convolutional neural networks (CNNs)
A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages
... on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 ...popular CNNs trained from ...
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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 ...Recently, convolutional neural networks, which ...
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Vehicle Type Detection by Convolutional Neural Networks
... of Convolutional Neural Network (CNN) has been used, namely AlexNet ...employ CNNs (AlexNet in particular) to object recognition, provides the neural network with an input image where the ...
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Content Based Image Retrieval by Convolutional Neural Networks
... Some other recent approaches are interested in similarity measurement. For example, ELALMI [19] proposed a model for CBIR where he injected a matching strategy to measure similarity between images, the proposed model ...
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Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks
... techniques, CNNs have been successfully applied to object recognition ...deep neural networks in some selected areas [13], [14], particularly in medical image analysis [15], [16], where CNNs ...
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Homography estimation with deep convolutional neural networks by random color transformations
... Most classic approaches to homography estimation are based on the filtering of out- liers by means of the RANSAC method. New proposals include deep convolutional neu- ral networks. Here a new method for ...
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Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms
... learning convolutional neural networks is ...of networks is considered in order to obtain an enhanced recognition per- formance of the system by the consensus of the networks of the ...
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Classification of echocardiography images using Convolutional Neural Network to assist Kawasaki disease diagnosis
... today Convolutional Neu- ral Networks are the most common solution and the architecture is still being researched and ...a Convolutional Neural Networks was introduced in the 1980’s by ...
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Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images
... One of the advantages of using a p-norm, with p < 2, is that it can allow reducing the effect of outliers in a minimization problem. Noise and artifacts in the images of the training set for MR superresolution are ...
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Aprendizaje profundo aplicado a problemas de predicción de supervivencia en cáncer
... Cancer claimed 18.1 millions deaths worldwide in 2018 and $ 87.8 billion for health-care in 2014 in USA. The tremendous impact this disease supposes worldwide, combined with the increasingly availability of genomic and ...
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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 ...
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Anomalous behaviour detection in video surveillance scenes
... Recurrent Convolutional Network (LRCN) architecture proposed by ...of Convolutional Neural Networks (CNN) in visual features extraction and the strength of learning sequence information of ...
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Deep learning applied to cryptocurrencies prices one step forecast
... investigated_the_use_of neural networks for one-step time series forecasting_on cryptocurrencies ...and Convolutional Neural Networks (CNN) models are put to test to see if binary ...
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Implementing YOLO algorithm for real time object detection on embedded system
... artificial neural networks known as Convolutional Neural ...the networks that have gained recognition in this area of computer vision is YOLO, an algorithm that is a collection of ...
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Deep Neural Networks to find genetics signatures
... • Deep Neural Networks handling: corresponds to functional requirement 5. For implement these functional groups I’m ging to take into acount the non-functional requirements. The two first requirements ...
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Glucose-Insulin regulator for type 1 diabetes using high order neural networks
... A neural network is a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experimental knowledge and making it available for use ...
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Clasificación automática de calidad embrionaria en embriones en estadio de Blastocisto, mediante una Convolutional Neural Network (CNN)
... El desarrollo de las nuevas tecnologías enmarcadas en el concepto Inteligencia Artificial (IA)(7) deben ser evaluadas en diferentes contextos como los laboratorios de reproducción humana. Una rama de la IA son los ...
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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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Método de predicción de velocidad para una red de tráfico de gran escala usando una convolutional neural network con separable convolution
... memory neural network (LSTM-NN) for traffic prediction and demonstrates that LSTM-NN is superior to other neural networks in both accuracy and stability in terms of traffic speed prediction, but it ...
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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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