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@article{156860,
        author = {Aruna Sudame and Azza Al-Ghamdi and Ubio Obu and Jaiyeyemi Adekunle},
        title = {ANALYZING END OF LIFE TYRE (ELT) CARBONS USING MACHINE LEARNING ALGORITHM},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {5},
        pages = {507-513},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156860},
        abstract = {Waste tyre constitute a large percentage of wastes that affect the ecosystem. With an approximation of 1.5 billion tyres reaching their end of life every year, it is important to critically understand what constitutes waste tyres. End of life tyres commonly called ELT has given researchers lot of concerns with understanding it, how it operates and how to better recycle it. This paper is one of such contribution to help with understanding the compositions of ELT tyres but specifically on different carbons produced from ELT tyres. Obviously carbon is the most dominant and useful remnant of such waste, but In this paper we examined 3 types of end of life tyre carbon, namely IB 550A, IB 330B and IB 660C and their characteristics. We used Machine learning Algorithms to differentiate these carbons base on their chemical composition, to understand all the constituents. This study gives insight on different carbons derived from waste tyres and its composition. It furnishes us with information on the future utility of waste tyres based on the class of carbon it falls under based on our machine learning clustering model and the best utility for each class. With most studies on waste tyres using chemical processes and methodologies to understand waste tyres carbon, a machine learning approach introduces an entirely new approach.},
        keywords = {carbon, End of life tyres(ELT), tyre waste, machine learning},
        month = {},
        }