Epidemics Fuzzy Decision-Making Applications and Fuzzy Genetic Algorithms Efficiency Enhancement.

Fuzzy logic is an innovative scientific field with several successful applications. Genetic algorithms and fuzzy logic systems fusion provide real-world problems modeling through the development of intelligent and adaptive systems. Moreover, the statistical analysis of the epidemiology of infectious diseases, which combines fuzzy logic aspects, is vital for perceiving their evolution and control potential. Author's objective is initially to provide a review of the efficiency of fuzzy logic applications. The advanced implementation of fuzzy logic theory in epidemiology and the application of fuzzy logic for controlling genetic algorithms within strategies based on the human experience and knowledge known as fuzzy logic controllers (FLCs) are analyzed. Outcomes of this review study show that not only can fuzzy sets be efficiently implemented in epidemiology but also prove the effectiveness of fuzzy genetic algorithms applications, thus suggesting that fuzzy logic applications are a really promising field of research.

Neuro-Fuzzy Networks and Their Applications in Medical Fields.

The fusion of artificial neural networks and fuzzy logic systems allows researchers to model real-world problems through the development of intelligent and adaptive systems. Artificial neural networks are able to adapt and learn by adjusting the interconnections between layers, while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as "neuro-fuzzy" systems. In this paper, the basic elements of both approaches are analyzed, noticing that this blending could be applied for pattern recognition in medical applications.

Fuzzy logic systems and medical applications.

The combination of Artificial Neural Networks and Fuzzy Logic Systems enables the representation of real-world problems via the creation of intelligent and adaptive systems. By adapting the interconnections between layers, Artificial Neural networks are able to learn. A computing framework based on the concept of fuzzy set and rules as well as fuzzy reasoning is offered by fuzzy logic inference systems. The fusion of the aforementioned adaptive structures is called a "Neuro-Fuzzy" system. In this paper, the main elements of said structures are examined. Researchers have noticed that this fusion could be applied for pattern recognition in medical applications.