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
For the full publication, please contact the author directly at: bibti001@gmail.com
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- University of Ilorin, Kwara State 398
- University of Jos, Jos, Plateau State 19
- University of Lagos 18
- University of Maiduguri ( - Elearning), Maiduguri, Borno State 3
- University of Maiduguri, Borno State 109
- University of Nigeria, Nsukka, Enugu State 269
- University of Port Harcourt Teaching Hospital, Port Harcourt , River State 5
- University of Port-Harcourt, Rivers State 174
- University of Uyo, Akwa Ibom State 206
- Usmanu Danfodio University, Sokoto, Sokoto State 245