… used for the classification of waste is reconstructed with the AutoEncoder network. The
feature sets are then extracted using two datasets by Convolutional Neural Network (CNN) …

… in waste management is the classification of waste based on its … In this paper, an
autoencoder-based deep learning method … of an autoencoder for highlight extraction and …

… the coastal waste condition. On the other hand, due to the complex composition of … waste,
the purpose of this study is to use image classification methods to quickly classify coastal waste …

… , ANNs are classified as feedforward and feedback neural networks. Owing to their simpler
and superior performance over feedback neural networks, feedforward neural networks have …

… each prediction or classification, or reconstruction in the case of an autoencoder. It iterates
… after the entire batch is passed to the neural network. A batch size is a hyperparameter used …

… for waste classification of TrashNet. In addition, to visually evaluate the performance of CNNs,
the confusion matrix displays the number of correct and incorrect predictions made by the …

… Therefore, the objective of this project is to properly classify waste through the use of deep-…
neural network, constraining the network makes the autoencoder a difficult neural network. …

… waste every year. However, current machine learning models for intelligent garbage detection
and classification … YOLO-based neural network model with Variational Autoencoder (VAE) …

… with an autoencoder network that simultaneously transformed the data from the image
space to the feature space and used a CNN model to extract features. Support Vector Machine (…

… novel Multitask multi-GRU-Autoencoder (MTGA) model for grain … ) autoencoder is GRU-autoencoder
ensembles. Specially, we … Broadly, we can classify these behaviors into four sets: …