Empirical Design Framework for Development of Convolutional Neural Network Based Model

Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/11681

Full metadata record

DC Field	Value	Language
dc.contributor.author	Subairu, S.O	-
dc.contributor.author	Alhassan, J.K	-
dc.contributor.author	Abdulhamid, S.M	-
dc.contributor.author	Ojeniyi, J.A	-
dc.date.accessioned	2021-07-26T13:49:20Z	-
dc.date.available	2021-07-26T13:49:20Z	-
dc.date.issued	2020	-
dc.identifier.citation	https://www.ijeai.com/archive-2020/volume-1-issue-4	en_US
dc.identifier.issn	2708-2792	-
dc.identifier.uri	http://repository.futminna.edu.ng:8080/jspui/handle/123456789/11681	-
dc.description.abstract	Convolutional Neural Network (CNN) has been described by most researchers as the best when it comes to image classification problems. This Neural Network is made up of high sensitive hyperparameters, such that if not properly design could lead to model misclassification and such returns high false positive (FP) and high false negative(FN). In other to solve this problem, this research proposed and developed design frameworks that mitigate this identified problem when it comes to image classification model using a Convolutional Neural Network	en_US
dc.language.iso	en	en_US
dc.publisher	International Journal of Engineering and Artificial Intelligence	en_US
dc.relation.ispartofseries	Volume 1;NO 4	-
dc.subject	Convolutional Neural Network, Hyperparameters, Model, False Positive, False Negative.	en_US
dc.title	Empirical Design Framework for Development of Convolutional Neural Network Based Model	en_US
dc.type	Article	en_US
Appears in Collections:	Cyber Security Science

Files in This Item:

File	Description	Size	Format
7IJEAICAP4.pdf		542.8 kB	Adobe PDF	View/Open