Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159809
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dc.contributor.authorFarizhandi, Amir Abbas Kazemzadehen_US
dc.contributor.authorAlishiri, Mahsaen_US
dc.contributor.authorLau, Raymonden_US
dc.date.accessioned2022-07-04T02:06:19Z-
dc.date.available2022-07-04T02:06:19Z-
dc.date.issued2021-
dc.identifier.citationFarizhandi, A. A. K., Alishiri, M. & Lau, R. (2021). Machine learning approach for carrier surface design in carrier-based dry powder inhalation. Computers and Chemical Engineering, 151, 107367-. https://dx.doi.org/10.1016/j.compchemeng.2021.107367en_US
dc.identifier.issn0098-1354en_US
dc.identifier.urihttps://hdl.handle.net/10356/159809-
dc.description.abstractIn this study, a machine learning approach was applied to evaluate the impact of operating and design variables on dry powder inhalation efficiency. Emitted dose and fine particle fraction data were extracted from the literature for a variety of drug and carrier combinations. Carrier surface properties are obtained by image analysis of SEM images reported. Models combining artificial neural network and genetic algorithm were developed to determine the emitted dose and fine particle fraction. Design strategies for the carrier surface were also proposed to enhance the fine particle fractions.en_US
dc.language.isoenen_US
dc.relation.ispartofComputers and Chemical Engineeringen_US
dc.rights© 2021 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Bioengineeringen_US
dc.titleMachine learning approach for carrier surface design in carrier-based dry powder inhalationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Chemical and Biomedical Engineeringen_US
dc.identifier.doi10.1016/j.compchemeng.2021.107367-
dc.identifier.scopus2-s2.0-85106614670-
dc.identifier.volume151en_US
dc.identifier.spage107367en_US
dc.subject.keywordsArtificial Neural Networken_US
dc.subject.keywordsDry Powder Inhalationen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
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