A Machine Learning Based Waste Classification System for Enhance Waste Management in Nigerian

Student: HABIBA ISMAIL (Project, 2025)
Department of Computer Science
Federal University, Dutsin-Ma, Katsina State

Abstract

Waste management is essential as a response to growth and urbanization challenges in the society. In this work, the use of machine learning solutions for increased efficiency in waste classification and the improvement of sustainability of waste management systems is discussed. Building upon the TrashNet dataset and utilizing Convolutional Neural Networks (CNNs), the specific objectives of the study is to determine an effective approach to waste sorting by defining pre-determined categories. The implementation employs Kaggle as the computational platform and TensorFlow and its ancillaries for model calibration. Performance indicators that are attentively used are accuracy, precision, and recall. To conclude this work makes a leeway for the integration and future implementations of machine learning in waste management system to enhance optimized classification and decision-making on waste management procedures and systems.

Keywords
machine learning waste classification waste management smart waste management artificial intelligence Nigeria sustainable development environmental protection AI-based waste sorting
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