A few years ago, probability theory was a unique tool in hands of the experts dealing with situations of uncertainty appearing in problems of science and in everyday life. However, nowadays, with the development of fuzzy set theory—introduced by Zadeh in 1965—and the extension of fuzzy logic, the situation has changed. In fact, these new mathematical tools provided scientists with the opportunity to model under conditions that are vague or not precisely defined, thus succeeding in mathematically solving problems whose statements are expressed in our natural language. As a result, the spectrum of application has been rapidly extended, covering all of the physical sciences, economics and management, expert systems like financial planners, diagnostic, meteorological, information retrieval, control systems, etc., industry, robotics, decision making, programming, medicine, biology, humanities, education and almost all the other sectors of the human activity, including human reasoning itself. The first major commercial application of fuzzy logic was in cement kiln control (Zadeh, 1983), followed by a navigation system for automatic cars, a fuzzy controller for the automatic operation of trains, laboratory level controllers, controllers for robot vision, graphics, controllers for automated police sketchers and many others. It should be mentioned that fuzzy mathematics has been also significantly developed on the theoretical level, providing important insights even to branches of the classical mathematics, like algebra, analysis, geometry, etc.
The target of the present Special Issue of the Journal of Electrical and Electronic Engineering is to provide the experts in the field (academics, researchers, practitioners, etc.) the opportunity to present recent theoretical advances on fuzzy sets and fuzzy logic and of their extension/generalization (e.g. intuitionistic fuzzy logic, neutrosophic sets, etc.) and their applications to all fields of human activity.

Aims and Scope:

1. Fuzzy sets
2. Neutrosophic sets
3. Intuitionistic fuzzy sets
4. Fuzzy Graphs and Intuitionistic fuzzy Graphs
5. Fuzzy Decision Making Problems
6. Uncertainty in Fuzzy Environments