Cloud-Enabled K-Means Clustering for Advancing Wildlife Disease Surveillance and Outbreak Prediction
Chitra Sabapathy Ranganathan, Mothiram Rajasekaran, B Sakthisaravanan, N. Mohankumar, M. Rajmohan, S. Murugan · 2024
The preservation of wildlife and the protection of public health depend on accurate disease monitoring and the ability to predict outbreaks. Advanced computational approaches are necessary since traditional methods struggle to handle massive data sets and the need for real-time analysis. An improved approach for monitoring animal diseases using cloud computing and K-Means clustering is proposed in this research. The method takes use of the scalability of cloud computing to handle massive amounts of data on wildlife health gathered from many sensors. Clustering this data using the K-Means technique to see trends and outliers that might be signs of disease outbreaks. The identification and prediction of wildlife disease outbreaks was greatly enhanced using the cloud-enabled K-Means clustering technology. When compared to more traditional methods, the system's ability to identify epidemics and detect patterns in disease transmission was significantly better. Quicker reaction times, which are essential for timely interventions, were made possible by real-time analytic capabilities. To improve the precision of disease monitoring and outbreak prediction, this method also yields useful data for conservation efforts. The new system is a major step forward in the industry, offering improved and timely management of wildlife health.