AIMC Topic: Neural Networks, Computer

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Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen.

Journal of dairy science
The objectives of this study were (1) to predict ruminal pH and ruminal ammonia and volatile fatty acid (VFA) concentrations by developing artificial neural networks (ANN) using dietary nutrient compositions, dry matter intake, and body weight as inp...

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.

Lancet (London, England)
BACKGROUND: Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to deve...

TGV Upsampling: A Making-Up Operation for Semantic Segmentation.

Computational intelligence and neuroscience
With the widespread use of deep learning methods, semantic segmentation has achieved great improvements in recent years. However, many researchers have pointed out that with multiple uses of convolution and pooling operations, great information loss ...

Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN.

Contrast media & molecular imaging
With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial ...

Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model.

Computational intelligence and neuroscience
We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network and extracting some additional features fro...

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.

PloS one
From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-s...

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures.

PloS one
Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction...

The GH-EXIN neural network for hierarchical clustering.

Neural networks : the official journal of the International Neural Network Society
Hierarchical clustering is an important tool for extracting information from data in a multi-resolution way. It is more meaningful if driven by data, as in the case of divisive algorithms, which split data until no more division is allowed. However, ...

Convergent Temperature Representations in Artificial and Biological Neural Networks.

Neuron
Discoveries in biological neural networks (BNNs) shaped artificial neural networks (ANNs) and computational parallels between ANNs and BNNs have recently been discovered. However, it is unclear to what extent discoveries in ANNs can give insight into...

A neural network-based software to recognise blepharospasm symptoms and to measure eye closure time.

Computers in biology and medicine
Blepharospasm (BSP) is an adult-onset focal dystonia with phenomenologically heterogeneous effects, including, but not limited to, blinks, brief or prolonged spasms, and a narrowing or closure of the eyelids. In spite of the clear and well-known symp...