International Journal of Science, Engineering and TechnologyAuthors: Gurpreet Kaur, Firoz Ansari, Angad Gupta
Abstract: The increasing generation of household waste and the informal nature of scrap trading have created challenges related to price transparency, material classification, and efficient reuse of recyclable resources. This study proposed an artificial intelligence-driven digital platform that integrated scrap trading, second-hand product resale, automated scrap classification, price prediction, and dealer matching within a unified ecosystem. A Convolutional Neural Network was employed to classify scrap images into plastic, metal, paper, glass, and electronic waste categories. Random Forest Regression was applied to predict scrap prices using material type, weight, volume, market demand, and prevailing market rates. A location-based matching mechanism was also incorporated to connect users with suitable nearby scrap dealers. The experimental evaluation showed that the proposed classification model achieved an accuracy of 94.2 percent, while the price prediction model obtained a Mean Absolute Error of 2.1, a Root Mean Square Error of 3.4, and an R squared score of 0.91. The findings indicated that the integrated approach improved scrap identification, pricing transparency, and transaction efficiency. The proposed platform provides a scalable digital approach that supports material reuse, sustainable waste management, and circular economy practices.
DOI: http://doi.org/10.5281/zenodo.21703475
