AIMC Topic: Neural Networks, Computer

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Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation.

Neural networks : the official journal of the International Neural Network Society
Multi-view clustering has become an active topic in artificial intelligence. Yet, similar investigation for graph-structured data clustering has been absent so far. To fill this gap, we present a Multi-View Graph embedding Clustering network (MVGC). ...

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not unive...

Echo state network models for nonlinear Granger causality.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour,...

Robustness of convolutional neural networks to physiological electrocardiogram noise.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. Howe...

A Generalized Approach for Automatic 3-D Geometry Assessment of Blood Vessels in Transverse Ultrasound Images Using Convolutional Neural Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Accurate 3-D geometries of arteries and veins are important clinical data for diagnosis of arterial disease and intervention planning. Automatic segmentation of vessels in the transverse view suffers from the low lateral resolution and contrast. Conv...

Practical machine learning for disease diagnosis.

Cell reports methods
Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process...

Chip Appearance Defect Recognition Based on Convolutional Neural Network.

Sensors (Basel, Switzerland)
To improve the recognition rate of chip appearance defects, an algorithm based on a convolution neural network is proposed to identify chip appearance defects of various shapes and features. Furthermore, to address the problems of long training time ...

Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection.

Sensors (Basel, Switzerland)
This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to id...

Analysis of the Possibilities of Tire-Defect Inspection Based on Unsupervised Learning and Deep Learning.

Sensors (Basel, Switzerland)
At present, inspection systems process visual data captured by cameras, with deep learning approaches applied to detect defects. Defect detection results usually have an accuracy higher than 94%. Real-life applications, however, are not very common. ...

CMOS Implementation of ANNs Based on Analog Optimization of N-Dimensional Objective Functions.

Sensors (Basel, Switzerland)
The design of neural network architectures is carried out using methods that optimize a particular objective function, in which a point that minimizes the function is sought. In reported works, they only focused on software simulations or commercial ...