Neural networks : the official journal of the International Neural Network Society
May 20, 2020
In this paper, the existence, uniqueness and stability criteria of solutions for Hopfield neural networks with discrete and distributed delays (DDD HNNs) are investigated by the definitions of three kinds of generalized norm (ΞΎ-norm). A general DDD H...
Neural networks : the official journal of the International Neural Network Society
May 19, 2020
This paper mainly studies quasi-bipartite synchronization (QBPS) of signed delayed neural networks (SDNNs) under impulsive effects, in which the nodes have cooperative as well as antagonistic interactions. It is assumed that disturbance occurs in the...
A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends conv...
Neural networks : the official journal of the International Neural Network Society
May 15, 2020
The actuator of any physical control systems is constrained by amplitude and energy, which causes the control systems to be inevitably affected by actuator saturation. In this paper, impulsive synchronization of coupled delayed neural networks with a...
At present, many researchers see hope that artificial intelligence, machine learning in particular, will improve several aspects of the everyday life for individuals, cities and whole nations alike. For example, it has been speculated that the so-cal...
The ventral visual stream is known to be organized hierarchically, where early visual areas processing simplistic features feed into higher visual areas processing more complex features. Hierarchical convolutional neural networks (CNNs) were largely ...
Neural networks : the official journal of the International Neural Network Society
May 4, 2020
Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data. However, as GNNs typically define the graph convolution ...
Journal of vascular and interventional radiology : JVIR
May 4, 2020
PURPOSE: To demonstrate that random forest models trained on a large national sample can accurately predict relevant outcomes and may ultimately contribute to future clinical decision support tools in IR.
BACKGROUND & AIMS: One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. ...
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Apr 28, 2020
INTRODUCTION: The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction o...
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