra Survey • Comparison of Predicte[r] ...
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Using artificial neural networks in estimating wood resistance
... Artificial Neural Networks in estimating the properties of wood ...Perceptron networks were also employed, training 100 of them for each of the evaluated ...Artificial Neural Networks ...
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Glucose-Insulin regulator for type 1 diabetes using high order neural networks
... artificial neural networks (ANN) is ...order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas’ beta-cells behavior of a virtual ...nonlinear ...
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