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Your search keyword '"Alharbi, Abir"' showing total 2 results
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2 results on '"Alharbi, Abir"'

1. Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on Saudi Arabian breast cancer database.

Author

Alharbi A and Tchier F

Subjects
Databases, Factual, Humans, Saudi Arabia, Algorithms, Breast Neoplasms diagnosis, Diagnosis, Computer-Assisted, Fuzzy Logic, Models, Genetic
Abstract

The computer-aided diagnosis has become one of the major research topics in medical diagnostics. In this research paper, we focus on designing an automated computer diagnosis by combining two major methodologies, namely the fuzzy base systems and the evolutionary genetic algorithms and applying them to the Saudi Arabian breast cancer diagnosis database, to be employed for assisting physicians in the early detection of breast cancers, and hence obtaining an early-computerized diagnosis complementary to that by physicians. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized diagnosis systems that attain high classification performance, in fact, our best three rule system obtained a 97% accuracy, with simple and well interpretive rules, and with a good degree of confidence of 91%., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Published
2017
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2. 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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