It goes without saying that a challenging quest for the construction of intelligent systems is realized through the development of hybrid information technologies and their vigorous and prudent exploitation. In a nutshell, what has emerged under the name of computational intelligence (CI) or soft computing is a well-orchestrated, highly synergistic consortium of technologies of neural networks,...

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts pay less attention n-ary (n≥2) containing more than two entities, which prevalent in the real world. Moreover, previous CQA methods can only make predictions a few given types of cannot be flexibly exten...

Fuzzy Set Theory and Rough Set Theory are the most popular mathematical tools for dealing with uncertainties. During past decades, these set theories are being applied successfully in several areas for solving many complex tasks. This paper is concerned with the application of hybrid Fuzzy-Rough set based approach for feature subset selection. Keywords— Fuzzy set theory, Rough Set theory, Fuzzy...

Since the early 1990s, many authors have studied fuzzy rough set models and their application in machine learning and data reduction. In this work, we adjust the β-precision and the ordered weighted average based fuzzy rough set models in such a way that the number of theoretical properties increases. Furthermore, we evaluate the robustness of the new models a-β-PREC and a-OWA to noisy data and...

Fuzzy logics are designed to support logical inferences on vague or uncertain premises, and they are useful in several theoretical and applicative areas of computer science. A central paradigm in mathematical fuzzy logic, popularized by Hájek [Háj98], is based on the idea of weakening Boolean logic starting from a suitable generalization of Boolean conjunction, namely, a class of [0, 1]-valued ...

The paper presents a transition from the crisp rough set theory to a fuzzy one, called Alpha Rough Set Theory or, in short, a-RST. All basic concepts or rough set theory are extended, i.e., information system, indiscernibility, dependency, reduction, core, de®nability, approximations and boundary. The resulted theory takes into account fuzzy data and allows the approximation of fuzzy concepts. ...

Classification based on fuzzy logic techniques can handle uncertainty to a certain extent as it provides only the fuzzy membership of an element in a set. This paper implements the extension of fuzzy logic: Neutrosophic logic to handle indeterminacy, uncertainty effectively. Classification is done on various techniques based on Neutrosophic logic i.e. Neutrosophic soft set, rough Neutrosophic s...

Fuzzy rule interpolation is an important technique for performing inferences with sparse rule bases. Even when given observations have no overlap with the antecedent values of any rule, fuzzy rule interpolation may still derive a conclusion. Nevertheless, fuzzy rule interpolation can only handle fuzziness but not roughness. Rough set theory is a useful tool to deal with incomplete knowledge, wh...

We devise a condition strictly between the existence of an $n$-ary and $n{+}1$-ary near-unanimity term. evaluate exactly distributivity modularity levels implied by such condition.