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Cardiac Arrhythmia Disorders Detection with Deep Learning Models of the electrocardiogram classifier is proposed, as an ensemble of a two-dimensional convolutional neural network and a

Генерация текстур с использованием свёрточных нейронных сетей representation of an original image in convolution neural network which takes into account the complex data

Prediction of protein-protein interaction with cosine matrices/shift invariance and self-correcting behavior. We developed a fully convolutional neural network architecture

НЕЙРОННАЯ СЕТЬ ДЛЯ ГЕНЕРАЦИИ 3D-КОНТЕНТА ОБРАЗОВАТЕЛЬНЫХ ПЛАТФОРМ specific algorithms and techniques, such as convolutional neural networks (CNNs) and generative

Классификация лейкоцитов с использованием сверточных нейронных сетей для изображений с низким разрешением, and the Convolutional Neural Network. The proposed classifiers were compared using experiments carried out on low

Editorial: Toward and Beyond Human-Level AIconvolutional neural network

Разработка системы мониторинга состояния покрытия проезжей частиconvolutional neural networks

Small object detection method development of deep convolutional neural networks (CNN)/ When it comes to small objects? the accuracy of deep

Application of neural networks to the analysis of the resistance of the human immunodeficiency virus to HIV reverse transcriptase inhibitors work, we used two topologies of neural networks: multilayer perceptron and convolutional neural network

Retinal Image Analysis for Diabetic Retinopathy Grading: Preliminarily Results developed a method for retina image analysis, based on image preprocessing stage and deep neural network

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