The Design and Development of a Flower Classification Hybrid Model for Feature Extraction using CNN and Intersection with Machine Learning with and without Optimization Techniques

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Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.663
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Volume 13, Special Issue 3, October 2022

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Rupinder Kaur
IIS University, Jaipur
DR. ANUBHA JAIN
Associate Professor, Department of Computer Science, IIS University, Jaipur, Rajasthan, India
Dr. Amita Sharma
Senior Assistant Professor, Department of Computer Science, IIS University, Jaipur, Rajasthan, India

Abstract

The research paper aims to implement a new flower classification method developed with the combination of convolutional neural networks and machine learning. A hybrid model is designed with grouping of feature extraction and feature selection methods using the CNN model. The resultant feature extraction is applied to the classification, which is built through a machine learning model, and lastly, the classified model is applied with and without optimization techniques to check the accuracy and performance of the model. The two separate results are performed by the model consisting of the optimization model and one without the optimization. The overall model's accuracy will be compared with respect to the different parameters for the classification

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How to Cite
Kaur, R., JAIN, D. A. ., & Sharma, D. A. . (2022). The Design and Development of a Flower Classification Hybrid Model for Feature Extraction using CNN and Intersection with Machine Learning with and without Optimization Techniques. International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.663
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