Exploration of an E-waste Prediction Model for Collection System Optimization Using Deep Learning Models in E-waste Management Facilities

E-waste or Waste Electrical and Electronic Equipment processing is of utmost importance in the modern world as the use of technology grows at an exponential rate. As such, the use of deep learning models has risen, specifically in the field of E-waste recycling and facilitation tasks. Automated E-waste reuse, reduction, and recycling have become important in the modern age. The paper explores an e-waste prediction model that uses object identification using YOLOv8 and is designed to help e-waste facility managers make data-driven decisions. YOLOv8 has been used for its ability to be rapidly deployed - it’s highly scalable and has enhanced feature extraction capabilities. The paper also explores deep learning models as viable options for image classifications and their viability for deployment in dynamic e-waste management facilities for decision-making purposes.