AIMC Topic: Neural Networks, Computer

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Compressing deep graph convolution network with multi-staged knowledge distillation.

PloS one
Given a trained deep graph convolution network (GCN), how can we effectively compress it into a compact network without significant loss of accuracy? Compressing a trained deep GCN into a compact GCN is of great importance for implementing the model ...

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds.

PloS one
Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs' feeding trails, which are unvegetated winding tracks left after feeding, have been used ...

Identification of disease genes and assessment of eye-related diseases caused by disease genes using JMFC and GDLNN.

Computer methods in biomechanics and biomedical engineering
Early detection of disease genes helps humans to recover from certain gene-related diseases, like genetic eye diseases. This work identifies the possibility of eye diseasesfor the disease genes utilizing a Gaussian-activation function (G)-centric dee...

Fast and scalable earth texture synthesis using spatially assembled generative adversarial neural networks.

Journal of contaminant hydrology
The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process. Accordingly, generatin...

Intermittent control for finite-time synchronization of fractional-order complex networks.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the finite-time synchronization problem for fractional-order complex dynamical networks (FCDNs) with intermittent control. Using the definition of Caputo's fractional derivative and the properties of Beta function, the Ca...

Active sensing with artificial neural networks.

Neural networks : the official journal of the International Neural Network Society
The fitness of behaving agents depends on their knowledge of the environment, which demands efficient exploration strategies. Active sensing formalizes exploration as reduction of uncertainty about the current state of the environment. Despite strong...

What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research.

Journal of the mechanical behavior of biomedical materials
Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary scientific domains where machines are provided with an approximation of human intelligence. The conjecture is that machines are able to learn from existing examp...

Multi-Modal Residual Perceptron Network for Audio-Video Emotion Recognition.

Sensors (Basel, Switzerland)
Emotion recognition is an important research field for human-computer interaction. Audio-video emotion recognition is now attacked with deep neural network modeling tools. In published papers, as a rule, the authors show only cases of the superiority...

Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation.

Sensors (Basel, Switzerland)
As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly aff...

Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model.

Sensors (Basel, Switzerland)
AIM: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run.