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.

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}
}