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Applied machine learning: A creation of sustainable development progress index based on circular economy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Global achievements in economic growth, poverty reduction and improved welfare have been counterbalanced by an increasing strain on the biosphere in the last decade. With technology improvement, 'big data' will become the dominant scientific paradigm increasing predictive strengthen and visibility. This study focuses on evaluating the relationship among five identified capital resources using unsupervised machine learning clustering analysis to construct pattern recognition then performed forest-based classification and regression with data collected from 32 OECD nations to create a Sustainable Development Progress Index (SDPI). The proposed SDPI ratings provides a clear outlook in ways for countries to balance resource demand and economic development.

Original languageEnglish
Title of host publicationProceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1263-1268
Number of pages6
ISBN (Electronic)9781728155845
DOIs
StatePublished - Dec 2019
Externally publishedYes
Event6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019 - Las Vegas, United States
Duration: Dec 5 2019Dec 7 2019

Publication series

NameProceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019

Conference

Conference6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019
Country/TerritoryUnited States
CityLas Vegas
Period12/5/1912/7/19

Keywords

  • Clustering analysis
  • Computational complexity
  • Principle component analysis (PCA)
  • Sustainable development
  • Unsupervised machine learning

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