dc.contributor.author |
Rao, Marada S. |
|
dc.contributor.author |
Kumar, S.P. |
|
dc.contributor.author |
Rao, Konda S. |
|
dc.date.accessioned |
2023-05-06T10:49:59Z |
|
dc.date.available |
2023-05-06T10:49:59Z |
|
dc.date.issued |
2023-10-01 |
|
dc.identifier.issn |
2210-142X |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/4926 |
|
dc.description.abstract |
Automated plant species recognition systems have recently been created to aid in the identification of various species by regular people. But compared to human interpretation, automatic computer analysis of plant species is challenging. This field has undergone studied in an effort to further develop plant species distinguishing proof. These strategies actually miss the mark concerning precisely ordering the plant species. Because of the flawed classification algorithm, there is an issue. Accuracy will be the main factor, especially when we consider the identification of medicinal plant species. The research's suggested solution uses deep learning to achieve high accuracy in the computer vision-based classification and recognition processes. Convolutional neural networks are utilized by this system. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Support vector machine (SVM); Logistic Regression (LR); K-Nearest Neighbor (K-NN); Convolutional neural network (CNN) |
en_US |
dc.title |
A Methodology for Identification of Ayurvedic Plant Based on Machine Learning Algorithms |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/140196 |
|
dc.volume |
14 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
10233 |
en_US |
dc.pageend |
10241 |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authoraffiliation |
GITAM School of Technology (GST), India & GITAM University |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |