New Entropy-Based Similarity Measure between Interval-Valued Intuitionstic Fuzzy Sets

In this paper we propose a new approach to construct similarity measures using the entropy measure for Interval-Valued Intuitionistic Fuzzy Sets. In addition, we provide several illustrative examples to demonstrate the practicality and effectiveness of the proposed formula. Finally, we use the new proposed similarity measure to develop new approach for solving problems of pattern recognition and multi-criteria fuzzy decision making

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

New Distance Measure of Single-Valued Neutrosophic Sets and Its Application

A single-valued neutrosophic set (SVNS) is an instance of a neutrosophic set, which can be used to handle uncertainty, imprecise, indeterminate, and inconsistent information in real life. In this paper, a new distance measure between two SVNSs is defined by the full consideration of truthmembership function, indeterminacy-membership function, and falsity-membership function for the forward and backward differences

Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment

As enterprises pay more and more attention to environmental issues, the green supply chain management (GSCM) mode has been extensively utilized to guarantee profit and sustainable development. Greensupplierselection(GSS),whichisakeysegmentofGSCM,hasbeeninvestigated to put forward plenty of GSS approaches

A Robust Intuitionistic Fuzzy Constraint Score based Potential Feature Subset Selection for Chronic Diseases Detection

This work proposes a novel feature selection algorithm for high-dimensional features in real-time datasets for prediction or classification. Conventional methods assume dataset values as crisp formats, but in real datasets, instances are represented in linguistic formats, requiring the use of uncertainty theories. The Intuitionistic Fuzzy Similarity based constraint score is proposed, where each feature is denoted as an independent variable and the class variable as a dependent variable. The features are represented in triplet form, with grade of belongingness, non-belongingness, and hesitancy index to maximize relevancy and reduce redundancy. Pairwise similarity matching is computed using Intuitionistic fuzzy similarity distance measure for supervised learning and intuitionistic fuzzy K-NN for semi-supervised learning. Potential feature subsets are selected and validated using deep learning algorithms. The results show that the proposed Intuitionistic fuzzy Constraint score feature selection algorithm produces optimal results compared to other state-of-the-art methods in chronic disease prediction