AIMC Topic: Neural Networks, Computer

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Recognition of normal-abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients.

Physiological measurement
UNLABELLED: Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals.

The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction.

PloS one
Recently, echo state network (ESN) has attracted a great deal of attention due to its high accuracy and efficient learning performance. Compared with the traditional random structure and classical sigmoid units, simple circle topology and leaky integ...

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.

NeuroImage
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the creden...

Mixed Neural Network Approach for Temporal Sleep Stage Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monit...

Artificial neural networks to evaluate organic and inorganic contamination in agricultural soils.

Chemosphere
The assessment of organic and inorganic contaminants in agricultural soils is a difficult challenge due to the large-scale dimensions of the areas under investigation and the great number of samples needed for analysis. On-site screening techniques, ...

Information reduction in a reverberatory neuronal network through convergence to complex oscillatory firing patterns.

Bio Systems
Dynamics of a reverberating neural net is studied by means of computer simulation. The net, which is composed of 9 leaky integrate-and-fire (LIF) neurons arranged in a square lattice, is fully connected with interneuronal communication delay proporti...

Efficient construction of sparse radial basis function neural networks using L-regularization.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the construction of sparse radial basis function neural networks (RBFNNs) for classification problems. An efficient two-phase construction algorithm (which is abbreviated as TPCLR for simplicity) is proposed by using L regular...

Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints.

IEEE transactions on cybernetics
The control problem of an uncertain n -degrees of freedom robotic manipulator subjected to time-varying output constraints is investigated in this paper. We describe the rigid robotic manipulator system as a multi-input and multi-output nonlinear sys...

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

PLoS computational biology
UNLABELLED: Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered...

Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

Interdisciplinary sciences, computational life sciences
BACKGROUND: The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning scheme...