Enhancing the Explainability of Neural Networks in the Aspects of Feature Importanc Domain Rules and Algorithmic Transparency using an Object Oriented Approach

dc.contributor.guide	Martin, A
dc.coverage.spatial	196
dc.creator.researcher	Enoch Arulprakash
dc.date.accessioned	2024-12-20T12:13:28Z
dc.date.available	2024-12-20T12:13:28Z
dc.date.awarded	2024
dc.date.completed	2024
dc.date.registered	2018
dc.description.abstract	newline Avaialable
dc.description.note
dc.format.accompanyingmaterial	DVD
dc.format.dimensions	A4
dc.format.extent	i, 180
dc.identifier.uri	http://hdl.handle.net/10603/608676
dc.language	English
dc.publisher.institution	Department of Computer Science
dc.publisher.place	Neelakudi
dc.publisher.university	Central University of Tamil Nadu
dc.relation	APA
dc.rights	university
dc.source.university	University
dc.subject.keyword	Computer Science
dc.subject.keyword	Computer Science Software Engineering
dc.subject.keyword	Engineering and Technology
dc.title	Enhancing the Explainability of Neural Networks in the Aspects of Feature Importanc Domain Rules and Algorithmic Transparency using an Object Oriented Approach
dc.title.alternative
dc.type.degree	Ph.D.