An Integrated FCEM-AHP Approach for Borrower’s Satisfaction and Perception Analysis of Microfinance Institution

Themain objective of this paper is to present an integrated approach to evaluate the level of satisfaction of borrowers with the products and services of microfinance institutions(MFI)at different criterion levels.For this,the study adopts the concept of FCEM(Fuzzy Comprehensive EvaluationMethod)in concurrence with the AHP(Analytical Hierarchy Process).In our day-to-day situation,the researchers have made many efforts to assess the impact of Microfinance on poverty reduction,but borrowers’satisfaction is always overlooked.Since the multiple factors impact the borrower’s satisfaction,each factor is made of different items.Thus,as the factors items increase,many uncertainties are created,and hence this will make the decisionmaking unsmooth or imprecise.To describe this,the FCEM method deals with the vagueness in the collection information phase.However,the AHP has been utilized to determine the objective weights of each factor.The presented integrated framework has been illustrated with a case study and presented their results.The study’s managerial benefit is also reported to address the situation.

Multi-Criteria Decision Making Based on Bipolar Picture Fuzzy Operators and New Distance Measures

This paper aims to introduce the novel concept of the bipolar picture fuzzy set(BPFS)as a hybrid structure of bipolar fuzzy set(BFS)and picture fuzzy set(PFS).BPFS is a new kind of fuzzy sets to deal with bipolarity(both positive and negative aspects)to each membership degree(belonging-ness),neutral membership(not decided),and non-membership degree(refusal).In this article,some basic properties of bipolar picture fuzzy sets(BPFSs)and their fundamental operations are introduced.The score function,accuracy function and certainty function are suggested to discuss the comparability of bipolar picture fuzzy numbers(BPFNs).Additionally,the concept of new distance measures of BPFSs is presented to discuss geometrical properties of BPFSs.In the context of BPFSs,certain aggregation operators(AOs)named as“bipolar picture fuzzy weighted geometric(BPFWG)operator,bipolar picture fuzzy ordered weighted geometric(BPFOWG)operator and bipolar picture fuzzy hybrid geometric(BPFHG)operator”are defined for information aggregation of BPFNs.Based on the proposed AOs,a new multicriteria decision-making(MCDM)approach is proposed to address uncertain real-life situations.Finally,a practical application of proposed methodology is also illustrated to discuss its feasibility and applicability.

Linguistic Single-Valued Neutrosophic Power Aggregation Operators and Their Applications to Group Decision-Making Problems

Linguistic single-valued neutrosophic set(LSVNS)is a more reliable tool,which is designed to handle the uncertainties of the situations involving the qualitative data.In the present manuscript,we introduce some power aggregation operators(AOs)for the LSVNSs,whose purpose is to diminish the influence of inevitable arguments about the decision-making process.For it,first we develop some averaging power operators,namely,linguistic single-valued neutrosophic(LSVN)power averaging,weighted average,ordered weighted average,and hybrid averaging AOs along with their desirable properties.Further,we extend it to the geometric power AOs for LSVNSs.Based on the proposed work;an approach to solve the group decision-making problems is given along with the numerical example.Finally,a comparative study and the validity tests are present to discuss the reliability of the proposed operators.

Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

These days,imbalanced datasets,denoted throughout the paper by ID,(a dataset that contains some(usually two)classes where one contains considerably smaller number of samples than the other(s))emerge in many real world problems(like health care systems or disease diagnosis systems,anomaly detection,fraud detection,stream based malware detection systems,and so on)and these datasets cause some problems(like under-training of minority class(es)and over-training of majority class(es),bias towards majority class(es),and so on)in classification process and application.Therefore,these datasets take the focus of many researchers in any science and there are several solutions for dealing with this problem.The main aim of this study for dealing with IDs is to resample the borderline samples discovered by Support Vector Data Description(SVDD).There are naturally two kinds of resampling:Under-sampling(U-S)and oversampling(O-S).The O-S may cause the occurrence of over-fitting(the occurrence of over-fitting is its main drawback).The U-S can cause the occurrence of significant information loss(the occurrence of significant information loss is its main drawback).In this study,to avoid the drawbacks of the sampling techniques,we focus on the samples that may be misclassified.The data points that can be misclassified are considered to be the borderline data points which are on border(s)between the majority class(es)and minority class(es).First by SVDD,we find the borderline examples;then,the data resampling is applied over them.At the next step,the base classifier is trained on the newly created dataset.Finally,we compare the result of our method in terms of Area Under Curve(AUC)and F-measure and G-mean with the other state-of-the-art methods.We show that our method has betterresults than the other state-of-the-art methods on our experimental study.

Information Theoretic Weighted Fuzzy Clustering Ensemble

In order to improve performance and robustness of clustering,it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique.Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks.However,in these approaches,cluster(or clustering)reliability has not paid much attention to.Ignoring cluster(or clustering)reliability makes these approaches weak in dealing with low-quality base clustering methods.In this paper,we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means(RBFCM),Reliability Based Graph Partitioning(RBGP)and Reliability Based Hyper Clustering(RBHC)as three new fuzzy clustering consensus functions.Our fuzzy clustering ensemble approach works based on fuzzy cluster unreliability estimation.Cluster unreliability is estimated according to an entropic criterion using the cluster labels in the entire ensemble.To do so,the new metric is dened to estimate the fuzzy cluster unreliability;then,the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator(RDCI).The time complexities of RBHC and RBGP are linearly proportional with thnumber of data objects.Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets.The experimental results demonstrate efciency and suitability of the proposed method.

Optimal Reordering Trace Files for Improving Software Testing Suitcase

An invariant can be described as an essential relationship between program variables.The invariants are very useful in software checking and verification.The tools that are used to detect invariants are invariant detectors.There are two types of invariant detectors:dynamic invariant detectors and static invariant detectors.Daikon software is an available computer program that implements a special case of a dynamic invariant detection algorithm.Daikon proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical analysis.This method has some drawbacks.One of its biggest drawbacks is its overwhelming time order.It is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace files.It is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this approach.Experimental findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

CHFS: Complex hesitant fuzzy sets‐their applications to decision making with different and innovative distance measures

The objective of the authors is to establish an innovative concept of the complex hesitant fuzzy set(CHFS),which is the combination of the hesitant fuzzy set and the complex fuzzy set to manage complex and awkward information in the real‐decision theory.The structure and the basic properties of the proposed set are studied in detail.Based on the internal structure of the set and to find the degree of the discrimination between the pairs of the CHFSs,the generalized distance measures and modified generalized distance measures are defined.Several properties and their relationship between them are derived in detail.Also,several cases of the proposed measures are exposed which reduce them to the existing studies.Furthermore,based on these proposed measures,a decision‐making approach is established under the uncertain environment and several numerical examples are given to examine the feasibility and validity of the explored measures.Finally,the credibility of the modified and parameterized distance measures based on CHFSs is verified by comparing them with some existing measures.

Guest Editorial: Neutrosophic decision making and applications in knowledge management

With the complexity of the socio-economic environment,today’s decision-making is one of the most notable ventures,whose mission is to decide the best alternative under the numerous known or unknown criteria.In cognition of things,people may not possess a precise or sufficient level of knowledge of the problem domain and hence they usually have some sort of uncertainties in their preferences over the objects.This will make the performance of the cognitive in terms of three-way model,namely acceptation,rejection,indeterminacy,which falls under the neutrosophic set theory,an extension of the fuzzy set theory.Nowadays,many new extensions of the ordinary neutrosophic set are proposed and they are expected to be competitive with the other extensions in the future.On the other hand,in recent years,human beings inescapably are met with numerous decision-making problems,which affect multiple fields;however,few of them are in the area of knowledge management.Therefore it is necessary to provide a dedicated forum for discussion on these settings and their extensions,and applications in the real world as well.

Multi-objective linear fractional inventory model with possibility and necessity constraints under generalised intuitionistic fuzzy set environment

This study presented a multi-objective linear fractional inventory (LFI) problem with generalised intuitionistic fuzzy numbers. In modelling, the authors have assumed the ambiances where generalised trapezoidal intuitionistic fuzzy numbers (GTIFNs) used to handle the uncertain information in the data. Then, the given multi-objective generalised intuitionistic fuzzy LFI model was transformed into its equivalent deterministic linear fractional programming problem by employing the possibility and necessity measures. Finally, the applicability of the model is demonstrated with a numerical example and the sensitivity analysis under several parameters is investigated to explore the study.

Cubic q-Rung Orthopair Fuzzy Linguistic Set and Their Application to Multiattribute Decision-making with Muirhead Mean Operator

The objective of this paper is to present a new concept,named cubic q-rung orthopair fuzzy linguistic set(Cq-ROFLS),to quantify the uncertainty in the information.The proposed Cq-ROFLS is a qualitative form of cubic q-rung orthopair fuzzy set,where membership degrees and nonmembership degrees are represented in terms of linguistic variables.The basic notions of Cq-ROFLS have been introduced and study their basic operations and properties.Furthermore,to aggregate the different pairs of preferences,we introduce the Cq-ROFL Muirhead mean-(MM),weighted MM-,dual MM-based operators.The major advantage of considering the MM is that it considers the interrelationship between more than two arguments at a time.On the other hand,the Cq-ROFLS has the ability to describe the qualitative information in terms of linguistic variables.Several properties and relation of the derived operators are argued.In addition,we also investigate multiattribute decision-making problems under the Cq-ROFLS environment and illustrate with a numerical example.Finally,the effectiveness and advantages of the work are established by comparing with other methods.

Comments on ＂Some new distance measures for type-2 fuzzy sets and distance measure based ranking for group decision making problems＂

In this article, we have pointed out that some propositions corresponding to the distance measure between the type-2 fuzzy sets （T2FSs） as provided by Singh （Frontiers of Computer Science, 2014, 8（5）, 741-752）, are incorrect by a counterexample. Further, these propositions have been corrected in the present manuscript by giving a correct relation between the T2FSs and validating it with a numerical example.