dc.contributor.author | Erkınay, Özdemir Merve | |
dc.contributor.author | Ali, Zaara | |
dc.contributor.author | Subeshan, Balakrishnan | |
dc.contributor.author | Asmatulu, Eylem | |
dc.date.accessioned | 2021-06-14T07:58:03Z | |
dc.date.available | 2021-06-14T07:58:03Z | |
dc.date.issued | 2021 | en_US |
dc.identifier.citation | Erkinay Ozdemir, M., Ali, Z., Subeshan, B., Asmatulu, E. (2021). Applying machine learning approach in recycling. Journal of Material Cycles and Waste Management, 23 (3), 855-871.
https://doi.org/10.1007/s10163-021-01182-y | en_US |
dc.identifier.uri | https://doi.org/10.1007/s10163-021-01182-y | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/1762 | |
dc.description.abstract | Waste generation has been increasing drastically based on the world’s population and economic growth. This has significantly affected human health, natural life, and ecology. The utilization of limited natural resources, and the harming of the earth in the process of mineral extraction, and waste management have far exceeded limits. The recycling rate are continuously increasing; however, assessments show that humans will be creating more waste than ever before. Some difficulties during recycling include the significant expense involved during the separation of recyclable waste from non-disposable waste. Machine learning is the utilization of artificial intelligence (AI) that provides a framework to take as a structural improvement of the fact without being programmed. Machine learning concentrates on the advancement of programs that can obtain the information and use it to learn to make future decisions. The classification and separation of materials in a mixed recycling application in machine learning is a division of AI that is playing an important role for better separation of complex waste. The primary purpose of this study is to analyze AI by focusing on machine learning algorithms used in recycling systems. This study is a compilation of the most recent developments in machine learning used in recycling industries. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.isversionof | 10.1007/s10163-021-01182-y | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Neural network | en_US |
dc.subject | Decision making | en_US |
dc.subject | Advanced recycling | en_US |
dc.subject.classification | Object detection | |
dc.subject.classification | CNN | |
dc.subject.classification | IOU | |
dc.subject.classification | Environmental Sciences | |
dc.subject.other | Economics | |
dc.subject.other | Learning algorithms | |
dc.subject.other | Mineral resources | |
dc.subject.other | Natural resources management | |
dc.subject.other | Population statistics | |
dc.subject.other | Recycling | |
dc.subject.other | Separation | |
dc.subject.other | Waste management | |
dc.subject.other | Machine learning approaches | |
dc.subject.other | Mineral extraction | |
dc.subject.other | Recyclable wastes | |
dc.subject.other | Recycling applications | |
dc.subject.other | Recycling industry | |
dc.subject.other | Recycling systems | |
dc.subject.other | Structural improvements | |
dc.subject.other | Waste generation | |
dc.title | Applying machine learning approach in recycling | en_US |
dc.type | review | en_US |
dc.relation.journal | Journal of Material Cycles and Waste Management | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.volume | 23 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.startpage | 855 | en_US |
dc.identifier.endpage | 871 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Erkınay, Özdemir Merve | |
dc.relation.index | Web of Science - Scopus | en_US |
dc.relation.index | Web of Science Core Collection - Science Citation Index Expanded | |