University of Bahrain
Scientific Journals

A Methodology for Identification of Ayurvedic Plant Based on Machine Learning Algorithms

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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


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