Towards sustainable societies: Convolutional neural networks optimized by modified crayfish optimization algorithm aided by AdaBoost and XGBoost for waste classification tasks

Abstract

The categorization of waste is playing a pivotal role in addressing and alleviating the environmental and health repercussions linked to waste. It is essential for safeguarding the environment, as improper handling of hazardous waste may result in soil, water, and air contamination, posing significant threats to ecosystems and human well-being and maintaining a sustainable society. Effective waste classification enhances the efficacy of waste management by organizing waste into distinctive groups based on characteristics that include toxicity, flammability, recyclable potential, and biodegradability. This research introduces a methodology that relies on employing convolutional neural networks and the AdaBoost and XGBoost models for the purpose of waste classification. It emphasizes the necessity of customizing every deep learning method to suit the specific problem that needs to be solved. An altered form of the latterly proposed crayfish optimization algorithm is suggested, explicitly developed to meet the requirements of the particular waste classification task in hand. The assessment of the presented method using real-world datasets consistently demonstrates that models configured by the proposed modified algorithm achieve an accuracy level that exceeds 89.6140%. This pinpoints the considerable potential of this method in effectively addressing pressing problems in waste management within real-world scenarios.