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

Bipolarity in ear biometrics

Identifying people using their biometric data is a problem that is getting increasingly more attention. This paper investigates a method that allows the matching of people in the context of victim identification by using their ear biometric data. A high quality picture (taken professionally) is matched against a set of low quality pictures (family albums). In this paper soft computing methods are used to model different kinds of uncertainty that arise when manually annotating the pictures. More specifically, we study the use of bipolar satisfaction degrees to explicitly handle the bipolar information about the available ear biometrics

An Intuitionistic Fuzzy Group Decision-Making to Measure the Performance of Green Supply Chain Management with TOPSIS Method

Green supply chain management (GSCM) integrates environmental regulations into supply chain management to diminish the negative effects of supply chain processes on the environment. The environmental problems appeared by an enterprise arise from designing the product and last until the recycling process. GSCM activities include five drivers such as green design, green purchasing, green transformation, green logistics and reverse logistics. In this chapter, the main aim is to explain these drivers and to show how to measure the GSCM success of companies, which operate as tire manufacturers by using an evaluation of a group of experts in their field. The proposed method, intuitionistic fuzzy technique for order preference by similarity to an ideal solution as an effective group decision-making method, helped to identify the alternative company 1 with the best GSCM performance among six different alternative tire companies under the consideration of five important GSCM drivers. The ranking result was as follows: A1 > A3 > A4 > A2 > A6 > A5

Application of intuitionistic fuzzy sets in determining the major in senior high school

Intuitionistic Fuzzy Set (IFS) is useful to construct a model with elaborate uncertainty and ambiguity involved in decision-making. In this paper, the concept relation and operation of intuitionistic fuzzy set and the application in major of senior high school determination using the normalized Euclidean distance method will be reviewed. Some theorem of relation and operation of intuitionistic fuzzy set are proved. In general, to prove the theorem the definition and some basic relation and operation laws of IFS are needed. The distance measure between IFS indicates the difference in grade between the information carried by IFS. There are science, social, and language majors in senior high school. The normalized Euclidean distance method is used to measure the distance between each student and each major. The major, which each student can choose, has been determined depending on test evaluations. The solution provided is the smallest distance between each student and each major using the normalized Euclidean distance method

Application of intuitionistic fuzzy sets in determining the major in senior high school

Intuitionistic Fuzzy Set (IFS) is useful to construct a model with elaborate uncertainty and ambiguity involved in decision-making. In this paper, the concept relation and operation of intuitionistic fuzzy set and the application in major of senior high school determination using the normalized Euclidean distance method will be reviewed. Some theorem of relation and operation of intuitionistic fuzzy set are proved. In general, to prove the theorem the definition and some basic relation and operation laws of IFS are needed. The distance measure between IFS indicates the difference in grade between the information carried by IFS. There are science, social, and language majors in senior high school. The normalized Euclidean distance method is used to measure the distance between each student and each major. The major, which each student can choose, has been determined depending on test evaluations. The solution provided is the smallest distance between each student and each major using the normalized Euclidean distance method

Multiple Attributes Decision Fusion for Wireless Sensor Networks Based on Intuitionistic Fuzzy Set

Decision fusion is an important issue in wireless sensor networks (WSN), and intuitionistic fuzzy set (IFS) is a novel method for dealing with uncertain data. We propose a multi-attribute decision fusion model based on IFS, which includes two aspects: data distribution-based IFS construction algorithm (DDBIFCA) and the category similarity weight-based TOPSIS intuitionistic fuzzy decision algorithm (CSWBT-IFS). The DDBIFCA is an IFS construction algorithm that transforms the original attribute values into intuitionistic fuzzy measures, and the CSWBT-IFS is an intuitionistic fuzzy aggregation algorithm improved by the traditional TOPSIS algorithm, which combines intuitionistic fuzzy values of different attributes and obtains a final decision for the monitoring target. Both algorithms have benefits, such as low energy consumption and low computational complexity, which make them suitable for implementation in energy-constrained WSNs. Simulation results show the efficiency of intuitionistic fuzzification for the DDBIFCA and a high classification accuracy, compared with traditional fuzzy fusion and other intuitionistic fuzzy aggregation algorithms, for the CSWBT-IFS