AIMC Topic: Neural Networks, Computer

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A Radar Signal Recognition Approach via IIF-Net Deep Learning Models.

Computational intelligence and neuroscience
In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general so...

[The use of artificial neural networks to classify the social vulnerability of municipalities in Rio Grande do Norte State, Brazil].

Cadernos de saude publica
The objective was to apply artificial neural networks to classify municipalities (counties) in Rio Grande do Norte State, Brazil, according to their social vulnerability. This was an ecological study using 17 variables that reflected epidemiological,...

Automated feature detection in dental periapical radiographs by using deep learning.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The aim of this study was to investigate automated feature detection, segmentation, and quantification of common findings in periapical radiographs (PRs) by using deep learning (DL)-based computer vision techniques.

Neurodynamical classifiers with low model complexity.

Neural networks : the official journal of the International Neural Network Society
The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an upper bound on the Vapnik-Chervonenkis (VC) dimension. The VC dimension measures the capacity or model complexity of a learning machine. Vapnik's ri...

MGAT: Multi-view Graph Attention Networks.

Neural networks : the official journal of the International Neural Network Society
Multi-view graph embedding is aimed at learning low-dimensional representations of nodes that capture various relationships in a multi-view network, where each view represents a type of relationship among nodes. Multitudes of existing graph embedding...

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems.

Neural networks : the official journal of the International Neural Network Society
We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and ac...

ncRDeep: Non-coding RNA classification with convolutional neural network.

Computational biology and chemistry
A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involved in many biological processes, diseases, and cancers. Numerous ncRNAs have been identified and classified with high throughput sequencing technology....

Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT.

Medical physics
PURPOSE: Deep learning-based whole-heart segmentation in coronary computed tomography angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients und...

Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units.

Biosensors
Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer durin...

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning.

Sensors (Basel, Switzerland)
Physical findings of auscultation cannot be quantified at the arteriovenous fistula examination site during daily dialysis treatment. Consequently, minute changes over time cannot be recorded based only on subjective observations. In this study, we s...