Paper Title
Waste Classification Using Deep Learning

Abstract
This research paper endeavors to provide a compre- hensive exploration of waste classification through deep learning techniques, addressing the critical need for efficient waste man- agement and recycling. Accurate categorization of waste into organic and inorganic types is pivotal for optimizing recycling efforts and minimizing environmental impact. Utilizing a diverse dataset comprising various waste materials, including food, plastics, and clothing, this study employs Convolutional Neural Networks (CNNs) to construct a robust waste classification model.The research outlines the model’s architecture,training process, and its impressive performance in distinguishing between organic and recyclable waste items. Furthermore, it examines the real-world implications of the model’s accuracy on enhancing waste segregation practices and promoting sustainability. This investigation underscores effectiveness of deep learning in ad- vancing waste management, contributing insights to waste sorting automation and environmental sustainability Keywords - Waste Classification, Deep Learning, Convolu- tional Neural Networks, Recycling, Sustainability