AIMC Topic: Neural Networks, Computer

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Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, particularly in the field of upper limbs for stroke patients. However, there is a lack of research in the lower limb area, and standardized open-sourc...

Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions.

Orthodontics & craniofacial research
OBJECTIVE: In many medical disciplines, facial attractiveness is part of the diagnosis, yet its scoring might be confounded by facial expressions. The intent was to apply deep convolutional neural networks (CNN) to identify how facial expressions aff...

Spiking generative adversarial network with attention scoring decoding.

Neural networks : the official journal of the International Neural Network Society
Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural n...

A syntactic evidence network model for fact verification.

Neural networks : the official journal of the International Neural Network Society
In natural language processing, fact verification is a very challenging task, which requires retrieving multiple evidence sentences from a reliable corpus to verify the authenticity of a claim. Although most of the current deep learning methods use t...

Optimization of Flavonoid Extraction from Flowers Using Ultrasonic Techniques: Predictive Modeling through Response Surface Methodology and Deep Neural Network and Biological Activity Assessment.

Molecules (Basel, Switzerland)
Understanding the optimal extraction methods for flavonoids from flowers (AMF) is crucial for unlocking their potential benefits. This study aimed to optimize the efficiency of flavonoid extraction from AMF. After comparing extraction methods, the u...

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.

Biomedical engineering online
BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated ...

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.

Scientific reports
To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep...

Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals.

Scientific reports
Standard clinical practice to assess fetal well-being during labour utilises monitoring of the fetal heart rate (FHR) using cardiotocography. However, visual evaluation of FHR signals can result in subjective interpretations leading to inter and intr...

A 3D ray traced biological neural network learning model.

Nature communications
Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another datase...

On energy complexity of fully-connected layers.

Neural networks : the official journal of the International Neural Network Society
The massive increase in the size of deep neural networks (DNNs) is accompanied by a significant increase in energy consumption of their hardware implementations which is critical for their widespread deployment in low-power mobile devices. In our pre...