There is vagueness in pairwise comparisons, and this vagueness has been modeled using fuzzy concepts.
The fuzzy inference system uses fuzzy concepts where uncertainties are inherently unavoidable due to insufficient information and engineering assumption and exploits fuzzy rules in the multi-criteria evaluation by replicating heuristic knowledge and experience in logic-based descriptive rules.
In such situations, the need to use fuzzy concept occurs.
We implement a hybrid solution for situation comprehension and projection that combines data-driven approach, by using temporal extension of Fuzzy Formal Concept Analysis, and goal-driven approach, by using Fuzzy Cognitive Maps.
The concepts of fuzzy theory can be applied to fuzzy optimization problems and probabilistic models, also by using fuzzy theory many innovative methods can be developed further in this fascinating area of the variational inequality theory.
When fuzzy systems are designed by using fuzzy clustering, each cluster corresponds to a fuzzy rule.
Enhancing contrast by using fuzzy clustering allows to define the contours of the lesion correctly.
Mathematical and graphical models are presented using fuzzy set and fuzzy logic concepts.
Finally, we have demonstrated here the proof of concept of using fuzzy logic to select relevant biomarkers and to better evaluate risk.
