The nature of statistical learning theory
The nature of statistical learning theory
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Applying rough sets to market timing decisions
Decision Support Systems - Special issue: Data mining for financial decision making
Classification of Petroleum Well Drilling Operations Using Support Vector Machine (SVM)
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Journal of Global Optimization
Expert Systems with Applications: An International Journal
Online updating belief rule based system for pipeline leak detection under expert intervention
Expert Systems with Applications: An International Journal
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
Rough set theory with discriminant analysis in analyzing electricity loads
Expert Systems with Applications: An International Journal
Rough sets to help medical diagnosis - Evidence from a Taiwan's clinic
Expert Systems with Applications: An International Journal
A hybrid approach of DEA, rough set and support vector machines for business failure prediction
Expert Systems with Applications: An International Journal
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
Hi-index | 12.05 |
The generation of leak along the pipeline carrying crude oils and liquid fuels results enormous financial loss to the industry and also affects the public health. Hence, the leak detection and localization problem has always been a major concern for the companies. In spite of the various techniques developed, accuracy and time involved in the prediction is still a matter of concern. In this paper, a novel leak detection scheme based on rough set theory and support vector machine (SVM) is proposed to overcome the problem of false leak detection. In this approach, 'rough set theory' is explored to reduce the length of experimental data as well as generate rules. It is embedded to enhance the decision making process. Further, SVM classifier is employed to inspect the cases that could not be detected by applied rules. For the computational training of SVM, this paper uses swarm intelligence technique: artificial bee colony (ABC) algorithm, which imitates intelligent food searching behavior of honey bees. The results of proposed leak detection scheme with ABC are compared with those obtained by using particle swarm optimization (PSO) and one of its variants, so-called enhanced particle swarm optimization (EPSO). The experimental results advocate the use of propounded method for detecting leaks with maximum accuracy.