Fuzzy Information and Engineering

Karbasioun, Asefeh; Ameri, Reza

2019 Fuzzy Information and Engineering

doi: 10.1080/16168658.2020.1747161

Sometimes information in a network model is based on multi-agent, multi-attribute, multi-object, multi-polar information or uncertainty rather than a single bit. An m-polar fuzzy model is useful for such network models which gives more and more precision, flexibility, and comparability to the system as compared to the classical, fuzzy models. On the other, The Steiner tree problem in networks, and particularly in graphs, was formulated by Hakimi [25] and Levi [21] by definition minimal size connected tree sub graph that contains the vertices in S. Steiner trees have applications to multiprocessor computer networks. For example, it may be desired to connect a certain set of processors with a sub network that uses the fewest communication links. In this paper, we extend Steiner distance SWk (G) for m-polar fuzzy graphs and give this parameter for Join, composition and Cartesian product of two m-polar fuzzy graphs.

Venkata Subba Reddy, Poli

2019 Fuzzy Information and Engineering

doi: 10.1080/16168658.2021.1873610

We consider fuzzy inference of the form ‘if ··· then ··· ’. ‘if ··· then ··· else ···’ and ‘if ··· and/or ··· and/or ··· then ···’. Muzomoto proposed logical constructs for ‘if. then.’ by applying Godelien and Standard Sequence methods. These method do not satisfy all the intuitions In this paper, We propose method of fuzzy inference and applied on logical constructs. We have shown that our fuzzy inference method satisfy all the intuitions under several criterions.

Rahman, K.; Abdullah, S.; Hussain, F.

2019 Fuzzy Information and Engineering

doi: 10.1080/16168658.2020.1746483

Pythagorean fuzzy set (PFS) is one of the prosperous extensions of the intuitionistic fuzzy set (IFS) for handling the fuzziness and uncertainties in the data. Under this environment, in this paper, we introduce the notion of two generalised Einstein hybrid operators namely, generalised Pythagorean fuzzy Einstein hybrid averaging (in short GPFEHA) operator and generalised Pythagorean fuzzy Einstein hybrid geometric (in short GPFEHG) operator along with their desirable properties, such as idempotency, boundedness and monotonicity. The main benefit of the proposed operators is that these operators deliver more general, more correct and precise results as compared to their existing methods. Generalised Einstein operators combine Einstein operators with some generalised operators using Pythagorean fuzzy values. Therefore these methods play a vital role in real world problems. Finally, the proposed operators have been applied to decision-making problems to show the validity, practicality and effectiveness of the new attitude.

Salam, Abdus; Ashraf, Wajih; Mahboob, Ahsan; Khan, Noor Mohammad

2019 Fuzzy Information and Engineering

doi: 10.1080/16168658.2020.1753496

Adopting the notion of a -quasi-coincidence of a fuzzy point with a fuzzy set, the idea of an (∈,∈∨(k ∗ , qk ))-antifuzzy left (right) ideal, (∈,∈∨(k ∗ , qk ))-antifuzzy ideal and (∈,∈∨(k ∗ , qk ))-antifuzzy (generalized) bi-ideal in ordered semigroups are proposed, that are the generalization of the idea of an antifuzzy left (right) ideal, antifuzzy ideal and antifuzzy (generalized) bi-ideal in ordered semigroups and a few fascinating characterizations are obtained. In this paper, we tend to focus to suggest a connection between standard generalized bi-ideals and (∈,∈∨(k ∗ , qk ))-antifuzzy generalized bi-ideals. In addition, different classes of regular ordered semigroups are characterized by the attributes of this new idea. Finally, the -lower part of an (∈,∈∨(k ∗ , qk ))-antifuzzy generalized bi-ideal is outlined and a few characterizations are mentioned.

Es-sabery, Fatima; Hair, Abdellatif

2019 Fuzzy Information and Engineering

doi: 10.1080/16168658.2020.1756099

Decision tree is the most efficient and fast technology of data mining that is frequently used in data analysis and prediction. According to the development in science and technology in the last years, the data is growing faster, and the principle of the decision tree algorithms become not efficient in respect runtime and speed-up ratio. In view of the above problem, we propose a new method of classification based on framework Hadoop and Fuzzy logic. Our proposed hybrid approach is designed to propose a new C4.5 decision tree algorithm using fuzzy logic and fuzzy set theory to handle uncertainty and imprecision in data, and Hadoop framework (MapReduce + HDFS) to parallelize our work. This combination of big data technologies, fuzzy systems and C4.5 decision tree algorithm has produced a parallel fuzzy decision tree model, which takes advantage of these three techniques (hadoop + fuzzy logic + C4.5) to produce a decision tree with higher predictive accuracy. In this paper, an experiment is presented to compare our approach with other approaches from the literature. Experiments were carried out using three datasets, and the results show that our new method outperforms the other approaches in terms of accuracy and execution time.

Pérez-Cañedo, Boris; Concepción-Morales, Eduardo René; Edalatpanah, Seyyed Ahmad

2019 Fuzzy Information and Engineering

doi: 10.1080/16168658.2020.1761511

Ezzati et al. (A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem. Appl Math Model. 2015;39(12):3183–3193) introduced a lexicographic criterion for ranking triangular fuzzy numbers (TFNs), and proposed a method to solve fully fuzzy linear programming (FFLP) problems based on the lexicographic method of multi-objective optimisation; the authors assumed that fuzzy inequality constraints can be transformed into fuzzy equality constraints by introducing non-negative fuzzy slack and surplus variables. They illustrated the proposed method by means of a fully fuzzy investment problem. Bhardwaj and Kumar (A note on “A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem”. Appl Math Model. 2015;39(19):5982–5985) demonstrated that introducing fuzzy slack and surplus variables is mathematically incorrect, and showed that the solution of the fuzzy investment problem is unfeasible. Towards the end of their paper, they claimed that there is no feasible solution to the fuzzy investment problem when considering Ezzati et al.’s ranking criterion. In this paper, we propose a revised version of Ezzati et al.’s method whereby the optimal solution of FFLP problems with equality and inequality constraints can be obtained. Furthermore, by using the revised method, we show that feasible solutions of the fuzzy investment problem actually exist, and therefore Bhardwaj and Kumar’s claim is false. To show the applicability of the revised method, we also consider a fully fuzzy project scheduling problem with budget constraint.

Hu, Renjie; Liao, Liping; Chen, Chen; Zhang, Guangyu

2019 Fuzzy Information and Engineering

doi: 10.1080/16168658.2020.1764465

Centrality analysis is one of the most important and commonly used tools in social networks. For social networks where edges are just present or absent and have no more information attached, many centrality measures have been presented, such as degree, closeness, betweenness, eigenvector and Laplacian centrality. There has been a growing need to design centrality measures for fuzzy enterprise technology innovation cooperation networks (FETICNs), because FETICNs where edges are attached with fuzzy technical cooperation relation would contain rich information. In this paper, we propose some new centrality measures called fuzzy logarithm attenuation closeness centrality and fuzzy logarithm attenuation closeness centralization which are applicable to the FETICNs. It unveils more structural information about fuzzy technical cooperation relation, attenuation factor, and connectivity of the FETICNs. Furthermore, we investigate the validness of a new centrality measure by illustrating this method to an experimental study and obtain reliable results, which provide strong evidences to the new measure’s utility.

Hussain, Sabir

2019 Fuzzy Information and Engineering

doi: 10.1080/16168658.2020.1773600

One of the most important and basic topological properties is connectedness, which reflects the main characteristic of topological spaces and helps us to differentiate two topologies. Keeping in mind the importance of this concept, we initiate binary soft connectedness in binary soft topological spaces and explore its properties. It is interesting to mention that union of two binary soft connected spaces over and may not be a binary soft connected space. We also define and discuss binary soft boundary in binary soft topological spaces and establish the characterisation of binary soft connected spaces in terms of binary soft boundary. Moreover, we present an application of binary soft sets theory in decision making problem. We expect that this will be potentially useful research in theoretical as well as in any applicable directions to handle the problems of uncertainties and environment having ambiguities.