{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T16:31:47Z","timestamp":1762014707738,"version":"build-2065373602"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"33","license":[{"start":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:00Z","timestamp":1760140800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:00Z","timestamp":1760140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s00521-025-11592-w","type":"journal-article","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T03:34:25Z","timestamp":1760153665000},"page":"28019-28041","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recognizing recyclable waste materials based on deep transfer learning models"],"prefix":"10.1007","volume":"37","author":[{"given":"Ghada","family":"Dahy","sequence":"first","affiliation":[]},{"given":"Mohamed","family":"Torky","sequence":"additional","affiliation":[]},{"given":"Vaclav","family":"Snasel","sequence":"additional","affiliation":[]},{"given":"Aboul Ella","family":"Hassanein","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,11]]},"reference":[{"issue":"1","key":"11592_CR1","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.wasman.2023.12.014","volume":"124","author":"MM Hossein","year":"2024","unstructured":"Hossein MM, Ashraf A, Hasan M, Majid ME, Chowdhury MEH (2024) GCDN-Net: garbage classifier deep neural network for recyclable urban waste management. Waste Manag 124(1):439\u2013450","journal-title":"Waste Manag"},{"unstructured":"Burggr\u00e4f P, Steinberg F, Sauer CR, Ludes A (2024) Boosting the circular manufacturing of the sustainable paper industry\u2014a first approach to recycle paper from unexploited sources such as lightweight packaging, residual and commercial waste. In: Proceedings of the 56th CIRP conference on manufacturing systems, CIRP, pp 1251\u20131258","key":"11592_CR2"},{"unstructured":"North Carolina Environmental Quality, Recycling, and Climate Change. https:\/\/deq.nc.gov\/conservation\/recycling\/recycling-climate-change. Last accessed 9 June, 2024","key":"11592_CR3"},{"unstructured":"Celeste Robinson, Recycling and Climate Change. https:\/\/www.colorado.edu\/ecenter\/2021\/03\/18\/recycling-and-climate-change. Last accessed 9 June, 2024","key":"11592_CR4"},{"unstructured":"The National Environment Agency, Waste Statistics and Overall Recycling. https:\/\/www.nea.gov.sg\/our-services\/waste-management\/waste-statistics-and-overall-recycling. Last accessed 9 June, 2024","key":"11592_CR5"},{"key":"11592_CR6","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.gr.2024.04.010","volume":"132","author":"Q Feng","year":"2024","unstructured":"Feng Q, Usman M, Saqib N, Mentel U (2024) Modelling the contribution of green technologies, renewable energy, economic complexity, and human capital in environmental sustainability: evidence from BRICS countries. Gondwana Res 132:168\u2013181","journal-title":"Gondwana Res"},{"unstructured":"Glass Packaging Institute (GPI) website, Glass Container Recycling Loop. https:\/\/www.gpi.org\/glass-recycling-facts. Last accessed 9 June, 2024","key":"11592_CR7"},{"unstructured":"Great Lakes Electronics website, Tokyo 2020 Olympic Medals Made From E-waste. https:\/\/www.ewaste1.com\/tokyo-2020-olympic-medals\/. Last accessed 9 June, 2024","key":"11592_CR8"},{"issue":"3","key":"11592_CR9","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/s10163-021-01182-y","volume":"23","author":"M Erkinay Ozdemir","year":"2021","unstructured":"Erkinay Ozdemir M, Ali Z, Subeshan B, Asmatulu E (2021) Applying machine learning approach in recycling. J Mater Cycles Waste Manag 23(3):855\u2013871","journal-title":"J Mater Cycles Waste Manag"},{"unstructured":"McKinsey sustainability website, Artificial intelligence, and the circular economy: AI as a tool to accelerate the transition. https:\/\/www.mckinsey.com\/business-functions\/sustainability\/our-insights\/artificial-intelligence-and-the-circular-economy-ai-as-a-tool-to-accelerate-the-transition. Last accessed 9 June, 2024","key":"11592_CR10"},{"key":"11592_CR11","first-page":"1","volume":"15","author":"S Salman","year":"2023","unstructured":"Salman S, Richardson E, Galvan E, Mooney P (2023) The role of artificial intelligence within circular economy activities\u2014a view from Ireland. Sustainability MDPI 15:1\u201318","journal-title":"Sustainability MDPI"},{"doi-asserted-by":"crossref","unstructured":"Kandpal N, Singhal N, Lavanya HV, Jain R, Singh R, Gaur A (2025) Utilizing artificial intelligence and machine learning for enhanced recycling efforts. In: AI technologies for enhancing recycling processes. IGI Global Scientific Publishing, pp, 65\u201382","key":"11592_CR12","DOI":"10.4018\/979-8-3693-7282-1.ch004"},{"doi-asserted-by":"crossref","unstructured":"Chattaraj S, Mitra D, Madan A, Pellegrini M, Choudhury T (2025) Conclusion of AI technologies for enhancing recycling processes. In: AI technologies for enhancing recycling processes. IGI Global Scientific Publishing, pp 531\u2013536","key":"11592_CR13","DOI":"10.4018\/979-8-3693-7282-1.ch022"},{"doi-asserted-by":"crossref","unstructured":"Asha V, Govindaraj M, Kolambkar ML, Mithuna P, Prasad A (2024) Classification of plastic waste products using deep learning. In: 2024 1st International conference on cognitive, green and ubiquitous computing (IC-CGU). IEEE, pp 1\u20136","key":"11592_CR14","DOI":"10.1109\/IC-CGU58078.2024.10530692"},{"issue":"Mar 1","key":"11592_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2025.109933","volume":"230","author":"A Lakhouit","year":"2025","unstructured":"Lakhouit A, Rashed WS, Abbas SY, Shaban M (2025) Integrating machine learning for precision agriculture waste estimation and sustainability enhancement. Comput Electron Agric 230(Mar 1):109933","journal-title":"Comput Electron Agric"},{"issue":"2","key":"11592_CR16","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/s12649-024-02697-9","volume":"16","author":"C Lytras","year":"2025","unstructured":"Lytras C, Lyberatos V, Lytras G, Papadopoulou K, Vlysidis A, Lyberatos G (2025) Development of a model composting process for food waste in an island community and use of machine learning models to predict its performance. Waste Biomass Valor 16(2):683\u2013700","journal-title":"Waste Biomass Valor"},{"doi-asserted-by":"publisher","unstructured":"Srivastava SK, Shrivastava R (2025) Modeling for copper recovery from E-waste by using machine learning technique: an approach for the circular economy. Curr Analyt Chem.  https:\/\/doi.org\/10.2174\/0115734110343714241130170537","key":"11592_CR17","DOI":"10.2174\/0115734110343714241130170537"},{"doi-asserted-by":"crossref","unstructured":"Ma Y, Chen S, Ermon S, Lobel DB (2024) Transfer learning in environmental remote sensing. Remote Sens Environ 301(1)","key":"11592_CR18","DOI":"10.1016\/j.rse.2023.113924"},{"doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","key":"11592_CR19","DOI":"10.1109\/CVPR.2017.195"},{"doi-asserted-by":"crossref","unstructured":"Sinha D, El-Sharkawy M (2019) Thin mobileNet: an enhanced mobile architecture. In: IEEE 10th annual ubiquitous computing, electronics and mobile communication conference (UEMCON). IEEE, pp 0280\u20130285","key":"11592_CR20","DOI":"10.1109\/UEMCON47517.2019.8993089"},{"unstructured":"Sashaank S, WASTE CLASSIFICATION. https:\/\/www.kaggle.com\/datasets\/techsash\/waste-classification-data. Last accessed 9 June, 2024","key":"11592_CR21"},{"issue":"142","key":"11592_CR22","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.wasman.2022.02.009","volume":"1","author":"W Lu","year":"2022","unstructured":"Lu W, Chen J (2022) Computer vision for solid waste sorting: a critical review of academic research. Waste Manag 1(142):29\u201343","journal-title":"Waste Manag"},{"issue":"86","key":"11592_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eiar.2020.106492","volume":"1","author":"KH Yu","year":"2021","unstructured":"Yu KH, Zhang Y, Li D, Montenegro-Marin CE, Kumar PM (2021) Environmental planning is based on reducing, reusing, recycling, and recovering using artificial intelligence. Environ Impact Assess Rev 1(86):106492","journal-title":"Environ Impact Assess Rev"},{"key":"11592_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.155389","volume":"20","author":"L Andeobu","year":"2022","unstructured":"Andeobu L, Wibowo S, Grandhi S (2022) Artificial intelligence applications for sustainable solid waste management practices in Australia: a systematic review. Sci Total Environ 20:155389","journal-title":"Sci Total Environ"},{"issue":"169","key":"11592_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.resconrec.2021.105543","volume":"1","author":"S Zhang","year":"2021","unstructured":"Zhang S, Chen Y, Yang Z, Gong H (2021) Computer vision-based two-stage waste recognition-retrieval algorithm for waste classification. Resour Conserv Recycl 1(169):105543","journal-title":"Resour Conserv Recycl"},{"issue":"Mar 1","key":"11592_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2021.114405","volume":"305","author":"Z Dong","year":"2022","unstructured":"Dong Z, Chen J, Lu W (2022) Computer vision to recognize construction waste compositions: a novel boundary-aware transformer (BAT) model. J Environ Manage 305(Mar 1):114405","journal-title":"J Environ Manage"},{"issue":"126","key":"11592_CR27","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.wasman.2021.03.017","volume":"1","author":"S Liang","year":"2021","unstructured":"Liang S, Gu Y (2021) A deep convolutional neural network to simultaneously localize and recognize waste types in images. Waste Manag 1(126):247\u2013257","journal-title":"Waste Manag"},{"issue":"164","key":"11592_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.resconrec.2020.105132","volume":"1","author":"WL Mao","year":"2021","unstructured":"Mao WL, Chen WC, Wang CT, Lin YH (2021) Recycling waste classification using optimized convolutional neural network. Resour Conserv Recycl 1(164):105132","journal-title":"Resour Conserv Recycl"},{"issue":"171","key":"11592_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.resconrec.2021.105636","volume":"1","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Zhang X, Mu X, Wang Z, Tian R, Wang X, Liu X (2021) Recyclable waste image recognition based on deep learning. Resour Conserv Recycl 1(171):105636","journal-title":"Resour Conserv Recycl"},{"issue":"Nov 1","key":"11592_CR30","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.wasman.2021.08.028","volume":"135","author":"C Wang","year":"2021","unstructured":"Wang C, Qin J, Qu C, Ran X, Liu C, Chen B (2021) A smart municipal waste management system based on deep learning and the internet of things. Waste Manag 135(Nov 1):20\u201329","journal-title":"Waste Manag"},{"issue":"135","key":"11592_CR31","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.wasman.2021.08.038","volume":"1","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Yang Q, Zhang X, Bao Q, Su J, Liu X (2021) Waste image classification based on transfer learning and convolutional neural network. Waste Manag 1(135):150\u2013157","journal-title":"Waste Manag"},{"issue":"7","key":"11592_CR32","doi-asserted-by":"publisher","first-page":"178631","DOI":"10.1109\/ACCESS.2019.2959033","volume":"11","author":"AH Vo","year":"2019","unstructured":"Vo AH, Vo MT, Le T (2019) A novel framework for trash classification using deep transfer learning. IEEE Access 11(7):178631\u2013178639","journal-title":"IEEE Access"},{"issue":"193","key":"11592_CR33","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.wasman.2024.12.023","volume":"1","author":"MS Islam","year":"2025","unstructured":"Islam MS, Sumon MS, Majid ME, Kashem SB, Nashbat M, Ashraf A, Khandakar A, Kunju AK, Hasan-Zia M, Chowdhury ME (2025) ECCDN-Net: a deep learning-based technique for efficient organic and recyclable waste classification. Waste Manag 1(193):363\u2013375","journal-title":"Waste Manag"},{"unstructured":"Puchianu DC (2024) Waste classification using vision transformers. Scientific Papers. Series E. Land reclamation, earth observation and surveying, environmental engineering 13, 727\u2013733","key":"11592_CR34"},{"issue":"2","key":"11592_CR35","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1504\/IJCAST.2024.143879","volume":"1","author":"S Kunwar","year":"2025","unstructured":"Kunwar S, Alade AS (2025) Deep learning in waste management: a brief survey. Int J Complex Appl Sci Technol 1(2):125\u2013141","journal-title":"Int J Complex Appl Sci Technol"},{"issue":"1","key":"11592_CR36","first-page":"10","volume":"5","author":"CG Samal","year":"2025","unstructured":"Samal CG, Biswal DR, Udgata G, Pradhan SK (2025) Estimation, classification, and prediction of construction and demolition waste using machine learning for sustainable waste management: a critical review. Constr Mater 5(1):10","journal-title":"Constr Mater"},{"issue":"6","key":"11592_CR37","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10462-025-11196-9","volume":"58","author":"I Dawar","year":"2025","unstructured":"Dawar I, Srivastava A, Singal M, Dhyani N, Rastogi S (2025) A systematic literature review on municipal solid waste management using machine learning and deep learning. Artif Intell Rev 58(6):183","journal-title":"Artif Intell Rev"},{"unstructured":"Kagle website, Waste Classification-VGG16(97.00%). https:\/\/www.kaggle.com\/code\/gauravrajpal\/waste-classification-vgg16-97-00. Last accessed 7 Mar, 2024","key":"11592_CR38"},{"unstructured":"Tan M, Le Q (2019 ) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning 2019 May 24. PMLR, pp 6105\u20136114","key":"11592_CR39"},{"doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11976\u201311986","key":"11592_CR40","DOI":"10.1109\/CVPR52688.2022.01167"},{"unstructured":"Arkadiy S, Drinking Waste Classification. https:\/\/www.kaggle.com\/datasets\/arkadiyhacks\/drinking-waste-classification. Access Mar, 2025","key":"11592_CR41"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11592-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11592-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11592-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T16:27:45Z","timestamp":1762014465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11592-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,11]]},"references-count":41,"journal-issue":{"issue":"33","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["11592"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11592-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,10,11]]},"assertion":[{"value":"24 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}