An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

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