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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Segment-based CO2 emission evaluations from passenger cars based on deep learning techniques
Authors: Niroomand, Naghmeh
Bach, Christian
Elser, Miriam
et. al: No
DOI: 10.1109/ACCESS.2021.3135604
Published in: IEEE Access
Volume(Issue): 9
Page(s): 166314
Pages to: 166327
Issue Date: 2021
Publisher / Ed. Institution: IEEE
ISSN: 2169-3536
Language: English
Subjects: Automobile; CO2 emission; Classification algorithm; Clustering algorithm
Subject (DDC): 006: Special computer methods
363: Environmental and security problems
Abstract: The overall level of emissions from the Swiss passenger cars is strongly dependent on the fleet composition. Despite technology improvements, the Swiss passenger cars fleet remains emissions intensive. To analyze the root of this problem and evaluate potential solutions, this paper applies deep learning techniques to evaluate the inter-class (namely micro, small, middle, upper middle, large and luxury class) and intra-class (namely sport utility vehicle and non-sport utility vehicle) differences in carbon dioxide (CO2) emissions. This paper takes full use of novel semi-supervised fuzzy C-means (SSFCM), random forest and AdaBoost models as well as model fusion to successfully classify passenger vehicles and enable segment-based CO2 emission evaluations.
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Management and Law
Organisational Unit: Center for Economic Policy (FWP)
Appears in collections:Publikationen School of Management and Law

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