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The object recognition problems realized in uncertain environments have played a paramount role in decision-making. In recent years, neutrosophic soft sets (NS-sets), a combination of soft and neutrosophic sets, have emerged as outstanding candidates in this field. If neutrosophic sets are used to handle problems involving imprecise, indeterminate, and inconsistent data, soft sets are used to deal with uncertainties that classical tools cannot control. This paper defines a new concept based on NS-sets, called the sequence of NS-sets (NSS-sequence). Their inclusions, special types, operations, distances are determined with reasonable, convincing, and well-proven properties. Furthermore, we also propose an algorithm for the decision-making problem on NS-sequence and apply it in medical diagnosis by a real-life experiment. Finally, intending to verify its validity and feasibility, we compare our algorithm to the algorithm for the decision-making problem on time NS-set (tNS-set) through the real-life mentioned earlier by Alkhazaleh. Our work also shows that the proposed algorithm on NS-sequence has the same results as that offered by Alkhazaleh, and the tNS-set is just a particular case of the NSS-sequence. [ABSTRACT FROM AUTHOR]

In the elicitation of decision makers' fuzzy and uncertain assessments, linguistic terms are natural and efficient as the preference modeling tools. Although the linguistic variables are available, they would not be operational without any detailed quantification. Motivated by the flexibility of information granularity, this paper develops information granules to represent linguistic terms in the form of intervals and interval type-2 fuzzy sets (IT2FSs) in best worst method (BWM). The development is aimed at minimizing inconsistency in the decision making (DM) process to ensure the rationality of the assessments provided by decision makers. Furthermore, the input and output based consistencies of BWM are considered. The granulation of entries of pairwise comparison vectors are the foundation of BWM to formulate an optimization problem where particle swarm optimization (PSO) algorithm serves as the optimization framework. Both individual and group decision making (GDM) scenarios are taken into consideration. For the GDM process, a performance index for measuring the group consensus is also proposed. Several examples and validity analysis are covered to illustrate the major ideas of this study. Finally, as a case study, a recommendation of the sequence of visiting tourist attractions in Wuhan and the corresponding comparative analysis are represented. • A granular computing-based method in BWM framework is proposed. • Construction of granular linguistic information to represent preference information of DMs is given. • A novel Euclidean distance function to measure the agreement among the group is proposed. • A case study of scenic spots recommendation in Wuhan with online reviews is exhibited. [ABSTRACT FROM AUTHOR]

Information quality has attracted increasing attention in recent years. In this paper, we propose a measure to quantify the information quality of intuitionistic fuzzy information based on a pseudo probability transformation, and derive its induced order to rank intuitionistic fuzzy values. The proposed measure and the induced order are proved to possess some well-defined properties that ensure rationality. Further, in order to rank intuitionistic fuzzy alternatives in decision making, we turn the proposed information quality into a ranking-oriented information quality, and present its induced order to generate the ranking. The decision capacity of our method is founded with the goal of rationalizing, simulating and further facilitating the interpretability and transparency in human decision making. A comparative study has been conducted, demonstrating the effectiveness of the proposed approach to ranking alternatives. The proposed approach is shown to outperform existing ranking methods via case-by-case comparisons. Finally, an application involving decision making of drug selection and an application in multi-criteria decision analysis of supplier selection are provided. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology provides new possibilities for a variety of applications, but it also comes with challenges influenced by distinct aspects, such as the size of organizations that use such technology. Therefore, it is important to understand which architecture of 5G-radio access networks (RANs) is best for a given purpose; this requires an evaluation platform for assessment. This paper tackles this problem by presenting a novel multi-criteria decision-making (MCDM) solution based on a new integrated fuzzy set. The proposed integrated approach, which is based on a Type-2 neutrosophic fuzzy environment, is developed to address the application challenges of 5G-RANs architecture evaluation, as also to face the MCDM theoretical challenge represented by ambiguities and inconsistencies among decision makers within the decision making context of the presented case study. Many MCDM techniques for weighting and selection were presented from the literature, yet many of them still suffer from inconsistencies and uncertainty. Therefore, the chosen methods in this research are unique in a way that previous issues are addressed, making them suitable for integration with Type-2 neutrosophic fuzzy environment, and therefore creating a more robust decision platform for the presented challenge in this research, as a theoretical contribution. First, a new "Type-2 Neutrosophic Fuzzy-Weighted Zero-Inconsistency" (T2NN-FWZIC) technique is formulated for weighting the evaluation criteria of RAN architectures. Second, another new method, namely, "Type-2 Neutrosophic Fuzzy Decision by Opinion Score Method" (T2NN-FDOSM), was formulated to select the optimal RAN architecture using the obtained weights. The weighting results by T2NN-FWZIC for the (n = 25) evaluation criteria revealed that (C 21 latency and C 22 reliability) as the most important criteria, with 0.06 value for each as opposed to (C 15 Data Processing) as the lowest weighted criteria with 0.0186 value. As for T2NN-FDOSM, a total of four 5G-RAN architectures were evaluated, including "virtualized cloud RAN" coming as the optimal one, followed by "fog RAN," "cloud RAN," and finally "heterogeneous cloud RAN." The results were confirmed by carrying out a sensitivity analysis. The outcome of this study can be used to assist future 5G-RAN developments according to business needs and to establish an assessment platform for 5G technology in different domains and applications. [ABSTRACT FROM AUTHOR]