A recurrent neural network (RNN) is a machine learning model that learns the relationship between elements of an input series, in addition to inferring a relationship between the data input to the model and target output. Memory augmentation allows t...
Neural networks : the official journal of the International Neural Network Society
Jun 22, 2022
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection problems where, according to recent and concurring experimental evaluati...
PURPOSE: Deep neural networks (DNNs) have been widely applied in medical image classification, benefiting from its powerful mapping capability among medical images. However, these existing deep learning-based methods depend on an enormous amount of c...
In the process of converting food-processing by-products to value-added ingredients, fine grained control of the raw materials, enzymes and process conditions ensures the best possible yield and economic return. However, when raw material batches lac...
Proceedings of the National Academy of Sciences of the United States of America
Jun 22, 2022
The high-dimensional character of most biological systems presents genuine challenges for modeling and prediction. Here we propose a neural network-based approach for dimensionality reduction and analysis of biological gene expression data, using, as...
Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recu...
This study proposes a new intelligent diagnostic method for bearing faults in rotating machinery. The method uses a combination of nonlinear mode decomposition based on the improved fast kurtogram, gramian angular field, and convolutional neural netw...
The detection of moving objects is one of the key problems in the field of computer vision. It is very important to detect moving objects accurately and rapidly for automatic driving. In this paper, we propose an improved moving object detection meth...
The timely identification of cohort participants at higher risk for attrition is important to earlier interventions and efficient use of research resources. Machine learning may have advantages over the conventional approaches to improve discriminati...
The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U[Formula: see text]-Net. Two statistical methods for deep neural networks are utilized: the bootstrap...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.