A neutrosophic transfer learning approach for enhanced recyclable waste identification

ABSTRACT Recycling solid waste is crucial for achieving sustainable development, as it enhances economic efficiency while preserving environmental stability. However, many solid waste image modalities are susceptible to noise and uncertainty, which can compromise the performance of automated waste classification systems. Neutrosophic sets (NS) provide a robust framework for managing such uncertainty by decomposing images into three components: truth (T), indeterminacy (I), and falsity (F). Unlike traditional representations, the NS framework explicitly models ambiguity and vagueness inherent in visual waste data. This study investigates the effectiveness of deep learning (DL) models integrated with the NS framework for solid waste classification using the TrashNet dataset. The original images are transformed into the neutrosophic domain, enabling their representation through these three distinct components. Four DL architectures—DenseNet121, DenseNet169, InceptionV3, and MobileNetV2 are trained on images processed within the neutrosophic environment. The performance of these models is evaluated and compared across both the NS and fuzzy set (FS) domains using key metrics such as accuracy, precision, recall, and F1-score. The experimental outcomes demonstrate that the NS framework outperforms the FS approach, with the neutrosophic falsity component achieving a maximum accuracy of 97.64% using InceptionV3, highlighting its potential for more reliable waste classification in real-world applications. Overall, the proposed neutrosophic–deep learning framework offers a robust and scalable solution for intelligent solid waste management, supporting sustainable smart-city initiatives.