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http://hdl.handle.net/2080/1572
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DC Field | Value | Language |
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dc.contributor.author | Singh, J | - |
dc.contributor.author | Sahoo, Bibhudatta | - |
dc.date.accessioned | 2011-12-22T09:07:14Z | - |
dc.date.available | 2011-12-22T09:07:14Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network (CCSN) (4):13-17, 2011. Published by Foundation of Computer Science, New York, USA. | en |
dc.identifier.uri | http://hdl.handle.net/2080/1572 | - |
dc.description | Copyright belongs to International Journal of Computer Applications | en |
dc.description.abstract | Failures of software are mainly due to the faulty project management practices, which includes effort estimation. Continuous changing scenarios of software development technology makes effort estimation more challenging. Ability of ANN(Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers) makes it as a potential tool for estimation. This paper presents a performance analysis of different ANNs in effort estimation. We have simulated four types of ANN created by MATLAB10 NNTool using NASA dataset | en |
dc.format.extent | 785811 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | Foundation of Computer Science, USA | en |
dc.subject | Effort Estimation | en |
dc.subject | Artificial Neural Network | en |
dc.subject | NNtool | en |
dc.subject | MMRE | en |
dc.title | Software Effort Estimation with Different Artificial Neural Network | en |
dc.type | Article | en |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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SoftwareEffort-ANN.pdf | 767.39 kB | Adobe PDF | View/Open |
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