This paper suggests an effective method for facial recognition using fuzzy theory and Shannon entropy. Combination of fuzzy theory and Shannon entropy eliminates the complication of other methods. Shannon entropy calculates the ratio of an element between faces, and fuzzy theory calculates the membership of the entropy with 1. More details will be mentioned in Section 3. The learning performanc...

The main purposes of this paper are to probe for the entropy differences between arithmetically manipulated Triangular Fuzzy Numbers (TFNs) using Shannon’s Function; and to study the relationships between any two TFNs. Simultaneously, we expand on the articles of Wang et al. [6] and Wang et al. [5]. The entropy differences between two TFNs subjected to different arithmetic operations are classi...

The concept of entropy is one the most important notions information theory. Entropy quantifies amount uncertainty involved in value a random variable or outcome process. Shannon’s useful types. notion enthalpy energy expressed by complement entropy. In this paper, we propose interval-valued hybrid fuzzy set modifying single and multisets. context, an contains about both data their We also prov...

Abstract In the case of conflicting individuals or evaluator groups, finding common preferences participants is a challenging task. This statement also refers to Intuitionistic Fuzzy Analytic Hierarchy Process models, in which uncertainty scoring well-handled, however, aggregation modified scores generally conducted by conventional way multi-criteria decision-making. paper offers two options fo...

This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of t...

The well-known generalisation of hard Cmeans (HCM) clustering is fuzzy C-means (FCM) clustering where a weight exponent on each fuzzy membership is introduced as the degree of fuzziness. An alternative generalisation of HCM clustering is proposed in this paper. This is called fuzzy entropy (FE) clustering where a weight factor of the fuzzy entropy function is introduced as the degree of fuzzy e...

To deal with situations involving uncertainty, Fermatean fuzzy sets are more effective than Pythagorean sets, intuitionistic and sets. Applications for similarity measures can be found in a wide range of fields, including clustering analysis, classification issues, medical diagnosis, etc. The computation the weights criteria multi-criteria decision-making problem heavily relies on entropy measu...

A sum of real numbers equals the mutual entropy of a fuzzy set and its complement set. A " fuzzy " or multivalued set is a point in a unit hypercube. Fuzzy mutual (Kullback) entropy arises from the logarithm of a unique measure of fuzziness. The proof uses the logistic map, a diffeomorphism that maps extended real space onto the fuzzy cube embedded in it. The logistic map equates the sum of a v...

Abstract: The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) has been used to propose a new method for filtering time series originating from nonlinear systems. The filtering method is based on fuzzy entropy and a new waveform. A new waveform is defined wherein Intrinsic Mode Functions (IMFs)—which are obtained by CEEMDAN algorithm—are firstly sorted in ascending o...

The entropies of structure, information and the effectiveness entropy between knowledge and organization structure are main entropy sources of supply chain network. Entropy model of fractal supply chain network organization structure is established. Moreover, the basic principle and process of fuzzy AHP are introduced. Fractal knowledge management network outside independently organization stru...