AIMC Topic: Neural Networks, Computer

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Neighborhood preserving neural network for fault detection.

Neural networks : the official journal of the International Neural Network Society
A novel statistical feature extraction method, called the neighborhood preserving neural network (NPNN), is proposed in this paper. NPNN can be viewed as a nonlinear data-driven fault detection technique through preserving the local geometrical struc...

Determination of mammographic breast density using a deep convolutional neural network.

The British journal of radiology
OBJECTIVE: High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue a...

A Parallel Workflow Pattern Modeling Using Spiking Neural P Systems With Colored Spikes.

IEEE transactions on nanobioscience
Spiking neural P systems, otherwise known as named SN P systems, are bio-inspired parallel and distributed neural-like computing models. Due to the spiking behavior, SN P systems fall into the category of spiking neural networks, and are considered t...

Reachable set estimation for Markovian jump neural networks with time-varying delay.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the reachable set estimation for Markovian jump neural networks with time-varying delay and bounded peak inputs. The objective is to find a description of a reachable set that is containing all reachable states starting f...

ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation.

Computers in biology and medicine
Electrocardiogram (ECG) is gaining increased attention as a biometric method in a wide range of applications, such as access control and security/privacy requirements. The majority of reported investigations using the ECG biometric method are usually...

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.

BMC bioinformatics
BACKGROUND: Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers.

A study of intracortical porosity's area fractions and aspect ratios using computer vision and pulse-coupled neural networks.

Medical & biological engineering & computing
Employing computer vision (CV) and optimized pulse-coupled neural networks (PCNN), this work automatically quantifies the geometrical attributes of intracortical bone porosity (namely lacunae and canaliculi (L-C), Haversian canals, and resorption cav...

Emergence of spontaneous assembly activity in developing neural networks without afferent input.

PLoS computational biology
Spontaneous activity is a fundamental characteristic of the developing nervous system. Intriguingly, it often takes the form of multiple structured assemblies of neurons. Such assemblies can form even in the absence of afferent input, for instance in...

Learning in the machine: Recirculation is random backpropagation.

Neural networks : the official journal of the International Neural Network Society
Learning in physical neural systems must rely on learning rules that are local in both space and time. Optimal learning in deep neural architectures requires that non-local information be available to the deep synapses. Thus, in general, optimal lear...

Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks.

Environmental monitoring and assessment
Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil...