Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production

Abstract

The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable energy resources in the upcoming years. Recycling sources from waste could efficiently ease the risk of world source strain. The waste classification was a good resolution for separating the waste from the recycled objects. It is inefficient and expensive to rely solely on manual classification of garbage and recycling sources. Convolutional neural networks (CNNs) have lately been used to classify recyclable waste, and this is the primary way for recycling the waste. This study presents a recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) model for bioenergy production. RWC-EPODL model focuses on recycling waste materials recognition and classification. When it comes to detecting and classifying trash, the RWC-EPODL model uses two stages. At the initial stage, the RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. In addition, Bayesian optimization (BO) algorithm is applied as hyperparameter optimizer of the AX-RetinaNet model. Following the EPO algorithm with a stacked auto-encoder (SAE) model, the EPO algorithm is used to fine-tune the parameters of the SAE technique for trash classification. The RWC-EPODL model's experimental validation is examined through a number of studies. The RWC-EPODL approach has a 98.96 percent success rate. The comparative result analysis reported the better performance of the RWC-EPODL model over recent approaches.