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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(2): 155-159 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.2.591

Comparison of West Nile Virus and Yellow Fever Virus Using Apriori Algorithm, Decision Tree, and Support Vector Machine(SVM)

Seunghwan Lee, Changyoon Lee, Donghee Kim, and Taeseon Yoon
Abstract—West Nile virus (WNV), initially being identified in 1937, has largely spread throughout the world in the 20th century. Posing a number of serious threats to both human and animals, WNV even became the cause of the neuro-invasive diseases. The development of the vaccine against WNV has increased in importance nowadays as it is now considered to be an endemic pathogen in various regions. In this study, we compared the nucleotide sequence of West Nile Virus and Yellow Fever virus. As the complete vaccine against Yellow Fever virus (YFV) was currently developed, comparing the sequence of both viruses would provide the essential key of developing vaccine against WNV. Decision Tree, Apriori algorithm, and Support Vector Machine were used to reanalyze the whole sequence of WNV and YFV, representing some distinguishable features of both viruses.

Index Terms—West nile virus, yellow fever virus, Apriori, decision tree, support vector machine.

Seunghwan Lee, Changyoon Lee, and Donghee Kim are with Hankuk Academy of Foreign Studies, Yonginsi, Republic of Korea (e-mail: glenlee3407@hafs.hs.kr, changyoon16@hafs.kr, 1021dh@naver.com).
Taeseon Yoon was with Korea University, Seoul, Republic of Korea. He is now with Hankuk Academy of Foreign Studies, Yonginsi, Republic of Korea (email: tsyoon@hafs.hs.kr).

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Cite: Seunghwan Lee, Changyoon Lee, Donghee Kim, and Taeseon Yoon, "Comparison of West Nile Virus and Yellow Fever Virus Using Apriori Algorithm, Decision Tree, and Support Vector Machine(SVM)," International Journal of Machine Learning and Computing vol.6, no. 2, pp. 155-159, 2016.

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