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Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal(More)
Cloud based Content distribution networks are most famous among the small scale content distribution network organization. Because it enables an organization to make use of required storage, bandwidth and CPU capacity from the cloud services instead of having to buy and maintain those. In the previous work resource provisioning and caching contents in the(More)
Feature selection refers to the problem of selecting the set of most relevant features which produces the most predictive outcome. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find the optimal subsets. This paper proposes a new feature selection method based on rough set(More)
The programmer has to understand the behavior of two similar programs and then identify the execution difference which produces difference in output. When two similar programs are executed under two different environments which shows different behavior in output. The main difference exists in the program behavior is due to two different types of input. This(More)
Fault localization is an expensive technique in software debugging. Program dependence graphs are used for testing, debugging and maintenance applications in software engineering. Program dependence graphs (PDG) are used to build a probabilistic graphical model of program behavior. In this paper we proposed a model based fault localization technique using(More)
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