[PDF] Top 20 Biosignals learning and synthesis using deep neural networks
Has 10000 "Biosignals learning and synthesis using deep neural networks" found on our website. Below are the top 20 most common "Biosignals learning and synthesis using deep neural networks".
Biosignals learning and synthesis using deep neural networks
... ing and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ...data ... See full document
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Lesion Classification in Mammograms Using Convolutional Neural Networks and Transfer Learning
... extracted and used as input of an Support Vec- tor Machine ...layers and a fully connected layer and DeCAF - a pre-trained model with ImageNet) obtaining AUC values of ...0.860 and 0.836, ... See full document
9
Automatic transcription of music using deep learning techniques
... approach using artificial neural ...artificial neural networks with the traditional ...classical deep learning technique was preferable in order to have a baseline ...technique ... See full document
116
Deep neural networks for image quality: a comparison study for identification photos
... first. Using a supervised learning approach, the model is fed with training data from which the correct output for each training sample is already ...predictions and the desired outputs, which ... See full document
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Lesion classification in mammograms using convolutional neural networks and transfer learning
... dataset and applied a fine-tuning operation on the trained deep CNN model in LSVRC (dataset containing more than one million labelled natural images), in order to extract middle-level and high-level ... See full document
8
Developing deep learning computational tools for cancer using omics data
... are neural networks similar to shallow ANN except they have more than one hidden ...a neural network we can gain more degrees of freedom, having the capacity to fit better the training data, even for ... See full document
106
Retinal image quality assessment using deep convolutional neural networks
... weight and bias parameters, learning algorithms also require some additional parameters, called hyperparameters that carry out the learning process, like the learning ...rate. Learning ... See full document
182
Deep learning for single image super-resolution using residual image learning and multiple degradations
... the deep model convergence speed becomes a critical issue during ...very deep convolutional network (VDSR) proposed by (KIM; LEE; LEE, 2016b) which was based on residual learning (HE et ...gradient ... See full document
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A COMPARATIVE ANALYSIS OF WEB INFORMATION EXTRACTION TECHNIQUES DEEP LEARNING vs. NAÏVE BAYES vs. BACK PROPAGATION NEURAL NETWORKS IN WEB DOCUMENT EXTRACTION
... through deep learning architecture. Deep learning is a fairly new space of machine learning and neural network ...utilizes neural networks having several ... See full document
7
Deep learning model combination and regularization using convolutional neural networks
... Global Average Pooling is a strategy to replace the traditional fully connected layers in CNN. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. ... See full document
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Numerical Solution of PDE’s Using Deep Learning
... All the implementations were done using TensorFlow and used only CPU’s. In spite being trained using a personal use computer (MacBook Pro 2015, Core i5 2.7Ghz) most trainings did not require more ... See full document
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Deep-PRWIS: Periocular Recognition Without the Iris and Sclera Using Deep Learning Frameworks
... popular deep learning architec- tures for image classification: Convolution Neural Networks (CNNs), which are a biologically inspired variant of multilayer perceptron networks (MLPs) ... See full document
9
Spatial predictive mapping using artificial neural networks
... analyse and predict forest infections by Ips typographus ...measures and possibly climate information should be ...qualitative and quantitative analysis of infections was possible with ANN and ... See full document
8
RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.
... CLR and KELLER) on the task of reverse engineering the gene network topology, in terms of the associated MCC ...patterns and the direction of the interaction, as shown on selected synthetic toy ...accuracy ... See full document
19
Ribosome binding site recognition using neural networks
... second and third models allowed an economic representation of a highly de- generated biological ...the networks, characterizing a non-conventional ...the learning processes of the networks to ... See full document
7
Res. Biomed. Eng. vol.32 número3
... diagnosis using morphological features for classifying breast lesions on ...analysis using support vector machines. Neural Computing & ...cross-validation and bootstrap for accuracy ... See full document
10
Meetei Mayek Unicode Modeling Using Swarm Intelligence and Neural Networks
... D. NEURAL NETWORK ARCHITECTURE: The feed forward neural network architecture with back propagation learning is used for recognition of the PSOFCM segmented ...done using by the three layers on ... See full document
9
Fuzzy nonlinear regression using artificial neural networks
... Since there is a variation in temperature throughout the day, instead of representing it by its average value, it would be more practical to represent it by a fuzzy number having support from minimum temperature to maxi- ... See full document
11
A Portuguese Flora Identification Tool Using Deep Learning
... hits, and now more than ever [28], changing weather patterns, pollution, invasive species, and over-exploitation pose as the main threats to its balance ...financial and human resources available to ... See full document
75
N EURALN ETWORKS C RISTIANOR
... 0 and 1) quantifies the inertia of the weights, forcing them to change direction contrary to the one which reduces the ...[12] and the most effectively ... See full document
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