An Intelligent Framework for Herb Leaves Classification - A. Hema Deepika (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The grouping result is low for picture handling in the current strategies, so the proposed model has been created to further develop the characterization results. Orders of the leaf were done in light of the removed highlights. Thus, a novel Ant Colony found Gradient Boosting Model (ACbGBM) is developed with required processing parameters: filtering and classification functions to improve the classification performance and make the system more robust and efficient. In this model, better classification results were obtained and are shown in the result section. Here, leaf data is collected and initialized to the system. In this manner, pre-handling is finished over the introduced informational collection after highlight extraction. The negligible elements are taken out in highlight extraction, and the entire significance highlights are picked. The extracted features' fitness is contrasted with the fitness of ants. After that, classification is done over the extracted features. Besides, a contextual investigation is created to make sense of the functioning technique of the proposed model. As a result, the constructed model was implemented in the MATLAB programme, and the recall, accuracy, precision, error rate, and f-measure were all measured in its presence.