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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Institutions
- HASSAN USMAN KATSINA POLYTECHNIC (NCE), KATSINA, KATSINA STATE 4
- Hassan Usman Katsina Polytechnic, Katsina, Katsina State 5
- Heritage Polytechnic, Ikot Udota, Akwa Ibom State 46
- Hussaini Adamu Federal Polytechnic, Kazaure, Jigawa State 8
- Ibrahim Badamasi Babangida University, Lapai, Niger State 24
- Igbinedion University, Okada, Benin City, Edo State 2
- Ignatius Ajuru University of Education, Port Harcourt, Rivers State 8
- Imo State Polytechnic, Umuagwo, Owerri, Imo State 3
- Imo State University, Owerri, Imo State 45
- Institute of Management and Technology, Enugu, Enugu State 11