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@article{206796,
        author = {K Vaishnavi Rao and Dr. Jithendra P R Nayak},
        title = {WasteNet AI: Smart Waste Classification System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {475-480},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206796},
        abstract = {Waste management is becoming a major concern amid fast urbanization and rising consumption. Manual segregation can be inefficient and even erroneous, resulting in environmental contamination. In an effort to tackle this problem, a solution has been provided by this project known as "WasteNet AI: Intelligent Waste Segregation." This project employs deep learning techniques for automation. To achieve its goal, the WasteNet model applies a pre-trained ResNet-18 with transfer learning techniques for classification into categories such as Cardboard, Glass, Metal, Paper, and Plastic. Image processing methods such as resizing and normalization also aid in increasing the efficiency and effectiveness of the proposed approach. A simple and intuitive user interface has been made available by using the Python framework known as Streamlit to enable users to upload images that predict the type of waste together with probability. Furthermore, the application offers users useful recycling tips on how best to dispose of the different kinds of waste.},
        keywords = {CNN, environmental Sustainability, image processing, ResNet-18, transfer learning.},
        month = {July},
        }