AIMC Topic: Neural Networks, Computer

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fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.

NeuroImage
Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area...

A Time Series Data Filling Method Based on LSTM-Taking the Stem Moisture as an Example.

Sensors (Basel, Switzerland)
In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verif...

Dynamic event-based state estimation for delayed artificial neural networks with multiplicative noises: A gain-scheduled approach.

Neural networks : the official journal of the International Neural Network Society
This study is concerned with the state estimation issue for a kind of delayed artificial neural networks with multiplicative noises. The occurrence of the time delay is in a random way that is modeled by a Bernoulli distributed stochastic variable wh...

Stacking models for nearly optimal link prediction in complex networks.

Proceedings of the National Academy of Sciences of the United States of America
Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missin...

Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function.

PloS one
With the widespread use of biometric authentication comes the exploitation of presentation attacks, possibly undermining the effectiveness of these technologies in real-world setups. One example takes place when an impostor, aiming at unlocking someo...

Are open set classification methods effective on large-scale datasets?

PloS one
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the ability to recognize inputs from o...

A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
PURPOSE:  To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning.

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network.

European radiology
OBJECTIVES: The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.

Fast Wearable Sensor-Based Foot-Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping.

Sensors (Basel, Switzerland)
Classification of foot-ground contact phases, as well as the swing phase is essential in biomechanics domains where lower-limb motion analysis is required; this analysis is used for lower-limb rehabilitation, walking gait analysis and improvement, an...