Please use this identifier to cite or link to this item:
Title: Machine learning approach for carrier surface design in carrier-based dry powder inhalation
Authors: Farizhandi, Amir Abbas Kazemzadeh
Alishiri, Mahsa
Lau, Raymond
Keywords: Engineering::Bioengineering
Issue Date: 2021
Source: Farizhandi, 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-.
Journal: Computers and Chemical Engineering
Abstract: In 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.
ISSN: 0098-1354
DOI: 10.1016/j.compchemeng.2021.107367
Schools: School of Chemical and Biomedical Engineering 
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCBE Journal Articles

Citations 50

Updated on Nov 30, 2023

Web of ScienceTM
Citations 20

Updated on Oct 31, 2023

Page view(s)

Updated on Dec 3, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.