Enhancing the Accuracy of Meal Waste from Innovative Trash Data Using Random Forest by Comparing Support Vector Machine Algorithms

The main objective of this paper is to improve the accuracy for automatic classification of meal waste from innovative trash data with the help of image processing. Material and Methods: There are 2572 images for the classification of meal waste were used for this paper. The images are labelled as “Cardboard”, “Plastic”, “Paper”, “Metal”, “Glass”, “Trash” and there are 20 number of images have been used for RF classifier taken as first set of machine learning algorithm and is compared with SVM algorithm taken as second set of machine learning algorithm With a g-power value of 80%, the revolutionary garbage data images, a threshold of 0.05%, a confidence interval of 95%, and a standard deviation, these photographs were gathered from various web sources. Results: When compared to the SVM method, which had an accuracy of 61.45%, the proposed system’s accuracy was enhanced to 84.81%, with a significant value of 0.001 (p 0.05) with a 95% confidence interval. Conclusion: This study found the meal waste from trash using the RF is significantly better than SVM algorithm.