Enhancing The Accuracy In Food Image Recognition Using Recurrent Neural Network Model In Comparison With Graph Neural Network Model

To find the Accuracy in Food image recognition using Graph Neural Network Model in comparison with Recurrent Neural Network Model. Dataset of images in jpg, format consists of the images of Food items of various varieties like vegetarian and non-vegetarian. Prepare the data-set. Split the data-set into two parts: a training set and a test set. The training set will be used to train the machine learning models, and the test set will be used to evaluate the performance of the trained models Results: The accuracy for finding accuracy in food recognition using RNN is $\mathbf{6 4. 8 0}$ % and GNN is $\mathbf{8 1. 4 0 \%}$. GNN uses non -parametric tricks to solve the nonlinear problems and RNN uses parametric tricks to solve and handle the high dimensional data to input space to solve the problem. The statistically significant difference between two groups. GNN Algorithm is showing more accuracy than the RNN in Food image recognition.