A merged fuzzy system and neural network for improving management method and strategy in scientific research and education.

In recent years, integrating artificial intelligence into scientific management in education has transformed traditional methodologies, offering new avenues for personalized learning and efficient resource allocation. This paper proposes a method that combines neural network classification and fuzzy systems to scientific management in the education field, paving the way for innovative solutions tailored to current challenges. Besides, improved activation functions and a novel management method were implemented in the education field, utilizing classification learners of neural network structures. By leveraging data-driven tools, the proposed method enhances decision-making processes, improves student outcomes, and optimizes educational resources based on the simulation data. The implementation demonstrates significant improvements in student performance and administrative efficiency. For instance, early identification of at-risk researchers is conducted, and a 15% increase in assessment pass rates is observed in the subsequent academic year.