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 сетейБакунова, О. М.,
Бакунов, А. М.,
Калитеня, И. Л.,
Бухта, Д. В.,
Петрик, А. И.,
Лобковская, В. В. 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
Small object detection method development of deep
convolutional neural networks (CNN)/ When it comes to small objects? the accuracy of deep
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