A Comprehensive Machine Learning Framework for Analyzing Public Perception of Border Biosecurity under the background of big data

Border biosecurity focuses on the procedures employed at borders to avoid the instigation and spread of harmful organisms such as diseases, pests, and invasive species. It may threaten human being, animal, plant health, and environment. With the advent of the big data era, biosecurity in border areas has received increasing attention. This article deeply explores the many challenges facing biosecurity in border areas under the background of big data, including data security and privacy protection issues, early identification and early warning of biosecurity threats, and barriers to biosecurity information sharing and collaborative cooperation. To develop effective policy and awareness programs, the study of public perception about border biosecurity is important. This paper presents a machine learning framework to investigate survey data collected on public attitudes, awareness, and concerns related to biosafety in border regions. This work makes use of both supervised and unsupervised learning algorithms to identify patterns and classify public attitudes. Logistic regression is used to forecast whether the person has the willingness to participate in biosafety training. Clustering algorithm is used to group the respondents based on their biosafety awareness and viewpoint on technological and international cooperation. These approaches are very useful for the government, researchers, and public health organizations for building strategies for border biosecurity.