Due to apparent flexibility of Intuitionistic Fuzzy Set (IFS) concepts in dealing with the imprecision or uncertainty, these are proving to be quite useful in many application areas for a more human consistent reasoning under imperfectly defined facts and imprecise knowledge. In this paper, we apply notions of entropy and intuitionistic fuzzy sets to present a new fuzzy decision-making approach called intuitionistic fuzzy entropy measure for selection and ranking the suppliers with respect to the attributes. An entropy-based model is formulated and applied to a real case study aiming to examine the rankings of suppliers. Furthermore, the weights for each alternative, with respect to the criteria, are calculated using intuitionistic fuzzy entropy measure. The supplier with the highest weight is selected as the best alternative. This proposed model helps the decision-makers in better understanding of the weight of each criterion without relying on the mere expertise.

This paper presents a system that can recognize handwritten words expressed using broken letters of the Persian alphabet. The proposed system can be used for most activities related to the gathering of public information. Statistical features of the separated/broken letters are employed in the system. Each letter is recognized using interconnected fuzzy neural network. The advantages of this method include high precision owing to the strength of the neural network algorithm and the possibility of extending dataset instance codes in a simple manner. At last, an evaluation for the proposed method is provided experimentally.

We propose a novel framework which is aimed to process gray images. By appropriately converting digital colors to intuitionistic fuzzy numbers in a generalized framework, the algorithm tends to remove noise impairments while processing the damaged images. A new entropy measure on intuitionistic fuzzy sets is introduced for determining the uncertainty of each pixel and consequently, to recognize the best processed image which has the least noise. We conclude by discussing two illustrative examples for verifying the developed approach.

Prediction of standard molar chemical exergy value for organic compounds is investigated using a quantitative structure-property relationship (QSPR) model combined with adaptive neuro-fuzzy inference system (ANFIS) strategy. Particle swarm optimization (PSO) method is also implemented to determine the optimal ANFIS structure. The proposed model uses three constitutional descriptors in model development’s procedure. The QSPR-ANFIS model represents a great performance in prediction of the standard molar chemical exergy values with 0.9999, 44548, and 1.49 values for R², RMSE, and %AARD, respectively.