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REFERENCES
1. Hiraga K, Taniguchi I, Yoshida S, Kimura Y, Oda K. Biodegradation of waste PET: a sustainable solution for dealing with plastic
pollution. EMBO Rep 2019;20:e49365. DOI PubMed PMC
2. Alqattaf A. Plastic waste management: global facts, challenges and solutions. 2020 Second International Sustainability and Resilience
Conference: Technology and Innovation in Building Designs(51154). 2020 Nov 11-12; Sakheer, Bahrain. IEEE; 2020. p. 1-7. DOI
3. Klemeš JJ, Fan YV. Plastic replacements: win or loss? 2020 5th International Conference on Smart and Sustainable Technologies
(SpliTech). 2020 Sep 23-26; Split, Croatia. IEEE; 2020. p. 1-6. DOI
4. Backstrom J, Kumar N. Advancing the circular economy of plastics through eCommerce. Available from:
https://hdl.handle.net/1721.1/130968 [Last accessed on 24 Jan 2022].
5. Joshi C, Browning S, Seay J. Combating plastic waste via Trash to Tank. Nat Rev Earth Environ 2020;1:142-142. DOI
6. Siddique R, Khatib J, Kaur I. Use of recycled plastic in concrete: a review. Waste Manag 2008;28:1835-52. DOI PubMed
7. Jiao W, Wang Q, Cheng Y, Zhang Y. End-to-end prediction of weld penetration: a deep learning and transfer learning based method. J
Manuf Process 2021;63:191-7. DOI
8. Duan Q, Li J. Classification of common household plastic wastes combining multiple methods based on near-infrared spectroscopy.
ACS EST Eng 2021;1:1065-73. DOI
9. Masoumi H, Safavi SM, Khani Z. Identification and classification of plastic resins using near infrared reflectance. Int J Mech Ind Eng
2012;6:213-20. DOI
10. Veerasingam S, Ranjani M, Venkatachalapathy R, et al. Contributions of Fourier transform infrared spectroscopy in microplastic
pollution research: a review. Crit Rev Environ Sci Technol 2021;51:2681-743. DOI
11. Bruno EA. Automated sorting of plastics for recycling. Available from: https://www.semanticscholar.org/paper/Automated-Sorting-of-
Plastics-for-Recycling-Edward-Bruno/e6e5110c06f67171409bab3b38f742db6dc110fc [Last accessed on 24 Jan 2022].
12. Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future
directions. J Big Data 2021;8:53. DOI PubMed PMC
13. Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. 2017 International Conference on
Engineering and Technology (ICET). 2017 Aug 21-23; Antalya, Turkey. IEEE;2017. p. 1-6. DOI
14. Xie L, Wang J, Wei Z, Wang M, Tian Q. Disturblabel: regularizing CNN on the loss layer. 2016 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR). 2016 Jun 27-30; Las Vegas, NV, USA. IEEE; 2016. p. 4753-62. DOI
15. Bobulski J, Piatkowski J. PET waste classification method and plastic waste DataBase - WaDaBa. In: Choraś M, Choraś RS, editors.
Image processing and communications challenges 9. Cham: Springer International Publishing; 2018. p. 57-64. DOI
16. Bobulski J, Kubanek M. Waste classification system using image processing and convolutional neural networks. In: Rojas I, Joya G,
Catala A, editors. Advances in computational intelligence. Cham: Springer International Publishing; 2019. p. 350-61. DOI
17. Agarwal S, Gudi R, Saxena P. One-Shot learning based classification for segregation of plastic waste. 2020 Digital Image Computing:
Techniques and Applications (DICTA). 2020 Nov 29-2020 Dec 2; Melbourne, Australia. IEEE; 2020. p. 1-3. DOI
18. Chazhoor AAP, Zhu M, Ho ES, Gao B, Woo WL. Intelligent classification of different types of plastics using deep transfer learning.
Available from: https://researchportal.northumbria.ac.uk/ws/portalfiles/portal/55869518/ROBOVIS_2021_33_CR.pdf [Last accessed
on 24 Jan 2022].
19. Guo Y, Zhang L, Hu Y, He X, Gao J. MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe B, Matas J,
Sebe N, Welling M, editors. Computer Vision - ECCV 2016. Cham: Springer International Publishing; 2016. p. 87-102. DOI
20. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst
2012;25:1097-105. DOI
21. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). 2016 Jun 27-30; Las Vegas, NV, USA. IEEE; 2016. p. 770-8. DOI
22. Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR). 2017 Jul 21-26; Honolulu, HI, USA. IEEE; 2017. p. 5987-95. DOI
23. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
and< 0.5 MB model size. Available from: https://arxiv.org/abs/1602.07360 [Last accessed on 24 Jan 2022].
24. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. Mobilenetv2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF
Conference on Computer Vision and Pattern Recognition. 2018 Jun 18-23; Salt Lake City, UT, USA. IEEE; 2018. p. 4510-20. DOI
25. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR). 2017 Jul 21-26; Honolulu, HI, USA. IEEE; 2017. p. 2261-9. DOI
26. Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. In: Kůrková V, Manolopoulos Y, Hammer B,
Iliadis L, Maglogiannis I, editors. Artificial neural networks and machine learning - ICANN 2018. Cham: Springer International
Publishing; 2018. p. 270-9. DOI
27. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. 2009 IEEE Conference on
Computer Vision and Pattern Recognition. 2009 Jun 20-25; Miami, FL, USA. IEEE; 2009. p. 248-55. DOI
28. Brock A, Lim T, Ritchie JM, Weston N. Freezeout: accelerate training by progressively freezing layers. Available from:
https://arxiv.org/abs/1706.04983 [Last accessed on 24 Jan 2022].