Exploring the Potential of Hybrid Feature-Based Research Topic Generation for Government Science and Technology Projects

This research addresses the challenge of identifying relevant research topics for Government Science and Technology (GRB) projects. Traditional methods for identifying research topics often prove inadequate due to the increasing volume and complexity of available data. This study proposes a novel framework that integrates text mining, K-means clustering, and AI optimization techniques to effectively identify and prioritize research topics. By analyzing existing research literature, identifying emerging trends, and considering the specific needs and priorities of GRB, the framework aims to generate a set of impactful and relevant research topics. The proposed framework has the potential to be adapted to other domains, such as healthcare and finance, facilitating the identification of promising research directions in various fields.