Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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To develop the secure software is one of the major concerns in the software industry. To make the easier task of finding and fixing the security flaws, software developers should integrate the security at all stages of Software Development Life Cycle (SDLC).In this paper, based on Neuro-Fuzzy approach software Risk Prediction tool is created. Firstly Fuzzy Inference system is created and then Neural Network based three different training algorithms: BR (Bayesian Regulation), BP (Back propagation) and LM (Levenberg-Marquardt) are used to train the neural network. From the results it is conclude that for the Software Risk Estimation, BR (Bayesian Regulation) performs better and also achieves the greater accuracy than other algorithms.
Pooja Rani. 2013. \u201cNeuro-Fuzzy Based Software Risk Estimation Tool\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C6): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 102
Country: India
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: Pooja Rani, Dalwinder Singh Salaria (PhD/Dr. count: 0)
View Count (all-time): 245
Total Views (Real + Logic): 9720
Total Downloads (simulated): 2557
Publish Date: 2013 06, Tue
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Neural Networks and Rules-based Systems used to Find Rational and
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To develop the secure software is one of the major concerns in the software industry. To make the easier task of finding and fixing the security flaws, software developers should integrate the security at all stages of Software Development Life Cycle (SDLC).In this paper, based on Neuro-Fuzzy approach software Risk Prediction tool is created. Firstly Fuzzy Inference system is created and then Neural Network based three different training algorithms: BR (Bayesian Regulation), BP (Back propagation) and LM (Levenberg-Marquardt) are used to train the neural network. From the results it is conclude that for the Software Risk Estimation, BR (Bayesian Regulation) performs better and also achieves the greater accuracy than other algorithms.
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