AIMC Topic: Neural Networks, Computer

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Densely connected U-Net retinal vessel segmentation algorithm based on multi-scale feature convolution extraction.

Medical physics
PURPOSE: The segmentation results of retinal blood vessels have a significant impact on the automatic diagnosis of various ophthalmic diseases. In order to further improve the segmentation accuracy of retinal vessels, we propose an improved algorithm...

Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder-Decoder Network.

Sensors (Basel, Switzerland)
Fatigue cracks are critical types of damage in steel structures due to repeated loads and distortion effects. Fatigue crack growth may lead to further structural failure and even induce collapse. Efficient and timely fatigue crack detection and segme...

Robust Korean License Plate Recognition Based on Deep Neural Networks.

Sensors (Basel, Switzerland)
With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in...

Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network.

Scientific reports
Cervical ossification of the posterior longitudinal ligament (OPLL) is a contributing factor to spinal cord injury or trauma-induced myelopathy in the elderly. To reduce the incidence of these traumas, it is essential to diagnose OPLL at an early sta...

Exponential passivity of discrete-time switched neural networks with transmission delays via an event-triggered sliding mode control.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the exponential passivity of discrete-time switched neural networks (DSNNs) with transmission delays via an event-triggered sliding mode control (SMC). Firstly, a novel discrete-time switched SMC scheme is constructed on the b...

Periodicity and multi-periodicity generated by impulses control in delayed Cohen-Grossberg-type neural networks with discontinuous activations.

Neural networks : the official journal of the International Neural Network Society
This paper discusses the periodicity and multi-periodicity in delayed Cohen-Grossberg-type neural networks (CGNNs) possessing impulsive effects, whose activation functions possess discontinuities and are allowed to be unbounded or nonmonotonic. Based...

Valorization of groundnut shell via pyrolysis: Product distribution, thermodynamic analysis, kinetic estimation, and artificial neural network modeling.

Chemosphere
Pyrolysis of agricultural biomass is a promising technique for producing renewable energy and effectively managing solid waste. In this study, groundnut shell (GNS) was processed at 500 °C in an inert gas atmosphere with a gas flow rate and a heating...

Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples.

Journal of microscopy
Identifying nuclei is a standard first step when analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not u...

Coordination of top-down influence on V1 responses by interneurons and brain rhythms.

Bio Systems
Top-down processing in neocortex underlies functions such as prediction, expectation, and attention. Visual systems have much feedback connection that carries information of behavioral context. Top-down signals along feedback pathways modulate the re...

Treatment initiation prediction by EHR mapped PPD tensor based convolutional neural networks boosting algorithm.

Journal of biomedical informatics
Electronic health records contain patient's information that can be used for health analytics tasks such as disease detection, disease progression prediction, patient profiling, etc. Traditional machine learning or deep learning methods treat EHR ent...