Please use this identifier to cite or link to this item:
http://ir.futminna.edu.ng:8080/jspui/handle/123456789/2142| Title: | Empirical Design Framework for Development of Convolutional Neural Network Based Model |
| Authors: | Subairu, S. O. Alhassan, J. K. Abdulhamid, S. M. Ojeniyi, J. A. |
| Keywords: | Convolutional Neural Network, Hyperparameters, Model, False Positive, False Negative. |
| Issue Date: | 2020 |
| Publisher: | International Journal of Engineering and Artificial Intelligence |
| Citation: | https://www.ijeai.com/archive-2020/volume-1-issue-4 |
| Series/Report no.: | Volume 1 Number 4; |
| 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 |
| URI: | http://repository.futminna.edu.ng:8080/jspui/handle/123456789/2142 |
| ISSN: | 2708-2792 |
| Appears in Collections: | Computer Science |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Emperical design framework.pdf | 40.63 kB | Adobe PDF | View/Open |
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