Author: "Alharbi, Abir" / Topic: artificial neural networks - Searchworks@Jio Institute Digital Library Search Results

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Start Over Author "Alharbi, Abir" Topic artificial neural networks

Author: Alharbi, Abir and Alghahtani, Munirah
Subjects: *GENETIC algorithms, *ARTIFICIAL neural networks, *FEATURE extraction, *EVOLUTIONARY algorithms, *SAUDI Arabians, *MACHINE learning, *GESTATIONAL diabetes
Abstract: An automated system for early diagnosis of type 2-diabetes mellitus is proposed in this paper, by using the Extreme Learning Machine neural network for classification and the evolutionary genetic algorithms for feature extraction, to be employed on a real data set from Saudi Arabian patients. The dimension of the feature space is reduced by the genetic algorithms and only the effective features are selected. The data is then fed to an Extreme Learning Machine neural network for classification. Diabetes is a major health problem in both industrial and developing countries, and when it appears in pregnancies it may cause many complications, hence its early diagnosis is beneficial for both mother and fetus. Our hybrid algorithm, the GA-ELM algorithm, has produced an optimized diagnosis of type 2-diabetes patients and classified the data set with an accuracy of 97.5% and with six effective features, out of the original eight features given in the dataset. Moreover, comparisons of the GA-ELM method with other available methods were conducted and the results are promising. [ABSTRACT FROM AUTHOR]
Published: 2019
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Author: Alharbi, Abir
Subjects: *ARTIFICIAL neural networks, *MATHEMATICAL models, *BRAIN physiology, *NUMERICAL analysis, *NUMERICAL solutions to differential equations, *FINITE differences, *HOPFIELD networks
Abstract: Neural field equations (NFE) are used to model the activity of neurons in the brain, it is introduced from a single neuron 'integrate-and-fire model' starting point. The neural continuum is spatially discretized for numerical studies, and the governing equations are modeled as a system of ordinary differential equations. In this article the recurrent neural network approach is used to solve this system of ODEs. This consists of a technique developed by combining the standard numerical method of finite-differences with the Hopfield neural network. The architecture of the net, energy function, updating equations, and algorithms are developed for the NFE model. A Hopfield Neural Network is then designed to minimize the energy function modeling the NFE. Results obtained from the Hopfield-finite-differences net show excellent performance in terms of accuracy and speed. The parallelism nature of the Hopfield approaches may make them easier to implement on fast parallel computers and give them the speed advantage over the traditional methods. [ABSTRACT FROM AUTHOR]
Published: 2012
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Author: Alharbi, Abir
Subjects: *ARTIFICIAL neural networks, *DIFFERENTIAL equations, *PARTIAL differential equations, *FINITE differences, *ALGORITHMS
Abstract: While there already exists many analytical and numerical techniques for solving PDEs, this paper introduces an approach using artificial neural networks. The approach consists of a technique developed by combining the standard numerical method, finite-difference, with the Hopfield neural network. The method is denoted Hopfield-finite-difference (HFD). The architecture of the nets, energy function, updating equations, and algorithms are developed for the method. The HFD method has been used successfully to approximate the solution of classical PDEs, such as the Wave, Heat, Poisson and the Diffusion equations, and on a system of PDEs. The software Matlab is used to obtain the results in both tabular and graphical form. The results are similar in terms of accuracy to those obtained by standard numerical methods. In terms of speed, the parallel nature of the Hopfield nets methods makes them easier to implement on fast parallel computers while some numerical methods need extra effort for parallelization. [ABSTRACT FROM AUTHOR]
Published: 2010
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