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

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