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Analysis of EEG records in an epileptic patient using wavelet transform
TLDR
In this research, discrete Daubechies and harmonic wavelets are investigated for analysis of epileptic EEG records and the capability of this mathematical microscope to analyze different scales of neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain signals. Expand
A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy
TLDR
It is observed that while there may not be significant differences in the values of the parameters obtained from the original EEG, differences may be identified when the parameters are employed in conjunction with specific EEG subbands. Expand
Neural Networks in Civil Engineering: 1989–2000
The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archivalExpand
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
TLDR
In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Expand
Neural Network Models for Earthquake Magnitude Prediction Using Multiple seismicity Indicators
TLDR
This research provides a scientific approach for evaluating the short-term seismic hazard potential of a region and yields the best prediction accuracies compared with LMBP and RBF networks. Expand
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection
TLDR
A new Multi-Spiking Neural Network (MuSpiNN) model is presented in which information from one neuron is transmitted to the next in the form of multiple spikes via multiple synapses and the model and learning algorithm employ the heuristic rules and optimum parameter values presented by the authors in a recent paper that improved the efficiency of the original single-spiking SNN model by two orders of magnitude. Expand
Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems
TLDR
Perceptron Learning with a Hidden Layer and an Object-Oriented Backpropagation Learning Model and Adaptive Conjugate Gradient Learning Algorithm for Efficient Training of Neural Networks. Expand
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella ofExpand
Fractality analysis of frontal brain in major depressive disorder.
TLDR
An investigation of the frontal brain of MDD patients using the wavelet-chaos methodology and Katz's and Higuchi's fractal dimensions (KFD and HFD) as measures of nonlinearity and complexity concludes that HFD is more discriminative than HFD of the gamma band for discriminating MDD and non-MDD participants. Expand
Augmented Lagrangian genetic algorithm for structural optimization
This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is noExpand
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