The real world decision-making often involves a comparison of uncertain systems, or alternatives based on fuzzy evaluations. The concept of fuzzy entropy is quite useful in such situations. This paper critically examines the existing fuzzy entropy functions, and redefine them to add to their usefulness as measures of the fuzzy uncertainty in decision-making applications. More specifically, new variants of the extant Luca & Termini, and Pal & Pal fuzzy entropy functions are proposed. The proposed fuzzy entropy functions are extended for the probabilistic-fuzzy uncertainty, commonly observed in the real world. A real application is included to demonstrate the usefulness of the proposed entropy functions in decision-making applications.