Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018)

Growth Enterprises Identification with Artificial Intelligence

Authors
Liang Wang
Corresponding Author
Liang Wang
Available Online October 2018.
DOI
10.2991/icmcs-18.2018.37How to use a DOI?
Keywords
Artificial intelligence; Logistics industry; Growth potential; Random forest
Abstract

The rise of big data and artificial intelligence has revolutionized many industries, including logistics. From the long-term development of the enterprise, growth plays an important role. Based on the 222 observations from logistics enterprises, two kinds of strategies are adopted and machine learning algorithm models such as artificial neural network, support vector machine and random forest are employed. To sum up, on one hand, it is feasible to establish growth index by reducing dimension with principal component analysis then classify on growth financial indicators. On the other hand, by comparison, the random forest algorithm model can identify the growth state of the enterprise in accuracy.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018)
Series
Advances in Computer Science Research
Publication Date
October 2018
ISBN
10.2991/icmcs-18.2018.37
ISSN
2352-538X
DOI
10.2991/icmcs-18.2018.37How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Liang Wang
PY  - 2018/10
DA  - 2018/10
TI  - Growth Enterprises Identification with Artificial Intelligence
BT  - Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018)
PB  - Atlantis Press
SP  - 185
EP  - 189
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmcs-18.2018.37
DO  - 10.2991/icmcs-18.2018.37
ID  - Wang2018/10
ER  -