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20 results on '"Alharbi, Abir"'

3. Understanding the Semantics of the Genetic Algorithm in Dynamic Environments : A Case Study Using the Shaky Ladder Hyperplane-Defined Functions

Author

Alharbi, Abir, Rand, William, Riolo, Rick, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Giacobini, Mario, editor

Published
2007
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8. A genetic-ELM neural network computational method for diagnosis of the Parkinson disease gait dataset.

Author

Alharbi, Abir

Subjects
*PARKINSON'S disease, *DIAGNOSIS, *DIAGNOSIS methods, *MACHINE learning, *FEATURE selection, *SUBTHALAMIC nucleus, *GAIT in humans
Abstract

A hybrid computational method based on the extreme learning machine (ELM) neural network for classification and the evolutionary genetic algorithms (GA) for feature selection is presented in this paper. The dimension of the feature space is reduced by the GA in this scheme and only the appointed features are selected. The data is then passed to an ELM neural network for the classification phase. An automated system for the diagnosis of Parkinson's Disease (PD) based on gait data set is proposed by using the GA-ELM method. PD is a neurodegenerative disease that may cause change in the central nervous causing disturbance to the gait cycle duration. Our hybrid GA-ELM algorithm has produced an optimized diagnosis of PD from healthy subjects given in the gait dataset with accuracy 93.5%, and with five effective features that reduce the original dataset dimension. [ABSTRACT FROM AUTHOR]

Published
2020
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12. Using Genetic Algorithm and ELM Neural Networks for Feature Extraction and Classification of Type 2-Diabetes Mellitus.

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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18. An Artificial Neural Networks Method for Solving Partial Differential Equations.

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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19. Understanding the Semantics of the Genetic Algorithm in Dynamic Environments.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Giacobini, Mario, Alharbi, Abir, Rand, William, and Riolo, Rick

Abstract

Researchers examining genetic algorithms (GAs) in applied settings rarely have access to anything other than fitness values of the best individuals to observe the behavior of the GA. In particular, researchers do not know what schemata are present in the population. Even when researchers look beyond best fitness values, they concentrate on either performance related measures like average fitness and robustness, or low-level descriptions like bit-level diversity measures. To understand the behavior of the GA on dynamic problems, it would be useful to track what is occurring on the "semantic" level of schemata. Thus in this paper we examine the evolving "content" in terms of schemata, as the GA solves dynamic problems. This allows us to better understand the behavior of the GA in dynamic environments. We finish by summarizing this knowledge and speculate about future work to address some of the new problems that we discovered during these experiments. [ABSTRACT FROM AUTHOR]

Published
2007
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20. Using a GeneticFuzzy Algorithm as a Computer Aided Breast Cancer Diagnostic Tool.

Author

Alharbi A, Tchier F, and Rashidi M

Subjects
Algorithms, Databases, Factual, Diagnosis, Computer-Assisted methods, Early Detection of Cancer methods, Female, Humans, Saudi Arabia, Breast Neoplasms diagnosis
Abstract

Computeraided diagnosis of breast cancer is an important medical approach. In this research paper, we focus on combining two major methodologies, namely fuzzy base systems and the evolutionary genetic algorithms and on applying them to the Saudi Arabian breast cancer diagnosis database, to aid physicians in obtaining an earlycomputerized diagnosis and hence prevent the development of cancer through identification and removal or treatment of premalignant abnormalities; early detection can also improve survival and decrease mortality by detecting cancer at an early stage when treatment is more effective. Our hybrid algorithm, the geneticfuzzy algorithm, has produced optimized systems that attain high classification performance, with simple and readily interpreted rules and with a good degree of confidence.

Published
2016

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