Abstract
Proper waste management models using recent technologies like computer vision, machine learning (ML), and deep learning (DL) are needed to effectively handle the massive quantity of increasing waste. Therefore, waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process. This study concentrates on the design of hazardous waste detection and classification using ensemble learn-ing (HWDC-EL) technique to reduce toxicity and improve human health. The goal of the HWDC-EL technique is to detect the multiple classes of wastes, particularly hazardous and non-hazardous wastes. The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns (DLBP), Effi-cientNet, and DenseNet121. In addition, the flower pollination algorithm (FPA) based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models. Moreover, a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine (SVM), extreme learning machine (ELM), and gradient boosting tree (GBT). The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.