Abstract
The issue that arouses concern is the issue of waste in general, especially waste in food. Deep learning has made its way into the aforementioned field. Pre-processing is done using a new filter derived from discrete wavelets that were derived from Legendre polynomials (LEGP) to achieve Multi Resolution Analyses (MRA), in which the image parameters are separated into proximity parameters and detail parameters, where the first is concentrated in LL, while the second is in HL, LH, HH to improve the input image of the waste in terms of noise removal and image compression. To begin the deep learning process, configure the New Convolutional Neural Network (NCNN) to configure 144 layer with the image size (224x224x3) with Google Net so that the waste resulting from leftover food is sorted. In this work, four foods were sorted: tomatoes, potatoes, bread, and rice, and the result was reached for accuracy for each upcoming image with the accuracy of the neural arrangement of rice (99.9337) to potato (100 %) to tomato (99.8959), and finally for bread (83.5492), with the accuracy of the neural network 94.12 % in 1 min 2 sec.
The accuracy reached in this work with Discrete Legendre Wavelets Transform (DLEGWT) and training the new network for google net was 94.12 % in 1 minute and 2 seconds with the new filter (DLEGWT). A program was created that was translated into a new algorithm. It was shown that the new filter worked in two stages, which is the initial processing stage, included analyses image with removed the noise from the image, after which the image is compressed to reduce the space used by the image data. As for the second stage, the deep learning stage takes place with the new filter, so that a new convolutional neural network is created after Dividing the image into three channels RGB to complete the convolution process for each channel, so that the hidden layers are formed until it reaches full connectivity, so that the classification process is performed with triple accuracy. The proposed algorithm has proven its efficiency
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