A New Method for Software Defect Prediction Based on Optimized Machine Learning Techniques

dc.contributor.author	SHAHO HASSEN
dc.contributor.author	Prof. Dr. Ali YAZICI
dc.contributor.author	Prof. Dr. Alok MISHRA
dc.date.accessioned	2026-01-02T12:08:47Z
dc.date.issued	2023-07-24
dc.description.abstract	This thesis aimed to develop a robust neuro-computing model for software defect prediction, utilizing the Levenberg Marquardt Neural Network (LM-ANN) with a novel improved genetic algorithm as a heuristic model. The goal was to achieve higher accuracy and overcome local minima and convergence issues
dc.format	application/pdf
dc.identifier.uri	https://geniusjournals.org/index.php/ejet/article/view/4707
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/78750
dc.language.iso	eng
dc.publisher	Genius Journals
dc.relation	https://geniusjournals.org/index.php/ejet/article/view/4707/3989
dc.rights	https://creativecommons.org/licenses/by-nc/4.0
dc.source	Eurasian Journal of Engineering and Technology; Vol. 20 (2023): EJET; 53-76
dc.source	2795-7640
dc.subject	Neuro-Computing
dc.subject	LM-ANN
dc.subject	Local Minima
dc.title	A New Method for Software Defect Prediction Based on Optimized Machine Learning Techniques
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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