Multiaspect fuzzy soft sets (MAFSS) is a generalization of fuzzy soft set and multiaspect soft set. It describes objects based on parameters which are associated with different sets of elements characterized by different types of aspect-related universal sets. The parameter-element relationships in a MAFSS are in the form of fuzzy sets. Distance and entropy for fuzzy sets are two important measures that determine the degree of dissimilarity between fuzzy sets and the degree of fuzziness of fuzzy sets, respectively. As MAFSSs are composed of fuzzy sets, the aforementioned measures can be extended to MAFSS environment. In this paper, we present the axiomatic definitions of distance and distance-based entropy for MAFSSs. A general formula for estimating the entropy based on normalized distances is established. Some normalized distance-based entropy measures are derived and their properties are investigated.
Type-2 fuzzy sets allow us to incorporate the uncertainties about the membership functions into fuzzy sets, thereby overcoming a problem that is inherent in type-1 fuzzy sets, which does not allow for any uncertainty in assigning values to the membership functions. Complex fuzzy sets are type-1 fuzzy sets with complex-valued grades of membership and are characterized by an additional phase term which enables it to better represent and capture the time-periodic and seasonal aspects of fuzziness that are prevalent in many real world problems and time-series applications. However, similar to type-1 fuzzy sets, the membership functions of complex fuzzy sets are difficult to enumerate, as they are subject to individual preferences and bias. To overcome this problem, we propose the concept of interval-valued complex fuzzy soft sets which combines complex fuzzy sets with type-2 fuzzy sets and soft sets. This adaption of complex fuzzy sets assigns an interval-based membership to each element and adequate parameterization, which betters corresponds to the intuition of representing fuzzy data. Subsequently this paper is concerned with the concepts related to this model, verifying the algebraic properties and demonstrating the utility of this model.
Complex fuzzy sets and its accompanying theory although at its infancy, has proven to be superior to classical type-1 fuzzy sets, due its ability in representing time-periodic problem parameters and capturing the seasonality of the fuzziness that exists in the elements of a set. These are important characteristics that are pervasive in most real world problems. However, there are two major problems that are inherent in complex fuzzy sets: it lacks a sufficient parameterization tool and it does not have a mechanism to validate the values assigned to the membership functions of the elements in a set. To overcome these problems, we propose the notion of complex fuzzy soft expert sets which is a hybrid model of complex fuzzy sets and soft expert sets. This model incorporates the advantages of complex fuzzy sets and soft sets, besides having the added advantage of allowing the users to know the opinion of all the experts in a single model without the need for any additional cumbersome operations. As such, this model effectively improves the accuracy of representation of problem parameters that are periodic in nature, besides having a higher level of computational efficiency compared to similar models in literature.
An effectiveness of a game based learning (GBL) can be determined from an application of fuzzy set conjoint analysis. The analysis was used due to the fuzziness in determining individual perceptions. This study involved a survey collected from 36 students aged 16 years old of SMK Mersing, Johor who participated in a Mathematics Discovery Camp organized by UKM research group called PRISMatik. The aim of this research was to determine the effectiveness of the module delivered to cultivate interest in mathematics subject in the form of game based learning through different values. There were 11 games conducted for the participants and students' perceptions based on the evaluation of six criteria were measured. A seven-point Likert scale method was used to collect students' preferences and perceptions. This scale represented seven linguistic terms to indicate their perceptions on each module of GBLs. Score of perceptions were transformed into degree of similarity using fuzzy set conjoint analysis. It was found that Geometric Analysis Recreation (GEAR) module was able to increase participant preference corresponded to the six attributes generated. The computations were also made for the other 10 games conducted during the camp. Results found that interest, passion and team work were the strongest values obtained from GBL activities in this camp as participants stated very strongly agreed that these attributes fulfilled their preferences in every module. This was an indicator of efficiency for the program. The evaluation using fuzzy conjoint analysis implicated the successfulness of a fuzzy approach to evaluate students' perceptions toward GBL.