AIMC Topic: Neural Networks, Computer

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The Deep Learning Generative Adversarial Random Neural Network in data marketplaces: The digital creative.

Neural networks : the official journal of the International Neural Network Society
Generative Adversarial Networks (GANs) have been proposed as a method to generate multiple replicas from an original version combining a Discriminator and a Generator. The main applications of GANs have been the casual generation of audio and video c...

BrainS: Customized multi-core embedded multiple scale neuromorphic system.

Neural networks : the official journal of the International Neural Network Society
Research on modeling and mechanisms of the brain remains the most urgent and challenging task. The customized embedded neuromorphic system is one of the most effective approaches for multi-scale simulations ranging from ion channel to network. This p...

The neuroconnectionist research programme.

Nature reviews. Neuroscience
Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have been not only lauded as the current best models of information processing ...

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.

Sensors (Basel, Switzerland)
With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health ...

CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization.

BMC bioinformatics
BACKGROUND: Circular RNAs (circRNAs) play a significant role in some diseases by acting as transcription templates. Therefore, analyzing the interaction mechanism between circRNA and RNA-binding proteins (RBPs) has far-reaching implications for the p...

CeLNet: a correlation-enhanced lightweight network for medical image segmentation.

Physics in medicine and biology
. Convolutional neural networks have been widely adopted for medical image segmentation with their outstanding feature representation capabilities. As the segmentation accuracy gets constantly updated, the complexity of networks increases as well. Co...

Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice simulated breast CT images.

Physics in medicine and biology
In this work, we propose a convolutional neural network (CNN)-based multi-slice ideal model observer using transfer learning (TL-CNN) to reduce the required number of training samples.To train model observers, we generate simulated breast CT image vo...

Principal component analysis-multivariate adaptive regression splines (PCA-MARS) and back propagation-artificial neural network (BP-ANN) methods for predicting the efficiency of oxidative desulfurization systems using ATR-FTIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Oxidative desulfurization (ODS) of diesel fuels has received attention in recent years due to mild working conditions and effective removal of the aromatic sulfur compounds. There is a need for rapid, accurate, and reproducible analytical tools to mo...

Forward propagation dropout in deep neural networks using Jensen-Shannon and random forest feature importance ranking.

Neural networks : the official journal of the International Neural Network Society
Dropout is a mechanism to prevent deep neural networks from overfitting and improving their generalization. Random dropout is the simplest method, where nodes are randomly terminated at each step of the training phase, which may lead to network accur...