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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Swine flu Detection and Location using Machine Learning Techniques and GIS

Author 1: P. Nagaraj
Author 2: A. V. Krishna Prasad
Author 3: V. B. Narsimha
Author 4: B. Sujatha

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.01309115

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

  • Abstract and Keywords
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Abstract: The H1N1 virus, more commonly referred to as swine flu, is an illness that is extremely infectious and can in some cases be fatal. Because of this, the lives of many individuals have been taken. The disease can be transmitted from pigs to people. This research presents an artificial neural network (ANN) classifier for disease forecasting, as well as a technique for detecting people who are sick based on the geographic region in which they are found. The source codes for these two algorithms are provided below. These coordinates serve as the foundation for the GIS coordinates that are utilized in the method for assessing the extent to which the illness has spread. The ICMR and NCDC datasets were utilized in the study. They used Dynamic Boundary Location algorithm to detect swine flu affected person’s location, the researchers discovered that the accuracy of the proposed classifier was 96 standard classifiers.

Keywords: Swine Flu; influenza; machine learning; GIS; classifiers; ANN; virus; algorithm

P. Nagaraj, A. V. Krishna Prasad, V. B. Narsimha and B. Sujatha, “Swine flu Detection and Location using Machine Learning Techniques and GIS” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01309115

@article{Nagaraj2022,
title = {Swine flu Detection and Location using Machine Learning Techniques and GIS},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01309115},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01309115},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {P. Nagaraj and A. V. Krishna Prasad and V. B. Narsimha and B. Sujatha}
}


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