AIMC Topic: Neural Networks, Computer

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An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding.

NeuroImage. Clinical
Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG research area. Previous studies on binary EEG pathology decoding have mainly used the Temple University Hospital (TUH) Abnormal EEG Corpus (TUAB) which contains appr...

DBlink: dynamic localization microscopy in super spatiotemporal resolution via deep learning.

Nature methods
Single-molecule localization microscopy (SMLM) has revolutionized biological imaging, improving the spatial resolution of traditional microscopes by an order of magnitude. However, SMLM techniques require long acquisition times, typically a few minut...

AEAU-Net: an unsupervised end-to-end registration network by combining affine transformation and deformable medical image registration.

Medical & biological engineering & computing
Deformable medical image registration plays an essential role in clinical diagnosis and treatment. However, due to the large difference in image deformation, unsupervised convolutional neural network (CNN)-based methods cannot extract global features...

Ultrafast review of ambulatory EEGs with deep learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Interictal epileptiform discharges (IED) are hallmark biomarkers of epilepsy which are typically detected through visual analysis. Deep learning has shown potential in automating IED detection, which could reduce the burden of visual analy...

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.

PloS one
Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of t...

Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework.

International journal of injury control and safety promotion
With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering proc...

ATNAS: Automatic Termination for Neural Architecture Search.

Neural networks : the official journal of the International Neural Network Society
Neural architecture search (NAS) is a framework for automating the design process of a neural network structure. While the recent one-shot approaches have reduced the search cost, there still exists an inherent trade-off between cost and performance....

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image.

Chemistry & biodiversity
The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based...

A Benchmark Study of Graph Models for Molecular Acute Toxicity Prediction.

International journal of molecular sciences
With the wide usage of organic compounds, the assessment of their acute toxicity has drawn great attention to reduce animal testing and human labor. The development of graph models provides new opportunities for acute toxicity prediction. In this stu...

Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition.

Scientific reports
In the field of machine intelligence and ubiquitous computing, there has been a growing interest in human activity recognition using wearable sensors. Over the past few decades, researchers have extensively explored learning-based methods to develop ...