Smart Waste Revolution: AI-Powered Biomedical Segregation and Recycling with IoT Integration

The threat to the environment and health posed by biomedical waste requires a proper approach to its management and recycling. This study aims to develop an advanced smart waste management system that combines IoT sensors for real-time monitoring and an Extreme Learning Machine (ELM) to segregate waste. This system applies IoT sensors to get data from hospital ward dustbins that alert bins' fullness. The ELM uses the information received to categorize the waste as recyclable or non-recyclable. The sorted-out trash items are then directed either to disposal or recycling bins accordingly. Our method improves efficiency in handling biomedical wastes by utilizing state-of-the-art ELM algorithms, which exhibit superior performance over conventional methods in accuracy and computation speed, among others. Our simulation results show that our model has 95% classification accuracy compared with other AI-based approaches such as Cohort Intelligence Algorithm (CIA), Hesitant Fuzzy Weight and Rank Finding (HFWRF), and Deep Learning (DL). This new system not only contributes to efficient waste management but also supports environmental sustainability, promoting effective recycling plans.