Certain properties of soft multi-set topology with applications in multi-criteria decision making

Riaz, Muhammad; Çağman, Nai̇m; Wali, Nabeela; Mushtaq, Aamir

doi:10.31181/dmame2003070r

Cited by 50 publications

(45 citation statements)

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“…Initially, the limitations of the model must be circumvented. Later, the proposed model could be flexibly used for other crucial decision‐making areas, such as logistic 56 decision, agrodecision, 57 tool/equipment decision, 58 and other areas. Finally, plans are being made to integrate concepts, such as rough sets 59 and machine learning with MCDM models for proper recommendation and decision‐making.…”

Section: Discussionmentioning

confidence: 99%

Assessment of cloud vendors using interval‐valued probabilistic linguistic information and unknown weights

et al. 2021

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Cloud vendors (CVs) play an indispensable role in the development of IT sectors and industry 4.0. Many CVs evolve every day, and a systematic selection of these is becoming substantial for organizations. Literature studies have shown that multicriteria decision‐making (MCDM) is a powerful tool for systematic selection. However, the major issue with the state‐of‐the‐art models is that they do not effectively represent uncertainty. Moreover, the personalized selection of CVs based on user queries is not prominent in an MCDM context. In this paper, to circumvent these issues, a new decision framework is proposed that utilizes a generalized preference style called interval‐valued probabilistic linguistic term set (IVPLTS). This preference style considers occurring probability values as interval numbers instead of a single precise value, which provides flexibility during preference elicitation. Initially, missing values are imputed systematically by using a case‐based method. Then, the consistency of these preferences is checked using Cronbach's alpha coefficient, and the inconsistent preferences are repaired rationally by using an iterative method. A programming model is proposed for determining the weights of the evaluation criteria. Furthermore, Maclaurin symmetric mean (MSM) is extended to IVPLTS for aggregating preferences from each expert. The interval‐valued probabilistic linguistic comprehensive (IVPLC) method is proposed for prioritizing CVs in a personalized manner. Finally, the framework's practicality is validated by using a case study of CV selection for an academic institution; strengths and weaknesses of the framework are conferred by comparison with extant CV selection models.

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Section: Discussionmentioning

confidence: 99%

Assessment of cloud vendors using interval‐valued probabilistic linguistic information and unknown weights

et al. 2021

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“…Hence, Garai et al 30,31 approach depict limitations as such this method will not be a good choice in decision making problems. Further, the decision by the current method is equivalent to that of Biswas et al 28 T A B L E = (1, 4, 4, 7), (0, 4, 4, 8), (1,4,4,7) and 〈 〉 ͠ b = (2,4,4,6), (1,4,4,7), (2,4,4,6) . A comparative study of the methods by Deli and Subas, 27 Biswas et al 28 and with the current method is done.…”

Section: 6mentioning

confidence: 99%

“…Another such generalization of fuzzy numbers, in fact, IFNs are picture fuzzy numbers and neutrosophic numbers, which incorporates the indeterminacy-membership apart from the truth-membership and falsitymembership functions. As such many works are done on the application of these numbers in various decision making problems, namely, Si et al, 5 Riaz et al, 6 Arya and Kumar, 7 Ates and Akay, 8 and so forth. The generalization of neutrosophic numbers is possible through the pioneering work by Smarandache.…”

Section: Introductionmentioning

confidence: 99%

Ordering single‐valued neutrosophic numbers based on flexibility parameters and its reasonable properties

et al. 2021

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Undoubtedly, most of the information conveyed is full of impreciseness or vagueness. In such situation, fuzzy set theory is a proper tool to handle these types of impreciseness or vagueness. Fuzzy numbers are also of different types based on the presence of uncertainty. Neutrosphic numbers are one type of fuzzy number that consists of three types of membership functions, namely, truth, indeterminacy and falsity. In fact, neutrosophic numbers are generalization of intuitionistic fuzzy numbers. These numbers might be used in decision making problems, where it is essential to rank those numbers. So, ordering of these numbers becomes a new trends with more challenges. In this paper, an innovative ranking approach of single-valued neutrosophic number has been put forwarded. The method is based on value, ambiguity and θ function. Value and ambiguity are defined on three functions of the neutrosophic numbers. Further, the reliability of the proposed method is established by considering the reasonable properties of ranking method. One important aspect of ranking neutrosophic numbers is to consider the consistency in ordering the neutrosophic numbers and their corresponding images. It is worth to mention that the current approach consistently ranks

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“…Song et al proposed a new similar measure of membership functions and applied it to clustering analysis and medical diagnosis [25]. Riaz, e.g., analyzed the properties of soft multi-set topology and proposed multi-criteria decision-making algorithms with aggregation operators [26]. Sahoo et al presented a soft computing neural networks tool based on radial function networks and studied the problem of transmission line congestion in electrical power systems [27].…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Fuzzy Set Driven Stochastic Active Contour Model with Uncertainty Analysis

Wang

Zhang

2021

Mathematics

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Image segmentation is a process that densely classifies image pixels into different regions corresponding to real world objects. However, this correspondence is not always exact in images since there are many uncertainty factors, e.g., recognition hesitation, imaging equipment, condition, and atmosphere environment. To achieve the segmentation result with low uncertainty and reduce the influence on the subsequent procedures, e.g., image parsing and image understanding, we propose a novel stochastic active contour model based on intuitionistic fuzzy set, in which the hesitation degree is leveraged to model the recognition uncertainty in image segmentation. The advantages of our model are as follows. (1) Supported by fuzzy partition, our model is robust against image noise and inhomogeneity. (2) Benefiting from the stochastic process, our model easily crosses saddle points of energy functional. (3) Our model realizes image segmentation with low uncertainty and co-produces the quantitative uncertainty degree to the segmentation results, which is helpful to improve reliability of intelligent image systems. The associated experiments suggested that our model could obtain competitive segmentation results compared to the relevant state-of-the-art active contour models and could provide segmentation with a pixel-wise uncertainty degree.

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Certain properties of soft multi-set topology with applications in multi-criteria decision making

Cited by 50 publications

References 0 publications

Assessment of cloud vendors using interval‐valued probabilistic linguistic information and unknown weights

Assessment of cloud vendors using interval‐valued probabilistic linguistic information and unknown weights

Ordering single‐valued neutrosophic numbers based on flexibility parameters and its reasonable properties

An Intuitionistic Fuzzy Set Driven Stochastic Active Contour Model with Uncertainty Analysis

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