The Novel Harmonic Soft Decision Algorithm for Suitable Institutions and Programme Selection in Higher Studies for Students

Abstract

Choosing a suitable college or university with respect to different parameters and uncertainty is quite tough. Traditional methods usually apply weightage, ranking, fuzzy membership function, or prediction methods that consume a lot of data. The present research paper tackles the problem with the development of a novel algorithm known as Harmonic Soft Decision Algorithm (HS-DA). The proposed methodological scheme of HS-DA creates a soft set at first, creates a soft univalent set next, introduces a harmonic univalent function, creates a soft harmonic univalent set, and calculates decision states in order to select the best alternative. A characteristic matrix is created using five students and five grade related parameters, and the Python code is written in order to show how the algorithm works. The decision state of each alternative is calculated by adding up the values of its binary parameter relationships, while the harmonic function gives function values. As per the above-mentioned experimental example, the decision states of the five available options are 1, 3, 2, 2, and 1. In other words, the decision state of the second option reaches the maximum decision state of 3, while the minimum decision state is 1. With respect to the harmonic scoring function, it yielded the following values: 1, 9, 7, 5, and 5, which shows how the evaluation of the characteristic vectors is made using this technique. The HS-DA presented here gives an analytical mathematical basis for the decision analysis using soft sets, with easy computation of the method. The primary importance of the algorithm is that it can be interpreted easily and the harmonic univalent functions can be incorporated in the decision-making process. It would be good to verify the results of the algorithm by using some practical decision-making data from higher education selections.