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  4. Vol 8, Issue 5, 2024

Research on Reducing the Number of Illegal Animal Trade Based on Machine Learning and Comprehensive Assessment Modeling

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DOI: 10.23977/acss.2024.080516 | Downloads: 20 | Views: 942

Author(s)

Siyi Huang 1

Affiliation(s)

1 School of Mathematics and Statistics, Jishou University, Jishou, China

Corresponding Author

Siyi Huang

ABSTRACT

This paper explores strategies to reduce illegal animal trade by leveraging machine learning and comprehensive assessment modeling. Using data from the World Bank and the World Justice Project, the study focuses on the financial capacity, human resources, legal integrity, and interest in wildlife conservation of the top ten GDP countries. Employing the TOPSIS evaluation model with entropy weight method, the United States is identified as the most influential nation in combating wildlife trade. Further analysis through Pearson correlation and DEA confirms the negative impact of illegal trade on economic and social indicators. LSTM neural network projections predict a significant reduction in illegal wildlife trade with the implementation of targeted programs, highlighting the potential of strategic interventions in protecting ecosystems and promoting sustainable development.

KEYWORDS

Illegal Wildlife Trade, Machine Learning, TOPSIS, LSTM

CITE THIS PAPER

Siyi Huang, Research on Reducing the Number of Illegal Animal Trade Based on Machine Learning and Comprehensive Assessment Modeling. Advances in Computer, Signals and Systems (2024) Vol. 8: 134-144. DOI: http://dx.doi.org/10.23977/acss.2024.080516.

REFERENCES

[1] Sas-Rolfes M, Challender D W S, Hinsley A, et al. Illegal wildlife trade: Scale, processes, and governance[J]. Annual Review of Environment and Resources, 2019, 44(1): 201-228.
[2] Duffy R. The illegal wildlife trade in global perspective [M]//Handbook of transnational environmental crime. Edward Elgar Publishing, 2016: 109-128.
[3] Shepherd C R, Compton J, Warne S. Transport infrastructure and wildlife trade conduits in the GMS: regulating illegal and unsustainable wildlife trade[J]. Biodiversity Conservation Corridors Initiative, 2007: 27-28. 
[4] Ren Z. Evaluation method of port enterprise product quality based on entropy weight TOPSIS[J]. Journal of Coastal Research, 2020, 103(SI): 766-769.
[5] Mansson R, Tsapogas P, Akerlund M, et al. Pearson Correlation Analysis of Microarray Data Allows for the Identification of Genetic Targets for Early B-cell Factor*[boxs][J]. Journal of Biological Chemistry, 2004, 279(17): 17905-17913.
[6] Cook W D, Seiford L M. Data envelopment analysis (DEA)–Thirty years on[J]. European journal of operational research, 2009, 192(1): 1-17.
[7] Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270.

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