Papers by Ku Ruhana Ku Mahamud
Traditional knowledge in Local wisdom in adapting to and coping with flood disasters in ASEAN cou... more Traditional knowledge in Local wisdom in adapting to and coping with flood disasters in ASEAN countries
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Proceedings of Malaysian …, Sep 1, 2007
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Research Journal of Applied Sciences, Engineering and Technology, 2015
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Journal of Business Case Studies (JBCS), 2011
... Her research interests include project management, e-commerce website evaluation, bridgingdig... more ... Her research interests include project management, e-commerce website evaluation, bridgingdigital divide and e-inclusion. ... scores, building student schedules, tracking student attendance, and managing many other student-related data needs in a school, college or university ...
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Intelligent Automation & Soft Computing, 2016
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Research Journal of Applied Sciences, Engineering and Technology, 2015
This study proposes machine learning strategies to control the parameter adaptation in ant colony... more This study proposes machine learning strategies to control the parameter adaptation in ant colony optimization algorithm, the prominent swarm intelligence metaheuristic. The sensitivity to parameters’ selection is one of the main limitations within the swarm intelligence algorithms when solving combinatorial problems. These parameters are often tuned manually by algorithm experts to a set that seems to work well for the problem under study, a standard set from the literature or using off-line parameter tuning procedures. In the present study, the parameter search process is integrated within the running of the ant colony optimization without incurring an undue computational overhead. The proposed strategies were based on a novel nature-inspired idea. The results for the travelling salesman and quadratic assignment problems revealed that the use of the augmented strategies generally performs well against other parameter adaptation methods.
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Indian Journal of Science and Technology, 2015
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The Scientific World Journal
A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behav... more A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.
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2015 Second International Conference on Computing Technology and Information Management (ICCTIM), 2015
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WSEAS Transactions on Computers
Ant Colony Optimization (ACO) has been used to solve Support Vector Machine (SVM) model selection... more Ant Colony Optimization (ACO) has been used to solve Support Vector Machine (SVM) model selection problem. ACO originally deals with discrete optimization problem. In applying ACO for optimizing SVM parameters which are continuous variables, there is a need to discretize the continuously value into discrete values. This discretize process would result in loss of some information and hence affect the classification accuracy. In order to enhance SVM performance and solving the discretization problem, this study proposes two algorithms to optimize SVM parameters using Continuous ACO (ACOR) and Incremental Continuous Ant Colony Optimization (IACOR) without the need to discretize continuous value for SVM parameters. Eight datasets from UCI were used to evaluate the credibility of the proposed integrated algorithm in terms of classification accuracy and size of features subset. Promising results were obtained when compared to grid search technique, GAwith feature chromosome-SVM, PSO-SVM, ...
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Support Vector Machines are considered to be excellent patterns classification techniques. The pr... more Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter. Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. Ant Colony Optimization originally deals with discrete optimization problems. Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous in nature, the values wil have to be discretized. The discretization process will result in loss of some information and, hence, affects the classification accuracy and seeks time. This paper presents an algorithm to optimize Support Vector Machine parameters using Incremental continuous Ant Colony Optimization without the need to discretize continuous values. Eight datasets from UCI were ...
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The performance of Support Vector Machine (SVM) classifier can be improved by simultaneously opti... more The performance of Support Vector Machine (SVM) classifier can be improved by simultaneously optimizing its parameters and features subset selection. The problem of selecting suitable features subset and optimizing SVM parameters must occur simultaneously, because these problems are essential and they affect each other which in turn will affect the SVM classification accuracy. Ant Colony Optimization (ACO) originally deals with discrete optimization problem. In applying ACO for optimizing SVM parameters which are continuous variables, there is a need to discretize the continuously value into discrete value. This discretize process would result in loss of some information and hence affect the classification accuracy and seeking time. This study proposed two algorithms, the first algorithm deals with optimizing only SVM parameter using Incremental Continuous Ant Colony Optimization without the need to discretize continuous value for support vector machine parameters, while the second ...
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2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), 2014
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A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behav... more A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant
hosts.The parasites’ reaction results from their ability to indicate the state of penetration.The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization
problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.
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Papers by Ku Ruhana Ku Mahamud
hosts.The parasites’ reaction results from their ability to indicate the state of penetration.The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization
problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.
hosts.The parasites’ reaction results from their ability to indicate the state of penetration.The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization
problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.