<![CDATA[This research aims to develop machine learning (ML) models for classifying non-organic waste. The goal is to support more effective waste management by increasing recycling rates, reducing landfill use, and minimizing environmental impact. The ML models proposed in this paper classify 20 types of non-organic waste collected from the internet, which consists of 2,552 instances. Experiments on our dataset reveal key findings. First, MobileNet, achieving 86% accuracy, outperforms VGG-16, which reaches 72% accuracy. Second, both models effectively classify distinct objects such as cigarette butts, toothbrushes, and glass bottles, demonstrating strong pattern recognition for these categories. Third, both models struggle with misclassification in visually similar categories, particularly paper-based waste like cardboard, carton packaging, books, and foam packaging. Fourth, MobileNet shows notable confusion in classifying plastic packaging, carton packaging, and books, while VGG-16 exhibits greater misclassification in foam packaging, cardboard, and newspapers. These results highlight the challenge of distinguishing waste types with overlapping textures and shapes. Moreover, it presents the urgency of improving the model to distinguish visually similar waste materials. Looking at the number of labels used in this paper compared with existing studies, the findings demonstrate the competitiveness of our models for non-organic waste classification.]]>
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