Detection of Microplastics Using Machine Learning

Chaczko, Z; Wajs-Chaczko, P; Tien, D; Haidar, Y

Detection of Microplastics Using Machine Learning

Chaczko, Z

Wajs-Chaczko, P Tien, D Haidar, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Machine Learning and Cybernetics, 2019, 2019-July
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.contributor.author	Wajs-Chaczko, P	en_US
dc.contributor.author	Tien, D	en_US
dc.contributor.author	Haidar, Y	en_US
dc.date.available	2021-08-12T19:00:34Z
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	Proceedings - International Conference on Machine Learning and Cybernetics, 2019, 2019-July	en_US
dc.identifier.isbn	9781728128160	en_US
dc.identifier.issn	2160-133X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137550
dc.description.abstract	© 2019 IEEE. Monitoring the presence of micro-plastics in human and animal habitats is fast becoming an important research theme due to a need to preserve healthy ecosystems. Microplastics pollute the environment and can represent a serious threat for biological organisms including the human body, as they can be inadvertently consumed through the food chain. To perceive and understand the level of microplastics pollution threats in the environment there is a need to design and develop reliable methodologies and tools that can detect and classify the different types of the microplastics. This paper presents results of our work related to exploration of methods and techniques useful for detecting suspicious objects in their respective ecosystem captured in hyperspectral images and then classifying these objects with the use of Neural Networks technique.	en_US
dc.relation.ispartof	Proceedings - International Conference on Machine Learning and Cybernetics	en_US
dc.relation.isbasedon	10.1109/ICMLC48188.2019.8949221	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Detection of Microplastics Using Machine Learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-July	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-July	en_US

Abstract:

© 2019 IEEE. Monitoring the presence of micro-plastics in human and animal habitats is fast becoming an important research theme due to a need to preserve healthy ecosystems. Microplastics pollute the environment and can represent a serious threat for biological organisms including the human body, as they can be inadvertently consumed through the food chain. To perceive and understand the level of microplastics pollution threats in the environment there is a need to design and develop reliable methodologies and tools that can detect and classify the different types of the microplastics. This paper presents results of our work related to exploration of methods and techniques useful for detecting suspicious objects in their respective ecosystem captured in hyperspectral images and then classifying these objects with the use of Neural Networks technique.

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http://hdl.handle.net/10453/137550