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.
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.
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.
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.
6. Siddique R, Khatib J, Kaur I. Use of recycled plastic in concrete: a review. Waste Manag 2008;28:1835-52.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
20. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012;25:1097-105.
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.
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.
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.
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.
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.
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.
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].
29. Han X, Zhong Y, Cao L, Zhang L. Pre-trained AlexNet Architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sensing 2017;9:848.
30. Talo M. Convolutional neural networks for multi-class histopathology image classification. 2019. Available from: https://arxiv.org/ftp/arxiv/papers/1903/1903.10035.pdf [Last accessed on 24 Jan 2022].
31. Go JH, Jan T, Mohanty M, Patel OP, Puthal D, Prasad M. Visualization approach for malware classification with ResNeXt. 2020 IEEE Congress on Evolutionary Computation (CEC). 2020 Jul 19-24; Glasgow, UK. IEEE; 2020. p. 1-7.
32. Seidaliyeva U, Akhmetov D, Ilipbayeva L, Matson ET. Real-time and accurate drone detection in a video with a static background. Sensors (Basel) 2020;20:3856.
33. Nguyen THB, Park E, Cui X, Nguyen VH, Kim H. fPADnet: small and efficient convolutional neural network for presentation attack detection. Sensors (Basel) 2018;18:2532.
34. Paszke A, Gross S, Chintala S, et al. Automatic differentiation in pytorch. Available from: https://openreview.net/pdf?id=BJJsrmfCZ [Last accessed on 24 Jan 2022].
35. You K, Long M, Wang J, Jordan MI. How does learning rate decay help modern neural networks? Available from: https://arxiv.org/abs/1908.01878 [Last accessed on 24 Jan 2022].
36. Li X, Chang D, Tian T, Cao J. Large-margin regularized Softmax cross-entropy loss. IEEE Access 2019;7:19572-8.
38. Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh KC, Roy K. Shallow convolutional neural network for COVID-19 outbreak screening using chest X-rays. Cognit Comput 2021; doi: 10.1007/s12559-020-09775-9.
39. Selvik JT, Abrahamsen EB. On the meaning of accuracy and precision in a risk analysis context. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2017;231:91-100.
40. Singh A, Príncipe JC. A loss function for classification based on a robust similarity metric. The 2010 International Joint Conference on Neural Networks (IJCNN). 2010 Jul 18-23; Barcelona, Spain. IEEE; 2010. p. 1-6.
41. Gao B, Bai L, Woo WL, Tian G. Thermography pattern analysis and separation. Appl Phys Lett 2014;104:251902.
42. Gao B, Zhang H, Woo WL, Tian GY, Bai L, Yin A. Smooth nonnegative matrix factorization for defect detection using microwave nondestructive testing and evaluation. IEEE Trans Instrum Meas 2014;63:923-34.
43. Ahmed J, Gao B, Woo WL, Zhu Y. Ensemble joint sparse low-rank matrix decomposition for thermography diagnosis system. IEEE Trans Ind Electron 2021;68:2648-58.
44. Song J, Gao B, Woo W, Tian G. Ensemble tensor decomposition for infrared thermography cracks detection system. Infrared Physics & Technology 2020;105:103203.
45. Ahmed J, Gao B, Woo WL. Sparse low-rank tensor decomposition for metal defect detection using thermographic imaging diagnostics. IEEE Trans Ind Inf 2021;17:1810-20.
