Discovering the secret behind managing WEEE: deep learning method in industry 4.0

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dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridShahidzadeh, Mohammad Hossein/0000-0002-2191-7649
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.contributor.authorShahidzadeh, Mohammad Hossein
dc.contributor.authorShokouhyar, Sajjad
dc.contributor.authorSafari, Aida
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorShokoohyar, Sina
dc.date.accessioned2024-05-19T14:45:51Z
dc.date.available2024-05-19T14:45:51Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractA large volume of waste electrical and electronic equipment (WEEE) is generated worldwide every year. This consists of hazardous and precious metals and represents a significant portion of this stream. Governments must make the right decisions regarding the influential factors affecting consumers' participation in electronic waste recycling programs in Industry 4.0 era to minimize the devastating impacts of these devices on the environment and human health and to recover precious metals and resources. Using the decentralized consensus decision-making concept, the proposed framework in this study uses social media users' opinions to improve decision-making concerning the influential factors affecting consumers' participation through artificial intelligence (AI). Considering Industry 4.0 concept, 20,348,014 million posts are extracted from Twitter, Facebook (Meta), and Reddit platforms and are analyzed using AI techniques. Then, more than 100 papers are analyzed to list influential factors comprehensively. Finally, the aggregated factors are presented to the Delphi method for further analysis. The findings demonstrate that economic incentives are considered significant factors in developed and developing countries. Since the living conditions of developed and developing nations are different, their concerns are also different. Hence, socio-economic and socio-political issues are the main concerns of people in developing countries. However, proximity, ease of access, and other factors play a significant role in developed countries. This study is among a few studies developing a real-time decision-making system to improve decision-making using social media data and AI techniques.en_US
dc.identifier.doi10.1007/s10479-023-05632-8
dc.identifier.issn0254-5330
dc.identifier.issn1572-9338
dc.identifier.scopus2-s2.0-85174265251en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s10479-023-05632-8
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5367
dc.identifier.wosWOS:001084506600002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofAnnals of Operations Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectMobile Phone Wasteen_US
dc.subjectWeeeen_US
dc.subjectSocial Media Analyticsen_US
dc.subjectDeep Learningen_US
dc.subjectTopic Modelingen_US
dc.subjectIndustry 4.0en_US
dc.titleDiscovering the secret behind managing WEEE: deep learning method in industry 4.0en_US
dc.typeArticleen_US

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