Implementing the reliability of data information in multi-criteria decision making process based on fuzzy topsis and fuzzy entropy

A multi-criteria decision-making process utilizes real-time data information, which is inherently uncertain and imprecise. To be relevant in the decision-making process, real-time data information must be reliable. Because fuzziness alone is insufficient to solve decision-making problems, measuring the information's reliability is critical. Z-number, which incorporates both restrictions and reliability in its definition is considered as a powerful tool to depict the imperfect information. In this paper, a new methodology is developed based on fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and fuzzy entropy for solving the multi-criteria decision-making problems where the weight information for decision makers and criteria is incomplete. The evaluation of the information is represented in the form of linguistic terms and the following calculation is performed using Z-numbers. Fuzzy entropy is applied to determine the weights of the criteria and fuzzy TOPSIS is used to rank the alternatives. An empirical study of subjective well-being of working women is used to demonstrate the proposed methodology

Exploring Multi-Criteria Decision-Making for Academic Blockchain Platform Adoption

A decentralised distributed ledger system called Blockchain Technology (BCT) enables safe, open, and impenetrable transactions without the need for a central authority. The technology was initially created for the Bitcoin cryptocurrency, but it has subsequently been applied to other areas such as voting procedures, supply chain management, and digital identity management. The technology is increasingly becoming accepted in the academic setting for a variety of purposes, including the creation and storage of academic records. There are numerous platforms accessible for this usage, though. When numerous decision-makers are engaged in the selection process, picking an appropriate platform can be a contentious affair. For decision makers, selecting among a wide range of acceptable options might be difficult. It is possible to overcome these difficulties by using Multi-criteria Decision-Making (MCDM) techniques. When there are numerous elements to take into account, one technique for making judgments is MCDM. The process entails assessing multiple options according to pre-established standards in order to identify the optimal selection. In essence, when there are several variables to consider, MCDM assists in selecting the option. The Fuzzy Analytic Hierarchy Process (FAHP) is one of the various MCDMs which this paper uses to choose the best BCT platform for academic records based on three choices (IBM, Ethereum, and Hyperledger Fabric) and five factors (cost, degree of acceptance, simplicity of use, data security, and level of customization). The analysis's findings indicate that data security is the most crucial factor, with a weight of 0.645, and that IBM is the best BCT platform, with a value of 0.448. By comparing the FAHP results to those of AHP, IBM's suitability as a platform was confirmed.

The implementation of z-numbers in fuzzy clustering algorithm for wellness of chronic kidney disease patients

By gleaning insights from the data, fuzzy clustering capable to learn from data, identify patterns and make decision with minimum human intervention. However, it cannot simply study in detail regarding the quality of data, particularly knowledge of human being. Since the data are collected through decision-makers, the quality and human knowledge of the particular data are crucial factors to be considered. Compared to classical fuzzy numbers, z-numbers has ability to describe the human knowledge because it has both restraint and reliability part in its definition. Consequently, the implementation of z-numbers in fuzzy clustering algorithm is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in uncertain information development. Thus, there are two objectives of this paper; (i) to propose a reliable fuzzy clustering algorithm using z-numbers and; (ii) to cluster the Chronic Kidney Disease (CKD) patients based on the selected indicators to identify which cluster the patients belongs to (Cluster 0, Cluster 1, Cluster 2, Cluster 3 or Cluster 4) based on the membership functions defined. A case study of the CKD patients with the selected indicators is considered to demonstrate the capability of z-numbers to handle the knowledge of human being and uncertain information and also will present the idea in developing a robust and reliable fuzzy clustering algorithm particularly in dealing with knowledge of human being using z-numbers

Fuzzy analytic hierarchy process using intuitive vectorial centroid for eco-friendly car selection

Eco-friendly car is expected to be the next driving market force for global transportation and technology due to its paramount importance towards the sustainability of the environment and society. However, the actual sales of eco-friendly car are not that convincing and it is even decreasing because the consumer is still uncertain to consider eco-friendly as one of the criteria for them to buy their cars. This situation is worsen by the lack of information and awareness regarding sustainability transportation initiatives. Due to the uncertainty and vague understanding of the consumer about this problem, this paper attempts to investigate the current preference of consumer to buy their cars, and whether they really need to buy the eco-friendly car by using the Fuzzy Analytic Hierarchy Process (FAHP) which implements the Intuitive Vectorial Centroid (IVC). Based on FAHP, the imprecise or fuzzy judgment from the decision maker can be incorporated, to anticipate a better decision for eco-friendly car selection. The outcome of FAHP is compared with crisp Analytic Hierarchy Process (AHP), and the findings shows that FAHP can provide an accurate and consistent result with AHP, although it deals with fuzzy judgment inputs from multiple decision makers