The concept of soft set is one of the recent topics developed for dealing with the uncertainties present in most of our real life situations. The parametrization tool of soft set theory enhance the flexibility of its applications. In this paper, we define intuitionistic fuzzy soft matrices and their operations which are more functional to make theoretical studies in the intuitionistic fuzzy sof...

Abstract This paper attempts to forward both soft topology and fuzzy with a pioneering analysis of their mutual relationships. With each we associate parameterized family topologies called its t -pushes. And defines -throwbacks. Different produce different But prove by example that not all are characterized The import these constructions is some properties stated in one setting can be investiga...

In this paper, interactions among fuzzy, rough, and soft set theory has been studied. The authors have examined these theories as a problem solving tool in association rule mining problems of data mining and knowledge discovery in databases. Although fuzzy and rough set have been well studied areas and successfully applied in association rule mining problem, but soft set theory needs more atten...

Soft computing is a part of an artificial intelligence, and fuzzy logic is the study of fuzziness on data. The combination of these two techniques can provide an intelligent system with more ability and flexibility. The nature of data in the stock/capital market is more complex and challenging to predict the movement of the price of the stock. The study has combined both fuzzy c-means and neura...

Soft Computing techniques play an important role for decision in applications with imprecise and uncertain knowledge. The application of soft computing disciplines is rapidly emerging for the diagnosis and prognosis in medical applications. Between various soft computing techniques, fuzzy expert system takes advantage of fuzzy set theory to provide computing with uncertain words. In a fuzzy exp...

Interval-valued intuitionistic fuzzy sets (IVIFSs) are widely used to handle uncertainty and imprecision in decision making. However, in more complicated environment, it is difficult to express the uncertain information by an IVIFS with considering the decision-making preference. Hence, this paper proposes a group generalized interval-valued intuitionistic fuzzy soft set (G-GIVIFSS) which conta...

Abstract The goal of this paper, is to introduce another classes the fuzzy soft bounded linear operator in Hilbert space which a quasi normal operator, as well as, give some properties about concept with investigating relationship among types on other kinds operators.

Clustering is a standard approach in analysis of data and construction of separated similar groups. The most widely used robust soft clustering methods are fuzzy, rough and rough fuzzy clustering. The prominent feature of soft clustering leads to combine the rough and fuzzy sets. The Rough Fuzzy C-Means (RFCM) includes the lower and boundary estimation of rough sets, and fuzzy membership of fuz...

fuzzy rule-based classification systems (frbcs) are highly investigated by researchers due to their noise-stability and  interpretability. unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. most of the pro...

in this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. utilizing the generalized characterization theorem. then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. here neural network is considered as a part of large eld called n...