AIMC Topic: Neural Networks, Computer

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GARAT: Generative Adversarial Learning for Robust and Accurate Tracking.

Neural networks : the official journal of the International Neural Network Society
Object tracking by the Siamese network has gained its popularity for its outstanding performance and considerable potential. However, most of the existing Siamese architectures are faced with great difficulties when it comes to the scenes where the t...

Dual Global Enhanced Transformer for image captioning.

Neural networks : the official journal of the International Neural Network Society
Transformer-based architectures have shown great success in image captioning, where self-attention module can model source and target interaction (e.g., object-to-object, object-to-word, word-to-word). However, the global information is not explicitl...

On minimal representations of shallow ReLU networks.

Neural networks : the official journal of the International Neural Network Society
The realization function of a shallow ReLU network is a continuous and piecewise affine function f:R→R, where the domain R is partitioned by a set of n hyperplanes into cells on which f is affine. We show that the minimal representation for f uses ei...

Human-computer interaction based interface design of intelligent health detection using PCANet and multi-sensor information fusion.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: At present, because health monitoring using human-computer interaction (HCI) has become a demand in society, an intelligent health detector with HCI characteristics is urgently needed. Our device and software framework can p...

Graph neural network approaches for drug-target interactions.

Current opinion in structural biology
Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non-Euclidian da...

Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: The purpose of this study was to evaluate the accuracy of auto-identification of the posteroanterior (PA) cephalometric landmarks using the cascade convolution neural network (CNN) algorithm and PA cephalogram images of a different qual...

Informing deep neural networks by multiscale principles of neuromodulatory systems.

Trends in neurosciences
Our brains have evolved the ability to configure and adapt their processing states to match the unique challenges of acting and learning in diverse environments and behavioral contexts. In biological nervous systems, such state specification and adap...

The effects of temperature on the dynamics of the biological neural network.

Journal of biological physics
The nerve cells are responsible for transmitting messages through the action potential, which generates electrical stimulation. One of the methods and tools of electrical stimulation is infrared neural stimulation (INS). Since the mechanism of INS is...

ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.

Journal of computational biology : a journal of computational molecular cell biology
Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under predict...

E-TBNet: Light Deep Neural Network for Automatic Detection of Tuberculosis with X-ray DR Imaging.

Sensors (Basel, Switzerland)
Currently, the tuberculosis (TB) detection model based on chest X-ray images has the problem of excessive reliance on hardware computing resources, high equipment performance requirements, and being harder to deploy in low-cost personal computer and ...