{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:37:05Z","timestamp":1778693825752,"version":"3.51.4"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"King Abdullah II Fund for Development","award":["2\/24"],"award-info":[{"award-number":["2\/24"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07931-2","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T06:18:40Z","timestamp":1759731520000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep transfer learning for sustainable waste management: Real-time waste segregation apparatus using a two-phase CNN framework"],"prefix":"10.1007","volume":"81","author":[{"given":"Natheer","family":"Almtireen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelrazzaq A.","family":"Abuhejleh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mutaz","family":"Ryalat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hisham","family":"Elmoaqet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghaith","family":"Al-refai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,6]]},"reference":[{"key":"7931_CR1","unstructured":"United Nations Environment Programme & International Solid Waste Association. Global Waste Management Outlook 2024 - Beyond an Age of Waste: Turning Rubbish into a Resource. United Nations Environment Programme; 2024. Technical Report: https:\/\/wedocs.unep.org\/20.500.11822\/44939. [Accessed 1 October 2024]"},{"key":"7931_CR2","volume-title":"Global Plastics Outlook: Policy Scenarios to 2060","author":"Organisation for Economic Co-operation and Development","year":"2022","unstructured":"Organisation for Economic Co-operation and Development (2022) Global Plastics Outlook: Policy Scenarios to 2060. OECD Publishing, Paris"},{"key":"7931_CR3","unstructured":"Statista.: Global Population and Municipal Solid Waste Generation Shares in 2018, by Select Country. Statista: https:\/\/www.statista.com\/statistics\/1026652. [Accessed 12 November 2024]"},{"key":"7931_CR4","doi-asserted-by":"crossref","unstructured":"Kaza S, Yao LC, Bhada-Tata P, Van\u00a0Woerden F. What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. World Bank Publications; 2018","DOI":"10.1596\/978-1-4648-1329-0"},{"key":"7931_CR5","unstructured":"Environmental Protection Agency.: Largest Municipal Solid Waste Landfills in the United States in 2024, by Design Capacity [Graph]. Statista: https:\/\/www.statista.com\/statistics\/1360264. [Accessed 13 November 2024]"},{"key":"7931_CR6","unstructured":"Environmental Protection Agency. Advancing Sustainable Materials Management: 2018 Tables and Figures; 2020. EPA Report: https:\/\/www.epa.gov\/sites\/default\/files\/2021-01\/documents\/2018_ff_fact_sheet_dec_2020_fnl_508.pdf. [Accessed 10 November 2024]"},{"key":"7931_CR7","unstructured":"EA-Earth Action.: Plastic Overshoot Day \u2013 Report 2024. Switzerland. [Accessed 16 November 2024]. Available from: https:\/\/plasticovershoot.earth\/report-2024\/"},{"key":"7931_CR8","unstructured":"Fleck, Anna.: Cigarette Butts & Food Packaging Instead of Pristine Beaches [Digital image]. Statista: https:\/\/www.statista.com\/chart\/30163. [Accessed 29 December 2024]"},{"key":"7931_CR9","unstructured":"Eurostat.: Treatment of Waste by Waste Category, Hazardousness and Waste Management Operations [Dataset]. Eurostat"},{"key":"7931_CR10","unstructured":"Environmental Protection Agency.: Distribution of Municipal Solid Waste Disposal in the United States in 2018 [Graph]. Statista: https:\/\/www.statista.com\/statistics\/1220833. [Accessed 14 November 2024]"},{"key":"7931_CR11","unstructured":"Environmental Protection Agency.: Number of Hazardous Waste Sites in the United States as of June 2024, by State [Graph]. Statista: https:\/\/www.statista.com\/statistics\/1147665. [Accessed 14 November 2024]"},{"key":"7931_CR12","unstructured":"Accounts Chamber of the Russian Federation.: Waste Reform. Bulletin of the Accounts Chamber of the Russian Federation, p.\u00a014. https:\/\/www.statista.com\/statistics\/1176725 [Accessed 13 November 2024]"},{"key":"7931_CR13","unstructured":"Organisation for Economic Co-operation and Development.: Rate of Incineration of Municipal Waste in Japan from 2012 to 2021 [Graph]. Statista: https:\/\/www.statista.com\/statistics\/1171265. [Accessed: 13 November 2024]"},{"key":"7931_CR14","unstructured":"TrashOut.: Estimated Number of Illegal Dumps Containing Plastic Waste in Europe as of October 2024, by select Country [Graph]. Statista: https:\/\/www.statista.com\/statistics\/990554\/. [Accessed: 13 November 2024]"},{"key":"7931_CR15","unstructured":"American Lung Association. State of the Air; 2024. Statista. https:\/\/www.statista.com\/statistics\/1358105 [Accessed 14 November 2024]"},{"key":"7931_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2023.108033","volume":"178","author":"KL Smalling","year":"2023","unstructured":"Smalling KL, Romanok KM, Bradley PM, Morriss MC, Gray JL, Kanagy LK et al (2023) Per- and polyfluoroalkyl substances (pfas) in united states tapwater: comparison of underserved private-well and public-supply exposures and associated health implications. Environ Int 178:108033","journal-title":"Environ Int"},{"key":"7931_CR17","doi-asserted-by":"crossref","unstructured":"Pereira W, Parulekar S, Phaltankar S, Kamble V. Smart Bin (Waste Segregation and Optimisation). In: 2019 Amity International Conference on Artificial Intelligence (AICAI). IEEE; 2019. p. 274\u2013279","DOI":"10.1109\/AICAI.2019.8701350"},{"issue":"2","key":"7931_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.54476\/iimrj397","volume":"1","author":"V Sowndharya","year":"2019","unstructured":"Sowndharya V, Savitha P, Hebziba Jeba Rani S, Rani J (2019) Smart waste segregation and monitoring system using IoT. Int Res J Multidiscip Techn 1(2):1\u201310","journal-title":"Int Res J Multidiscip Techn"},{"key":"7931_CR19","first-page":"1","volume":"2021","author":"G Sanathkumar","year":"2021","unstructured":"Sanathkumar G, Nagesh KJ, Hadimani G, Charanraj BR, Smart HPB, Segregation W (2021) In Ieee 9th region 10 humanitarian technology conference (r10-htc). IEEE 2021:1\u20136","journal-title":"IEEE"},{"key":"7931_CR20","doi-asserted-by":"crossref","unstructured":"Jayson M, Hiremath S, Lakshmi HR. SmartBin-Automatic Waste Segregation and Collection. In: Proceedings of the 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC). IEEE; 2018. p. 1\u20134","DOI":"10.1109\/ICAECC.2018.8479531"},{"issue":"2","key":"7931_CR21","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/S0736-5845(01)00035-7","volume":"18","author":"HI Bozma","year":"2002","unstructured":"Bozma HI, Yal\u00e7\u0131n H (2002) Visual processing and classification of items on a moving conveyor: a selective perception approach. Robot Comput-Integr Manuf 18(2):125\u2013133","journal-title":"Robot Comput-Integr Manuf"},{"key":"7931_CR22","unstructured":"UiPath.: Industry Transformation through AI-driven Automation Worldwide in 2024 [Graph]. Statista: https:\/\/www.statista.com\/statistics\/1456571. [Accessed 6 November 2024]"},{"key":"7931_CR23","unstructured":"360iResearch.: Smart Waste Management Market Size Worldwide in 2022, with a Forecast from 2023 to 2030 (in Million U.S. Dollars) [Graph]. Statista: https:\/\/www.statista.com\/statistics\/1286398 [Accessed 14 November 2024]"},{"issue":"28","key":"7931_CR24","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7378","volume":"34","author":"KO Mohammed Aarif","year":"2022","unstructured":"Mohammed Aarif KO, Mohamed Yousuff C, Mohammed Hashim BA, Mohamed Hashim C, Sivakumar P (2022) Smart bin: waste segregation system using deep learning-internet of things for sustainable smart cities. Concurr Comput Pract Exp 34(28):e7378","journal-title":"Concurr Comput Pract Exp"},{"issue":"1","key":"7931_CR25","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/electronics10010014","volume":"10","author":"S Kumar","year":"2020","unstructured":"Kumar S, Yadav D, Gupta H, Verma OP, Ansari IA, Ahn CW (2020) A novel yolov3 algorithm-based deep learning approach for waste segregation: towards smart waste management. Electronics 10(1):14","journal-title":"Electronics"},{"key":"7931_CR26","doi-asserted-by":"crossref","unstructured":"Rajakumaran G, Usharani S, Vincent C, Sujatha M. Smart Waste Management: Waste Segregation using Machine Learning. In: Journal of Physics: Conference Series. vol. 2471. IOP Publishing; 2023. p. 012030","DOI":"10.1088\/1742-6596\/2471\/1\/012030"},{"issue":"2","key":"7931_CR27","first-page":"86","volume":"4","author":"A Shenoy","year":"2022","unstructured":"Shenoy A, Ranjitha P, Haripriya R, Vinutha CB (2022) Smart waste segregation system using convolutional neural networks. J Electr Eng Autom 4(2):86\u201399","journal-title":"J Electr Eng Autom"},{"issue":"11","key":"7931_CR28","doi-asserted-by":"publisher","first-page":"56","DOI":"10.35940\/ijitee.K7700.0991120","volume":"9","author":"DN Patel","year":"2020","unstructured":"Patel DN, Dasari C, Chembarpu A, Sasi A, Usha CS (2020) Smart waste segregation using ml techniques. Int J Innov Technol Explor Eng 9(11):56\u201359","journal-title":"Int J Innov Technol Explor Eng"},{"key":"7931_CR29","doi-asserted-by":"crossref","unstructured":"Varudandi S, Mehta R, Mahetalia J, Parmar H, Smart SKA, Management W, System S, that Uses Internet of Things, Machine Learning, and Android Application. In, (2021) 6th international conference for convergence in technology (i2ct). IEEE 2021:1\u20136","DOI":"10.1109\/I2CT51068.2021.9418125"},{"key":"7931_CR30","doi-asserted-by":"crossref","unstructured":"Divakar S, Bhattacharjee A, Soni VK, Priyadarshini R, Barik RK, Roy DS. An IoT-Enabled Smart Waste Segregation System. In: Nayak S, Rout JK, editors. Machine Vision and Augmented Intelligence-Theory and Applications: Select Proceedings of MAI 2021. Singapore: Springer; 2021. p. 101\u2013108","DOI":"10.1007\/978-981-16-5078-9_9"},{"key":"7931_CR31","doi-asserted-by":"crossref","unstructured":"Zubair M, Mathur Y, Rathore H, Gupta P, Banerjee S. Smart Waste Bin: Mechanical and AI Based Waste Segregation. In: Proceedings of the 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS). IEEE; 2022. p. 63\u201366","DOI":"10.1109\/ICAIS53314.2022.9742806"},{"issue":"25","key":"7931_CR32","doi-asserted-by":"publisher","first-page":"39617","DOI":"10.1007\/s11042-021-11537-0","volume":"82","author":"MA Mohammed","year":"2023","unstructured":"Mohammed MA, Abdulhasan MJ, Kumar NM, Abdulkareem KH, Mostafa SA, Maashi MS et al (2023) Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities. Multimed Tools Appl 82(25):39617\u201339632","journal-title":"Multimed Tools Appl"},{"issue":"16","key":"7931_CR33","doi-asserted-by":"publisher","first-page":"10226","DOI":"10.3390\/su141610226","volume":"14","author":"SM Cheema","year":"2022","unstructured":"Cheema SM, Hannan A, Pires IM (2022) Smart waste management and classification systems using cutting edge approach. Sustainability 14(16):10226","journal-title":"Sustainability"},{"key":"7931_CR34","doi-asserted-by":"crossref","unstructured":"Strollo E, Sansonetti G, Mayer MC, Limongelli C, Micarelli A. An AI-Based Approach to Automatic Waste Sorting. In: HCI International 2020-Posters: 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19\u201324, 2020, Proceedings, Part I. Springer; 2020. p. 662\u2013669","DOI":"10.1007\/978-3-030-50726-8_86"},{"issue":"1","key":"7931_CR35","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K et al (2019) A guide to deep learning in healthcare. Nat Med 25(1):24\u201329","journal-title":"Nat Med"},{"issue":"4","key":"7931_CR36","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1109\/TPAMI.2017.2700390","volume":"40","author":"C Ding","year":"2018","unstructured":"Ding C, Tao D (2018) Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans Pattern Anal Mach Intell 40(4):1002\u20131014","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7931_CR37","doi-asserted-by":"crossref","unstructured":"Chen LC, Hermans A, Papandreou G, Schroff F, Wang P, Adam H. MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2018. p. 4013\u20134022","DOI":"10.1109\/CVPR.2018.00422"},{"key":"7931_CR38","unstructured":"Lilhore UK, Simaiya S, Dalal S, Radulescu M, Balsalobre-Lorente D. Intelligent Waste Sorting for Sustainable Environment: A Hybrid Deep Learning and Transfer Learning Model. Gondwana Research. 2024;"},{"key":"7931_CR39","unstructured":"Shanmugamani R. Deep Learning for Computer Vision: Expert Techniques to Train Advanced Neural Networks Using TensorFlow and Keras. Packt Publishing; 2018"},{"key":"7931_CR40","doi-asserted-by":"crossref","unstructured":"Aral RA, Keskin \u015eR, Kaya M, Hac\u0131\u00f6merog\u0306lu M. Classification of TrashNet Dataset Based on Deep Learning Models. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE; 2018. p. 2058\u20132062","DOI":"10.1109\/BigData.2018.8622212"},{"key":"7931_CR41","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.wasman.2021.08.038","volume":"135","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 135:150\u2013157","journal-title":"Waste Manag"},{"key":"7931_CR42","unstructured":"White G, Cabrera C, Palade A, Li F, Clarke S. WasteNet: Waste Classification at the Edge for Smart Bins. arXiv preprint arXiv:2006.05873. 2020; arXiv preprint arXiv:2006.05873"},{"issue":"12","key":"7931_CR43","doi-asserted-by":"publisher","first-page":"7222","DOI":"10.3390\/su14127222","volume":"14","author":"M Malik","year":"2022","unstructured":"Malik M, Sharma S, Uddin M, Chen CL, Wu CM, Soni P et al (2022) Waste classification for sustainable development using image recognition with deep learning neural network models. Sustainability 14(12):7222","journal-title":"Sustainability"},{"key":"7931_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107459","volume":"153","author":"M To\u011fa\u00e7ar","year":"2020","unstructured":"To\u011fa\u00e7ar M, Ergen B, C\u00f6mert Z (2020) Waste classification using autoencoder network with integrated feature selection method in convolutional neural network models. Measurement 153:107459","journal-title":"Measurement"},{"issue":"5","key":"7931_CR45","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.3390\/app12052281","volume":"12","author":"FS Alsubaei","year":"2022","unstructured":"Alsubaei FS, Al-Wesabi FN, Hilal AM (2022) Deep learning-based small object detection and classification model for garbage waste management in smart cities and iot environment. Appl Sci 12(5):2281","journal-title":"Appl Sci"},{"key":"7931_CR46","doi-asserted-by":"crossref","unstructured":"Handhayani T, Hendryli J. Leboh: An Android Mobile Application for Waste Classification Using TensorFlow Lite. In: Proceedings of the SAI Intelligent Systems Conference. Springer; 2022. p. 53\u201367","DOI":"10.1007\/978-3-031-16075-2_4"},{"issue":"1","key":"7931_CR47","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s42492-023-00146-3","volume":"6","author":"D Zheng","year":"2023","unstructured":"Zheng D, Wang R, Duan Y, Pang PCI, Tan T (2023) Focus-rcnet: a lightweight recyclable waste classification algorithm based on focus and knowledge distillation. Vis Comput Ind Biomed Art 6(1):19","journal-title":"Vis Comput Ind Biomed Art"},{"issue":"9","key":"7931_CR48","doi-asserted-by":"publisher","first-page":"3193","DOI":"10.1080\/00207543.2023.2225652","volume":"62","author":"M Shahin","year":"2024","unstructured":"Shahin M, Chen FF, Hosseinzadeh A, Bouzary H, Shahin A (2024) Waste reduction via image classification algorithms: beyond the human eye with an ai-based vision. Int J Prod Res 62(9):3193\u20133211","journal-title":"Int J Prod Res"},{"key":"7931_CR49","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1016\/j.procs.2025.03.273","volume":"260","author":"TP Swaminathan","year":"2025","unstructured":"Swaminathan TP, Silver C, Akilan T, Kumar J (2025) Benchmarking deep learning models on nvidia jetson nano for real-time systems: an empirical investigation. Procedia Comput Sci 260:906\u2013913","journal-title":"Procedia Comput Sci"},{"issue":"18","key":"7931_CR50","doi-asserted-by":"publisher","first-page":"9171","DOI":"10.3390\/app12189171","volume":"12","author":"Y Hu","year":"2022","unstructured":"Hu Y, Xu Y, Zhuang H, Weng Z, Lin Z (2022) Machine learning techniques and systems for mask-face detection-survey and a new ood-mask approach. Appl Sci 12(18):9171","journal-title":"Appl Sci"},{"issue":"5","key":"7931_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3508391","volume":"21","author":"E Jeong","year":"2022","unstructured":"Jeong E, Kim J, Ha S (2022) Tensorrt-based framework and optimization methodology for deep learning inference on jetson boards. ACM Trans Embed Comput Syst 21(5):1\u201326","journal-title":"ACM Trans Embed Comput Syst"},{"issue":"3","key":"7931_CR52","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.3390\/app15031550","volume":"15","author":"N Almtireen","year":"2025","unstructured":"Almtireen N, Reddy V, Sutton M, Nedvidek A, Karn C, Ryalat M et al (2025) Plc-controlled intelligent conveyor system with ai-enhanced vision for efficient waste sorting. Appl Sci 15(3):1550","journal-title":"Appl Sci"},{"key":"7931_CR53","doi-asserted-by":"crossref","unstructured":"Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Pearson; 2020","DOI":"10.1093\/oso\/9780190905033.003.0012"},{"issue":"6","key":"7931_CR54","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386\u2013408","journal-title":"Psychol Rev"},{"issue":"2","key":"7931_CR55","first-page":"91","volume":"6","author":"M Kaya","year":"2023","unstructured":"Kaya M, Ulut\u00fcrk S, \u00c7etin Kaya Y, Alt\u0131nta\u015f O, Turan B (2023) Optimization of several deep cnn models for waste classification. Sak Univ J Comput Inf Sci 6(2):91\u2013104","journal-title":"Sak Univ J Comput Inf Sci"},{"key":"7931_CR56","first-page":"29935","volume":"34","author":"SA Rebuffi","year":"2021","unstructured":"Rebuffi SA, Gowal S, Calian DA, Stimberg F, Wiles O, Mann TA (2021) Data augmentation can improve robustness. Adv Neural Inf Process Syst 34:29935\u201329948","journal-title":"Adv Neural Inf Process Syst"},{"key":"7931_CR57","unstructured":"Fei-Fei L, Deng J, Russakovsky O, Berg A, Li K.: ImageNet. Stanford University, Princeton University, and collaborators. https:\/\/www.image-net.org\/about.php. [Accessed 29 December 2024]"},{"key":"7931_CR58","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H. How Transferable are Features in Deep Neural Networks? Advances in Neural Information Processing Systems. 2014;27"},{"key":"7931_CR59","unstructured":"Keras Team.: Keras Applications. https:\/\/keras.io\/api\/applications\/. [Accessed 5 March 2024]"},{"key":"7931_CR60","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV. Learning Transferable Architectures for Scalable Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2018. p. 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"key":"7931_CR61","unstructured":"Tan M, Le QV. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML 2019). PMLR; 2019. p. 6105\u20136114"},{"key":"7931_CR62","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, et\u00a0al. Searching for MobileNetV3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE; 2019. p. 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"7931_CR63","unstructured":"Kingma DP, Ba, Jimmy L. Adam: A Method for Stochastic Optimization. In: Proceedings of the International Conference on Learning Representations (ICLR); 2015."},{"key":"7931_CR64","doi-asserted-by":"publisher","first-page":"318","DOI":"10.7551\/mitpress\/5236.001.0001","volume-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, vol 1. MIT Press, pp 318\u2013362"},{"key":"7931_CR65","unstructured":"Serra J, Sur\u00eds D, Miron M, Karatzoglou A. Overcoming Catastrophic Forgetting with Hard Attention to the Task. In: Proceedings of the International Conference on Machine Learning (ICML). PMLR; 2018.n p. 4548\u20134557"},{"key":"7931_CR66","doi-asserted-by":"crossref","unstructured":"Prechelt L. Early Stopping-But When? In: Orr GB, M\u00fcller KR, editors. Neural Networks: Tricks of the Trade. vol. 1524 of Lecture Notes in Computer Science. Springer; 1998. p. 55\u201369","DOI":"10.1007\/3-540-49430-8_3"},{"issue":"1","key":"7931_CR67","first-page":"335","volume":"15","author":"G Raskutti","year":"2014","unstructured":"Raskutti G, Wainwright MJ, Yu B (2014) Early stopping and non-parametric regression: an optimal data-dependent stopping rule. J Mach Learn Res 15(1):335\u2013366","journal-title":"J Mach Learn Res"},{"key":"7931_CR68","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). IEEE; 2017. p. 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"key":"7931_CR69","doi-asserted-by":"crossref","unstructured":"Erickson BJ, Kitamura F. Magician\u2019s Corner: 9. Performance Metrics for Machine Learning Models. Radiology: Artificial Intelligence. 2021;3(3):e200126","DOI":"10.1148\/ryai.2021200126"},{"key":"7931_CR70","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern Recognition and Machine Learning. Springer, Information Science and Statistics. New York"},{"key":"7931_CR71","doi-asserted-by":"crossref","unstructured":"Diet F, Kassem\u00a0Sbeyti M, Karg M. Prediction Accuracy & Reliability: Classification and Object Localization Under Distribution Shift. In: Pedrycz W, Chen Sm, editors. Machine Learning and Granular Computing: A Synergistic Design Environment. Cham: Springer; 2024. p. 263\u2013301","DOI":"10.1007\/978-3-031-66842-5_9"},{"key":"7931_CR72","unstructured":"Tryolabs. Machine Learning on Edge Devices: Benchmark Report. Edge AI and Vision Alliance. 2019; https:\/\/www.edge-ai-vision.com\/2019\/11\/machine-learning-on-edge-devices-benchmark-report. [Accessed 5 January 2024]"},{"issue":"2","key":"7931_CR73","doi-asserted-by":"publisher","first-page":"51","DOI":"10.5626\/JCSE.2023.17.2.51","volume":"17","author":"R Tobiasz","year":"2023","unstructured":"Tobiasz R, Wilczynski G, Graszka P, Czechowski N, Luczak S (2023) Edge devices inference performance comparison. J Comput Sci Eng 17(2):51\u201359","journal-title":"J Comput Sci Eng"},{"key":"7931_CR74","unstructured":"International Organization for Standardization.: Robots and Robotic Devices \u2013 Safety Requirements for Industrial Robots \u2013 Part 2: Robot Systems and Integration"},{"issue":"3","key":"7931_CR75","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1049\/smc2.12057","volume":"5","author":"X Li","year":"2023","unstructured":"Li X, Grammenos R (2023) Evaluation of practical edge computing cnn-based solutions for intelligent recycling bins. IET Smart Cities 5(3):194\u2013209","journal-title":"IET Smart Cities"},{"issue":"1","key":"7931_CR76","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/recycling7010009","volume":"7","author":"D Ziouzios","year":"2022","unstructured":"Ziouzios D, Baras N, Balafas V, Dasygenis M, Stimoniaris A (2022) Intelligent and real-time detection and classification algorithm for recycled materials using convolutional neural networks. Recycling 7(1):9","journal-title":"Recycling"},{"key":"7931_CR77","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.wasman.2023.02.014","volume":"162","author":"S Jin","year":"2023","unstructured":"Jin S, Yang Z, Kr\u00f3lczykg G, Liu X, Gardoni P, Li Z (2023) Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling. Waste Manag 162:123\u2013130","journal-title":"Waste Manag"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07931-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07931-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07931-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T06:19:01Z","timestamp":1759731541000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07931-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,6]]},"references-count":77,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["7931"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07931-2","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,6]]},"assertion":[{"value":"29 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 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":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1411"}}