Scholarship selection reinvented: a fuzzy inference approach to assessing overseas study candidates

Scholarship selection is a critical process that determines access to higher education opportunities, yet conventional methods often suffer from subjectivity, rigidity, and bias. This project introduces a fuzzy inference system (FIS) as an innovative approach to assessing overseas scholarship candidates. The system integrates four key criteria CGPA, parents’ income, co-curricular activeness, and number of siblings using trapezoidal membership functions and a rule base of 81 fuzzy rules. By applying MATLAB’s Fuzzy Logic Toolbox, the model transforms crisp input values into fuzzy sets, evaluates them through inference rules, and defuzzifies the results into clear eligibility outcomes. This method captures uncertainties in candidate profiles and provides nuanced decisions beyond binary acceptance or rejection. The novelty lies in its fairness, transparency, and flexibility in handling borderline cases, offering a scalable framework adaptable to diverse scholarship schemes. Ultimately, the system ensures more equitable opportunities for students pursuing overseas education.