nach oben

Journal of Material Cycles and Waste Management

Erschienen in:

17.02.2021 | REVIEW

Applying machine learning approach in recycling

verfasst von: Merve Erkinay Ozdemir, Zaara Ali, Balakrishnan Subeshan, Eylem Asmatulu

Erschienen in: Journal of Material Cycles and Waste Management | Ausgabe 3/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Waste generation has been increasing drastically based on the world’s population and economic growth. This has significantly affected human health, natural life, and ecology. The utilization of limited natural resources, and the harming of the earth in the process of mineral extraction, and waste management have far exceeded limits. The recycling rate are continuously increasing; however, assessments show that humans will be creating more waste than ever before. Some difficulties during recycling include the significant expense involved during the separation of recyclable waste from non-disposable waste. Machine learning is the utilization of artificial intelligence (AI) that provides a framework to take as a structural improvement of the fact without being programmed. Machine learning concentrates on the advancement of programs that can obtain the information and use it to learn to make future decisions. The classification and separation of materials in a mixed recycling application in machine learning is a division of AI that is playing an important role for better separation of complex waste. The primary purpose of this study is to analyze AI by focusing on machine learning algorithms used in recycling systems. This study is a compilation of the most recent developments in machine learning used in recycling industries.

Nächster Artikel A systems engineering study of integration reverse vending machines into the waste management system of Kazakhstan

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Chu Y, Huang C, Xie X et al (2018) Multilayer hybrid deep-learning method for waste classification and recycling. Comput Intell Neurosci. https://doi.org/10.1155/2018/5060857CrossRef

Kapur R (2016) Natural resources and environmental issues. J Ecosyst Ecogr 6:2–5. https://doi.org/10.4172/2157-7625.1000196CrossRef

Management Association IR (2013) Sustainable practices: concepts, methodologies, tools, and applications: concepts, methodologies, tools, and applications. IGI Global, Philadelphia

Recycling facts—greening forward. http://greeningforward.org/environmental-issues/waste/recycle/recycling-facts/. Accessed 2 Mar 2020

Saidani M, Kendall A, Yannou B et al (2019) Management of the end-of-life of light and heavy vehicles in the US: comparison with the European union in a circular economy perspective. J Mater Cycles Waste Manag 21:1449–1461. https://doi.org/10.1007/s10163-019-00897-3CrossRef

Ijaola AO, Farayibi PK, Asmatulu E (2020) Superhydrophobic coatings for steel pipeline protection in oil and gas industries: a comprehensive review. J Nat Gas Sci Eng 83:103544. https://doi.org/10.1016/j.jngse.2020.103544CrossRef

Ferronato N, Torretta V (2019) Waste mismanagement in developing countries: a review of global issues. Int J Environ Res Public Health 16:1060. https://doi.org/10.3390/ijerph16061060CrossRef

Mandadi G.K, Balakrishnan S, Asmatulu E, (2017) Hands-on Training of Engineering Students on Recycling of Electronic Waste Materials. 2017 ASEE Midwest Section Conference (Oklahoma State University-Stillwater, OK)

Kumar S, Smith SR, Fowler G et al (2017) Challenges and opportunities associated with waste management in India. R Soc Open Sci 4:160764. https://doi.org/10.1098/rsos.160764CrossRef

10.

Arebey M, Hannan MA, Basri H, Abdullah H (2009) Solid waste monitoring and management using RFID, GIS and GSM. In: SCOReD2009—proceedings of 2009 IEEE Student conference on research and development, pp 37–40. https://doi.org/10.1109/SCORED.2009.5443382

11.

Asmatulu R, Asmatulu E (2011) Importance of recycling education: a curriculum development at WSU. J Mater Cycles Waste Manag 13:131–138. https://doi.org/10.1007/s10163-011-0002-4CrossRef

12.

Roh SB, Park SB, Oh SK et al (2018) Development of intelligent sorting system realized with the aid of laser-induced breakdown spectroscopy and hybrid preprocessing algorithm-based radial basis function neural networks for recycling black plastic wastes. J Mater Cycles Waste Manag 20:1934–1949. https://doi.org/10.1007/s10163-018-0701-1CrossRef

13.

Asmatulu E, Subeshan B, Twomey J, Overcash M (2020) Increasing the lifetime of products by nanomaterial inclusions—life cycle energy implications. Int J Life Cycle Assess. https://doi.org/10.1007/s11367-020-01794-wCrossRef

14.

Ferronato N, Ragazzi M, Gorritty Portillo MA et al (2019) How to improve recycling rate in developing big cities: an integrated approach for assessing municipal solid waste collection and treatment scenarios. Environ Dev 29:94–110. https://doi.org/10.1016/j.envdev.2019.01.002CrossRef

15.

Amershi S, Cakmak M, Knox WB, Kulesza T (2014) Power to the people: the role of humans in interactive machine learning. AI Mag 35:105–120. https://doi.org/10.1609/aimag.v35i4.2513CrossRef

16.

Berral JL, Gavaldà R, Torres J (2011) Adaptive scheduling on power-aware managed data-centers using machine learning. In: Proceedings—2011 12th IEEE/ACM international conference on grid computing 2011, pp 66–73. https://doi.org/10.1109/Grid.2011.18

17.

Dai F, Nie G, Chen Y (2020) The municipal solid waste generation distribution prediction system based on FIG–GA-SVR model. J Mater Cycles Waste Manag. https://doi.org/10.1007/s10163-020-01022-5CrossRef

18.

Alexander T, Subeshan B, Asmatulu R (2020) Modifying the figure of merit of thermoelectric materials with inclusions of porous structures. Energy Ecol Environ 5:313–329. https://doi.org/10.1007/s40974-020-00183-1CrossRef

19.

Chhay L, Reyad MAH, Suy R et al (2018) Municipal solid waste generation in China: influencing factor analysis and multi-model forecasting. J Mater Cycles Waste Manag 20:1761–1770. https://doi.org/10.1007/s10163-018-0743-4CrossRef

20.

Behera SK, Meher SK, Park HS (2015) Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste. Clean Technol Environ Policy 17:443–453. https://doi.org/10.1007/s10098-014-0798-4CrossRef

21.

Schmidt J, Marques MRG, Botti S, Marques MAL (2019) Recent advances and applications of machine learning in solid-state materials science. Npj Comput Mater. https://doi.org/10.1038/s41524-019-0221-0CrossRef

22.

Meza JKS, Yepes DO, Rodrigo-Ilarri J, Cassiraga E (2019) Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon 5:e02810. https://doi.org/10.1016/j.heliyon.2019.e02810CrossRef

23.

Roh SB, Oh SK, Park EK, Choi WZ (2017) Identification of black plastics realized with the aid of Raman spectroscopy and fuzzy radial basis function neural networks classifier. J Mater Cycles Waste Manag 19:1093–1105. https://doi.org/10.1007/s10163-017-0620-6CrossRef

24.

Al-Garadi MA, Mohamed A, Al-Ali A et al (2020) A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun Surv Tutor. https://doi.org/10.1109/COMST.2020.2988293CrossRef

25.

(2019) Essential steps in waste management. https://noharm-global.org/issues/global/essential-steps-waste-management. Accessed 2 Mar 2020

26.

Ching C (2019) How to build an image classifier for waste sorting. https://towardsdatascience.com/how-to-build-an-image-classifier-for-waste-sorting-6d11d3c9c478. Accessed 2 Mar 2020

27.

Phu STP, Fujiwara T, Hoang MG et al (2019) Waste separation at source and recycling potential of the hotel industry in Hoi An city, Vietnam. J Mater Cycles Waste Manag 21:23–34. https://doi.org/10.1007/s10163-018-0807-5CrossRef

28.

de Vega CA, Benítez SO, Barreto MER (2008) Solid waste characterization and recycling potential for a university campus. Waste Manag 28:S21–S26CrossRef

29.

Anastassakis GN (2018) Solid waste separation and processing. Handb Environ Eng. https://doi.org/10.1002/9781119304418.ch21CrossRef

30.

Schmidt J, Marques MRG, Botti S, Marques MAL (2019) Recent advances and applications of machine learning in solid-state materials science. Npj Comput Mater 5:1–36. https://doi.org/10.1038/s41524-019-0221-0CrossRef

31.

Van Engelen JE, Hoos HH (2020) A survey on semi-supervised learning. Mach Learn 109:373–440MathSciNetCrossRef

32.

Bock FE, Aydin RC, Cyron CJ et al (2019) A review of the application of machine learning and data mining approaches in continuum materials mechanics. Front Mater 6:110. https://doi.org/10.3389/fmats.2019.00110CrossRef

33.

Holzinger A, Plass M, Holzinger K et al (2016) Towards interactive machine learning (iML): applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach 1 introduction and motivation for research. Lncs 9817:81–95. https://doi.org/10.1007/978-3-319-45507-5CrossRef

34.

Kourou K, Exarchos TP, Exarchos KP et al (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17. https://doi.org/10.1016/j.csbj.2014.11.005CrossRef

35.

Marblestone AH, Wayne G, Kording KP (2016) Toward an integration of deep learning and neuroscience. Front Comput Neurosci 10:1–41. https://doi.org/10.3389/fncom.2016.00094CrossRef

36.

Vamathevan J, Clark D, Czodrowski P et al (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18:463–477. https://doi.org/10.1038/s41573-019-0024-5CrossRef

37.

Ahmed Z, Zeeshan S, Mendhe D, Dong X (2020) Human gene and disease associations for clinical-genomics and precision medicine research. Clin Transl Med 10:297–318. https://doi.org/10.1002/ctm2.28CrossRef

38.

Deo RC (2015) Machine learning in medicine. Circulation 132:1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593CrossRef

39.

Nguyen G, Dlugolinsky S, Bobák M et al (2019) Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev 52:77–124. https://doi.org/10.1007/s10462-018-09679-zCrossRef

40.

Hosny A, Parmar C, Quackenbush J et al (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510. https://doi.org/10.1038/s41568-018-0016-5CrossRef

41.

Abiodun OI, Jantan A, Omolara AE et al (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4:e00938. https://doi.org/10.1016/j.heliyon.2018.e00938CrossRef

42.

Bianco MJ, Gerstoft P, Traer J et al (2019) Machine learning in acoustics: theory and applications. J Acoust Soc Am 146:3590–3628. https://doi.org/10.1121/1.5133944CrossRef

43.

Majumder A, Behera L, Member S, Subramanian VK (2016) FER using deep network-based data fusion. IEEE Trans Cybern 48:103–114CrossRef

44.

Rashidi HH, Tran NK, Betts EV et al (2019) Artificial intelligence and machine learning in pathology: the present landscape of supervised methods. Acad Pathol. https://doi.org/10.1177/2374289519873088CrossRef

45.

Najafabadi MM, Villanustre F, Khoshgoftaar TM et al (2015) Deep learning applications and challenges in big data analytics. J Big Data 2:1–21. https://doi.org/10.1186/s40537-014-0007-7CrossRef

46.

Chung Y, Haas PJ, Upfal E, Kraska T, (2018) Unknown examples & machine learning model generalization. arXiv preprint arXiv:1808.08294

47.

Alom MZ, Taha TM, Yakopcic C et al (2019) A state-of-the-art survey on deep learning theory and architectures. Electron 8:1–67. https://doi.org/10.3390/electronics8030292CrossRef

48.

Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629. https://doi.org/10.1007/s13244-018-0639-9CrossRef

49.

Sze V, Chen YH, Yang TJ, Emer JS (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105:2295–2329. https://doi.org/10.1109/JPROC.2017.2761740CrossRef

50.

Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 29:102–127. https://doi.org/10.1016/j.zemedi.2018.11.002CrossRef

51.

Zhang Q, Zhang M, Chen T et al (2019) Recent advances in convolutional neural network acceleration. Neurocomputing 323:37–51. https://doi.org/10.1016/j.neucom.2018.09.038CrossRef

52.

Xiang Y, Choi W, Lin Y, Savarese S (2017) Subcategory-Aware convolutional neural networks for object proposals and detection. In: Proceedings—2017 IEEE winter conference on applications of computer vision, WACV 2017, pp 924–933. https://doi.org/10.1109/WACV.2017.108

53.

Mukherjee K, Natesan S (2009) Parameter-uniform hybrid numerical scheme for time-dependent convection-dominated initial-boundary-value problems. Computing (Vienna/New York) 84:209–230. https://doi.org/10.1007/s00607-009-0030-2MathSciNetCrossRefMATH

54.

Cintra RJ, Duffner S, Garcia C, Leite A (2018) Low-complexity approximate convolutional neural networks. IEEE Trans Neural Netw Learn Syst 29:5981–5992. https://doi.org/10.1109/TNNLS.2018.2815435CrossRef

55.

Zeng M, Yu T, Wang X et al (2017) Semi-supervised convolutional neural networks for human activity recognition. In: Proceedings—2017 IEEE international conference on big data, Big Data 2017, 2018-Jan, pp 522–529. https://doi.org/10.1109/BigData.2017.8257967

56.

Kim J, Nocentini O, Scafuro M et al (2019) An innovative automated robotic system based on deep learning approach for recycling objects, pp 613–622. https://doi.org/10.5220/0007839906130622

57.

Gruber F, Grählert W, Wollmann P, Kaskel S (2019) Classification of black plastics waste using fluorescence imaging and machine learning. Recycling 4:1–17. https://doi.org/10.3390/recycling4040040CrossRef

58.

Sreelakshmi K, Akarsh S, Vinayakumar R, Soman KP (2019) Capsule neural networks and visualization for segregation of plastic and non-plastic wastes. In: 2019 5th international conference on advanced computing and communication systems (ICACCS) 2019, pp 631–636. https://doi.org/10.1109/ICACCS.2019.8728405

59.

Costa BS, Bernardes ACS, Pereira JVA et al (2019) Artificial intelligence in automated sorting in trash recycling, pp 198–205. https://doi.org/10.5753/eniac.2018.4416

60.

Dhulekar P, Gandhe ST, Mahajan UP (2018) Development of bottle recycling machine using machine learning algorithm. In: 2018 International conference on advances in communication and computing technology (ICACCT) 2018, pp 515–519. https://doi.org/10.1109/ICACCT.2018.8529483

61.

Nalepa J, Kawulok M (2019) Selecting training sets for support vector machines: a review. Artif Intell Rev 52:857–900. https://doi.org/10.1007/s10462-017-9611-1CrossRef

62.

Huang S, Nianguang CAI, Penzuti Pacheco P et al (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom Proteom 15:41–51. https://doi.org/10.21873/cgp.20063CrossRef

63.

Ghezelbash R, Maghsoudi A, Carranza EJM (2019) Performance evaluation of RBF- and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of S-A multifractal model and mineralization controls. Earth Sci Inform 12:277–293. https://doi.org/10.1007/s12145-018-00377-6CrossRef

64.

Mahvash Mohammadi N, Hezarkhani A (2018) Application of support vector machine for the separation of mineralised zones in the Takht-e-Gonbad porphyry deposit, SE Iran. J Afr Earth Sci 143:301–308. https://doi.org/10.1016/j.jafrearsci.2018.02.005CrossRef

65.

Khan A, Rinner B, Cavallaro A (2018) Cooperative robots to observe moving targets: review. IEEE Trans Cybern 48:187–198. https://doi.org/10.1109/TCYB.2016.2628161CrossRef

66.

Lundberg L, Lennerstad H, Boeva V, García-Martín E (2019) Handling non-linear relations in support vector machines through hyperplane folding. In: ACM 11th international conference on machine learning and computing series part F1481, pp 137–141. https://doi.org/10.1145/3318299.3318319

67.

Kowsari K, Meimandi KJ, Heidarysafa M et al (2019) Text classification algorithms: a survey. Information 10:1–68. https://doi.org/10.3390/info10040150CrossRef

68.

Gibbons C, Richards S, Valderas JM, Campbell J (2017) Supervised machine learning algorithms can classify open-text feedback of doctor performance with human-level accuracy. J Med Internet Res 19:1–12. https://doi.org/10.2196/jmir.6533CrossRef

69.

Auer M, Osswald K, Volz R, Woidasky J (2019) Artificial intelligence-based process for metal scrap sorting. arXiv preprint arXiv:1903.09415

70.

Ramalingam B, Lakshmanan AK, Ilyas M et al (2018) Cascaded machine-learning technique for debris classification in floor-cleaning robot application. Appl Sci 8:1–19. https://doi.org/10.3390/app8122649CrossRef

71.

Sakr GE, Mokbel M, Darwich A et al (2016) Comparing deep learning and support vector machines for autonomous waste sorting. In: 2016 International multidisciplinary conference on engineering technology (IMCET), pp 207–212. https://doi.org/10.1109/IMCET.2016.7777453

72.

Wang Z, Peng B, Huang Y, Sun G (2019) Classification for plastic bottles recycling based on image recognition. Waste Manag 88:170–181. https://doi.org/10.1016/j.wasman.2019.03.032CrossRef

73.

Magana-Mora A, Bajic VB (2017) OmniGA: optimized omnivariate decision trees for generalizable classification models. Sci Rep 7:1–11. https://doi.org/10.1038/s41598-017-04281-9CrossRef

74.

Liu S, Yang Z, Li Y, Wang S (2020) Decision tree-based sensitive information identification and encrypted transmission system. Entropy. https://doi.org/10.3390/e22020192MathSciNetCrossRef

75.

Xin Y, Kong L, Liu Z et al (2018) Machine learning and deep learning methods for cybersecurity. IEEE Access 6:35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950CrossRef

76.

Hehn TM, Hamprecht FA (2019) End-to-end learning of deterministic decision trees. In: Lecture notes on computer science (including Subser lect notes artif intell lect notes bioinformatics) 11269 LNCS, pp 612–627. https://doi.org/10.1007/978-3-030-12939-2_42

77.

Hehn TM, Kooij JFP, Hamprecht FA (2020) End-to-end learning of decision trees and forests. Int J Comput Vis 128:997–1011. https://doi.org/10.1007/s11263-019-01237-6MathSciNetCrossRef

78.

Yan-yan SONG, Ying LU (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27:130–135

79.

Akata Z, Perronnin F, Harchaoui Z, Schmid C (2016) Label-embedding for image classification. IEEE Trans Pattern Anal Mach Intell 38:1425–1438. https://doi.org/10.1109/TPAMI.2015.2487986CrossRef

80.

Zhang Z, Jung C (2020) GBDT-MO: Gradient-Boosted Decision Trees for Multiple Outputs. IEEE Transactions on Neural Networks and Learning Systems.

81.

Prasath VBS, Alfeilat HAA, Hassanat ABA et al (2017) Distance and similarity measures effect on the performance of k-nearest neighbor classifier—a review, pp 1–39. https://doi.org/10.1089/big.2018.0175

82.

Jiang Z, Huynh DQ (2018) Multiple pedestrian tracking from monocular videos in an interacting multiple model framework. IEEE Trans Image Process 27:1361–1375. https://doi.org/10.1109/TIP.2017.2779856MathSciNetCrossRefMATH

83.

Zhang S, Li X, Zong M et al (2018) Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans Neural Networks Learn Syst 29:1774–1785. https://doi.org/10.1109/TNNLS.2017.2673241MathSciNetCrossRef

84.

Hu LY, Huang MW, Ke SW, Tsai CF (2016) The distance function effect on k-nearest neighbor classification for medical datasets. Springerplus. https://doi.org/10.1186/s40064-016-2941-7CrossRef

85.

Nweke HF, Teh YW, Mujtaba G et al (2019) Multi-sensor fusion based on multiple classifier systems for human activity identification. Hum Centric Comput Inf Sci. https://doi.org/10.1186/s13673-019-0194-5CrossRef

86.

Dada EG, Bassi JS, Chiroma H et al (2019) Machine learning for email spam filtering: review, approaches and open research problems. Heliyon. https://doi.org/10.1016/j.heliyon.2019.e01802CrossRef

87.

Torres-García A, Rodea-Aragón O, Longoria-Gandara O et al (2015) Intelligent waste separator. Comput y Sist 19:487–500. https://doi.org/10.13053/CyS-19-3-2254CrossRef

88.

Zhang Q, Yu H, Barbiero M et al (2019) Artificial neural networks enabled by nanophotonics. Light Sci Appl. https://doi.org/10.1038/s41377-019-0151-0CrossRef

89.

Adamović VM, Antanasijević DZ, Ristić M et al (2018) An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level. J Mater Cycles Waste Manag 20:1736–1750. https://doi.org/10.1007/s10163-018-0741-6CrossRef

90.

Pfeiffer M, Pfeil T (2018) Deep learning with spiking neurons: opportunities and challenges. Front Neurosci. https://doi.org/10.3389/fnins.2018.00774CrossRef

91.

Zantalis F, Koulouras G, Karabetsos S, Kandris D (2019) A review of machine learning and IoT in smart transportation. Futur Internet 11:1–23. https://doi.org/10.3390/FI11040094CrossRef

92.

Gopal S (2016) Artificial neural networks in geospatial analysis. Int Encycl Geogr People Earth Environ Technol. https://doi.org/10.1002/9781118786352.wbieg0322CrossRef

93.

Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ (2019) 1D convolutional neural networks and applications: A survey. Mech Syst Sig Process 151:107398CrossRef

94.

Johnson JM, Khoshgoftaar TM (2019) Survey on deep learning with class imbalance. J Big Data. https://doi.org/10.1186/s40537-019-0192-5CrossRef

95.

Kalantary S, Jahani A, Pourbabaki R, Beigzadeh Z (2019) Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies. RSC Adv 9:24858–24874. https://doi.org/10.1039/c9ra04927dCrossRef

96.

Boutaba R, Salahuddin MA, Limam N et al (2018) Comprehensive survey machine learning. J Internet Serv Appl 9:99. https://doi.org/10.1186/s13174-018-0087-2CrossRef

97.

Deng F, He Y, Zhou S et al (2018) Compressive strength prediction of recycled concrete based on deep learning. Constr Build Mater 175:562–569. https://doi.org/10.1016/j.conbuildmat.2018.04.169CrossRef

98.

Sousa J, Rebelo A, Cardoso JS (2019) Automation of waste sorting with deep learning. In: Proceedings of 15th workshop on computer vision, WVC 2019, pp 43–48. https://doi.org/10.1109/WVC.2019.8876924

99.

Mwangi HW, Mokoena M (2019) Using deep learning to detect polyethylene terephthalate (PET) bottle status for recycling. Glob J Comput Sci Technol 19:27–31. https://doi.org/10.34257/gjcstgvol19is4pg27CrossRef

100.

Seredkin AV, Tokarev MP, Plohih IA et al (2019) Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1359/1/012127CrossRef

101.

Adedeji O, Wang Z (2019) Intelligent waste classification system using deep learning convolutional neural network. Proc Manuf 35:607–612. https://doi.org/10.1016/j.promfg.2019.05.086CrossRef

102.

Hulyalkar S, Deshpande R, Makode K (2018) Implementation of smartbin using convolutional neural networks. Int Res J Eng Technol 5:3352–3358

103.

Akinina N V., Akinin M V., Taganov AI, Nikiforov MB (2017) Methods of detection in satellite images of illegal dumps by using a method based on tree classifier. In: 2017 6th Mediterranean conference on embedded computing (MECO)—Incl ECYPS 2017, pp 6–8. https://doi.org/10.1109/MECO.2017.7977179

104.

Yang M, Thung G (2016) Classification of trash for recyclability status. In: CS229 project report 2016, pp 1–6

105.

Paulraj SG, Hait S, Thakur A (2016) Automated municipal solid waste sorting for recycling using a mobile manipulator. Proc ASME Des Eng Tech Conf. https://doi.org/10.1115/DETC2016-59842CrossRef

106.

Darling-Hammond L, Flook L, Cook-Harvey C et al (2020) Implications for educational practice of the science of learning and development. Appl Dev Sci 24:97–140. https://doi.org/10.1080/10888691.2018.1537791CrossRef

107.

Huntingford C, Jeffers ES, Bonsall MB et al (2019) Machine learning and artificial intelligence to aid climate change research and preparedness. Environ Res Lett 14:124007. https://doi.org/10.1088/1748-9326/ab4e55CrossRef

108.

Al-Salem SM, Lettieri P, Baeyens J (2009) Recycling and recovery routes of plastic solid waste (PSW): a review. Waste Manag 29:2625–2643. https://doi.org/10.1016/j.wasman.2009.06.004CrossRef

109.

Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS ONE 14:e0224365. https://doi.org/10.1371/journal.pone.0224365CrossRef

110.

Matheny ME, Whicher D, Israni ST (2020) Artificial intelligence in health care. JAMA 323:509. https://doi.org/10.1001/jama.2019.21579CrossRef

111.

Akamangwa N (2016) Working for the environment and against safety: how compliance affects health and safety on board ships. Saf Sci 87:131–143. https://doi.org/10.1016/j.ssci.2016.03.027CrossRef

112.

Wagner TP (2017) Reducing single-use plastic shopping bags in the USA. Waste Manag 70:3–12. https://doi.org/10.1016/j.wasman.2017.09.003CrossRef

113.

Rudin C, Radin J (2019) Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition. Harvard Data Sci Rev 1:1–9. https://doi.org/10.1162/99608f92.5a8a3a3dCrossRef

114.

Xu C, Jackson SA (2019) Machine learning and complex biological data. Genome Biol 20:1–4. https://doi.org/10.1186/s13059-019-1689-0CrossRef

115.

Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthc J 6:94–98. https://doi.org/10.7861/futurehosp.6-2-94CrossRef

116.

Vollmer S, Mateen BA, Bohner G et al (2020) Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. https://doi.org/10.1136/bmj.l6927CrossRef

117.

Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1:206–215. https://doi.org/10.1038/s42256-019-0048-xCrossRef

118.

Sakr S, Elshawi R, Ahmed AM et al (2017) Comparison of machine learning techniques to predict all-cause mortality using fitness data: The Henry Ford exercIse testing (FIT) project. BMC Med Inform Decis Mak 17:1–15. https://doi.org/10.1186/s12911-017-0566-6CrossRef

119.

Stanley KO, Clune J, Lehman J, Miikkulainen R (2019) Designing neural networks through neuroevolution. Nat Mach Intell 1:24–35. https://doi.org/10.1038/s42256-018-0006-zCrossRef

120.

Liu Y, Zhao T, Ju W et al (2017) Materials discovery and design using machine learning. J Mater 3:159–177. https://doi.org/10.1016/j.jmat.2017.08.002CrossRef

121.

Elton DC, Boukouvalas Z, Butrico MS et al (2018) Applying machine learning techniques to predict the properties of energetic materials. Sci Rep 8:1–12. https://doi.org/10.1038/s41598-018-27344-xCrossRef

122.

Walker S, Khan W, Katic K et al (2020) Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings. Energy Build 209:109705. https://doi.org/10.1016/j.enbuild.2019.109705CrossRef

123.

Wang Z, O’Boyle M (2018) Machine learning in compiler optimization. Proc IEEE 106:1879–1901. https://doi.org/10.1109/JPROC.2018.2817118CrossRef

124.

Kilimci ZH, Akyuz AO, Uysal M et al (2019) An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Complexity. https://doi.org/10.1155/2019/9067367CrossRef

125.

Zhang S, Zhang S, Wang B, Habetler TG (2020) Deep learning algorithms for bearing fault diagnosticsx—a comprehensive review. IEEE Access 8:29857–29881. https://doi.org/10.1109/ACCESS.2020.2972859CrossRef

126.

Sitawarin C, Wagner D (2019) On the robustness of deep K-nearest neighbors. In: Proceedings—2019 IEEE symposium on security and privacy workshops (SPW) 2019, pp 1–7. https://doi.org/10.1109/SPW.2019.00014

127.

Gandhi R (2018) R-CNN, fast R-CNN, faster R-CNN, YOLO—object detection algorithms. In: Towar. Data sci. https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e. Accessed 24 April 2020

128.

Nowakowski P, Pamuła T (2020) Application of deep learning object classifier to improve e-waste collection planning. Waste Manag 109:1–9. https://doi.org/10.1016/j.wasman.2020.04.041CrossRef

129.

Melinte DO, Travediu AM, Dumitriu DN (2020) Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification. Appl Sci 10(20):7301. https://doi.org/10.1007/s10163-021-01182-yCrossRef

130.

Liu L, Ouyang W, Wang X et al (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128:261–318. https://doi.org/10.1007/s11263-019-01247-4CrossRef

131.

Kadaous W (2016) What are the advantages and disadvantages of deep learning? Can you compare it with the statistical learning theory?—Quora. In: Quora. https://www.quora.com/What-are-the-advantages-and-disadvantages-of-deep-learning-Can-you-compare-it-with-the-statistical-learning-theory. Accessed 24 April 2020

132.

K D (2019) Top 4 advantages and disadvantages of support vector machine or SVM. In: Medium. https://medium.com/@dhiraj8899/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107. Accessed 24 April 2020

133.

Guo H, Wu S, Tian Y et al (2021) Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: a review. Bioresour Technol 319:124114. https://doi.org/10.1016/j.biortech.2020.124114CrossRef

134.

Jahnavi M (2017) Introduction to neural networks, advantages and applications. In: Mediu. Corp. https://towardsdatascience.com/introduction-to-neural-networks-advantages-and-applications-96851bd1a207. Accessed 24 April 2020

Titel: Applying machine learning approach in recycling
verfasst von: Merve Erkinay Ozdemir
Zaara Ali
Balakrishnan Subeshan
Eylem Asmatulu
Publikationsdatum: 17.02.2021
Verlag: Springer Japan
Erschienen in: Journal of Material Cycles and Waste Management / Ausgabe 3/2021
Print ISSN: 1438-4957
Elektronische ISSN: 1611-8227
DOI: https://doi.org/10.1007/s10163-021-01182-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 3/2021

Recycling of waste PP and crumb rubber together by use of self-healing ionomer as process compatibilizer

Geopolymer synthesized from spent fluid catalytic cracking catalyst and its heavy metal immobilization behavior

Elaboration of geopolymer binder from pharmaceutical glass waste and optimization of its synthesis parameters

A systems engineering study of integration reverse vending machines into the waste management system of Kazakhstan

Comparison of composting of chemically pretreated and fermented sugarcane bagasse for zero-waste biorefinery

Investigating mechanical properties and durability of concrete containing recycled rubber ash and fibers