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A Neural Network based Method to Optimize the Software Component Searching Results in K-Model

by Suresh Chand Gupta, Ashok Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 7
Year of Publication: 2013
Authors: Suresh Chand Gupta, Ashok Kumar
10.5120/12506-9022

Suresh Chand Gupta, Ashok Kumar . A Neural Network based Method to Optimize the Software Component Searching Results in K-Model. International Journal of Computer Applications. 72, 7 ( June 2013), 20-27. DOI=10.5120/12506-9022

@article{ 10.5120/12506-9022,
author = { Suresh Chand Gupta, Ashok Kumar },
title = { A Neural Network based Method to Optimize the Software Component Searching Results in K-Model },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 7 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 20-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number7/12506-9022/ },
doi = { 10.5120/12506-9022 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:17.531003+05:30
%A Suresh Chand Gupta
%A Ashok Kumar
%T A Neural Network based Method to Optimize the Software Component Searching Results in K-Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 7
%P 20-27
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Here we propose a storage and retrieval approach of reusable software components based on UML diagram, metadata repository and neural network. If we search the repository on the basis of attributes of MDL file descriptions, the search result would be better and thus giving higher precision, as compared to keyword based search, then apply neural network to searching results of reusable software component for optimizing the searching results. The proposed approach is tested on various reusable software component datasets containing purely continuous or purely categorical or a mix of both types of attributes. Many features used in the analysis of reusable software component. In this paper reusable software component classified using feed forward back propagation Neural Network. One thousand sets of reusable software component obtained by software reusable techniques. The dataset consist of twenty eight features which represent the input layer to the FNN. The FNN will classify the reusable software component into type4, type3, type2 and type1 reusable software component. The sensitivity, specificity and accuracy were found to be equal 99. 64%, 98. 54% and 98. 80% respectively. It can be concluded that FNN gives fast and accurate classification and it works as promising tool for optimizing the searching results of reusable software component. The overall accuracy of optimizing searching results of the proposed system is 96. 50%. Thus, this approach is suitable for automated real time reusable software storing and searching.

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Index Terms

Computer Science
Information Sciences

Keywords

Metadata repository UML Diagram MDL File Search Engine K-model ontology neural network

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