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An Information Dissemination Influence Model for Mobile Social Network under MultiRole View Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
Jianfeng Li; Fangshuo Li; Wenxiang Wang; Jun ZhaiMobile social networks are dominating in our society’s daily life because of fast advancements of information technologies. To further exploit benefits from the ubiquitous service, studying the influence of information dissemination in this kind of social network becomes a necessity. This paper proposes a mobile social network influence model with regard to multiple roles. In the model, the concept

Monthly River Discharge Prediction by Wavelet Fuzzy Time Series Method Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
Eyyup Ensar Başakin; Mehmet ÖzgerPrediction of river discharge is important for water resources management. Engineers have developed many physical and mathematical models for prediction of river discharge. The fact that physical hydrological models are site specific and include many parameters, has led researchers to work on mathematical blackbox models. In this study, the fuzzy time series (FTS) method was used in the prediction

Ridge Estimation for Uncertain Autoregressive Model with Imprecise Observations Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
Dan Chen; Xiangfeng YangThe objective of time series analysis is to study the relationship between the data over time and to predict future values. Traditionally, statisticians assume that the observation data are precise, and we can get some exact values. However, in many cases, the imprecise observation data are available. We assume that these data are uncertain variables in the sense of uncertainty theory. In this paper

On the Connectedness of Random Sets of ℝ Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
Juan Jesús SalamancaRandom sets arise as distinguished models to study probability under imprecision. This work aims to determine the connectedness of a random set in terms of its capacity functional (the probability of hitting a set). This problem is linked to the celebrated Choquet–Kendall–Matheron theorem, which states that a random closed set is characterized by its capacity functional. Hence, such functional must

Fuzzy TypeII Resource Allocation and Target Setting in Data Envelopment Analysis: A Real Case of Gas Refineries Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
Sarah J. Sharahi; Kaveh KhaliliDamghani; AmirReza Abtahi; Alireza Rashidi KomijanData envelopment analysis (DEA) is a linear programming method to measure the relative efficiency of a set of decisionmaking units (DMUs) with multiple inputs and outputs. Some DEA models are used to allocate resources and to set targets. Previous studies in resource allocation and target setting have used deterministic data; however, in real problems resources and targets are not deterministic. In

On Some Construction Method and Orness Measure of BiCapacities Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
LeSheng Jin; Radko Mesiar; Andrea Stupnanov; Ronald R. Yager; Martin KalinaBiChoquet Integrals based on bicapacities are very powerful aggregation operators introduced by Grabisch and Labreuche, and they can generalize Choquet Integrals. Bicapacities can model a lager range of preferences than capacities. However, there are few existent knowledge about constructing bicapacities, and there lacks the practical and reasonable orness/andness definitions for bicapacities

The α, βCut Intervals and Weakest tNorm Based Importance Measure for Criticality Analysis in Intuitionisitic Fuzzy Fault Tree Analysis of LNGESD System Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
Mohit Kumar; Swati SharmaDifferent importance measures have been developed and widely utilized in system reliability analysis to measure the importance of system components. During operation period of a complex system, it becomes more difficult to collect exact failure data of system components i.e. the collected failure data may have some sort of uncertainties due to various reasons. Generally, triangular shaped fuzzy numbers

Intuitionistic Fuzzy Calculus Based on Einstein Operations Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
Changhong Guo; Shaomei FangThe intuitionistic fuzzy set (AIFS) was introduced by Atanassov to generalize the concept of Zadeh’s fuzzy set. The basic elements of an AIFS are the intuitionistic fuzzy numbers (IFNs). In this paper, we deal with the intuitionistic fuzzy set with the help of Einstein operations. First we introduce some operations of AIFS, such as the Einstein sum, Einstein product, Einstein scalar multiplication

Interval Methods in Knowledge Representation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20210120
Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA.

JGraphs: A Toolset to Work with MonteCarlo Tree SearchBased Algorithms Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201228
Vicente GarcíaDíaz; Edward Rolando NúñezValdez; Cristian González García; Alberto GómezGómez; Rubén González CrespoMonteCarlo methods are the basis for solving many computational problems using repeated random sampling in scenarios that may have a deterministic but very complex solution from a computational point of view. In recent years, researchers are using the same idea to solve many problems through the socalled MonteCarlo Tree Search family of algorithms, which provide the possibility of storing and reusing

A New Mail System for Secure Data Transmission in Cyber Physical Systems Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201228
Diego Piedrahita Castillo; Francisco Machío Regidor; Javier Bermejo Higuera; Juan Ramón Bermejo Higuera; Juan Antonio Sicilia MontalvoThis paper provides a complete study on email requirements, with special emphasis on its security aspects and architecture. It explores how current protocols have evolved, the environment in which they have been developed and the evolution of security requirements. This paper also analyzes email vulnerabilities and the reasons that have motivated the exploitation of them. The threats and solutions

Voltage Regulation using Probabilistic and Fuzzy Controlled Dynamic Voltage Restorer for Oil and Gas Industry Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201228
Monika Gupta; Smriti Srivastava; Gopal Chaudhary; Manju Khari; Javier Parra FuenteIn a power distribution system, faults occurring can cause voltage sag that can affect critical loads connected in the power network which can cause serious effects in the oil and gas industry. The objective of this paper is to design and implement an efficient and economical dynamic voltage restorer (DVR) to compensate for voltage sag conditions in the oil and gas industry. Due to the complexity and

A Review on Intrusion Detection Systems and Techniques Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201228
Nitesh Singh Bhati; Manju Khari; Vicente GarcíaDíaz; Elena VerdúAn Intrusion Detection System (IDS) is a network security system that detects, identifies, and tracks an intruder or an invader in a network. As the usage of the internet is growing every day in our society, the IDS is becoming an essential part of the network security system. Therefore, the proper research and implementation of IDSs are required. Today, with the help of improved technologies at our

Adaptive Decentralized Tracking Control for Nonlinear LargeScale Systems Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201228
Karthik Chandran; Weidong Zhang; Rajalakshmi Murugesan; S. Prasanna; A. Baseera; Sanjeevi PandiyanDecentralized Model Reference Adaptive Control problems are investigated for a class of linear timeinvariant two timescale model, having fast and slow dynamics and unmatched interconnections. Design of full state feedback controller is a critical task to the system having interrelated dynamics and nonlinear interconnections of time varying lags, however, can be addressed by singular perturbation

The Optimization of the Dynamic Mechanism of Jilin’s Economic Revitalization from the Perspective of Viterbi Algorithm’s SupplySide Structural Reform Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201228
Yuzi JinUnder the background of the rapid development of the Internet, the development information and social status that affect economic regeneration exist in the form of literal data in massive data information. The use of Viterbi algorithm can establish a digital model of information extraction, so as to quickly and accurately find the important content information in the Internet that influences the development

Pattern Mining Approaches Used in Social Media Data Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201228
Jyotismita Chaki; Nilanjan Dey; B. K. Panigrahi; Fuqian Shi; Simon James Fong; R. Simon SherrattSocial media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structuredunstructured and formalinformal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites

Research on the Evaluation of the Dissemination Ability of SciTech Periodicals Based on Hesitant Fuzzy Linguistic Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201228
Bing Liu; Zhijun Lv; Nan Zhu; Dongyu ChangAlthough there is much discussion on the evaluation of the dissemination ability of scitech periodicals, there is no perfect evaluation method system in the existing studies, and especially the evaluation model is not operable. This paper converts the qualitative evaluation of the dissemination ability into a multicriteria decisionmaking problem, and proposes a research method of scitech periodicals

Novel Fuzzy MultiAttribute Decision Making Method for the Selection of Open Unit Trusts Under Uncertainty Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201125
Adam BorovičkaInvestment decision making is a complex process. Important decision concerns the (pre)selection of potential investment instruments to compose an investment portfolio. Conscientious selection involves the application of quantitative multicriteria procedures. The typical element of uncertainty (i.e. unstable return or investor’s vague preferences about the criteria importance) must be considered. This

MultiCriteria Decision Making Analysis Based on TargetOriented OWA Operator Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201125
Meimei XiaThe targetoriented multicriteria decision making is investigated based on the ordered weighted averaging (OWA) operator. The criteria evaluations are measured by using the likelihood of satisfying the targets of criteria. To aggregate the targetoriented criteria evaluations, the targetoriented OWA operator is firstly introduced, in which the targetoriented criteria evaluations are reordered and

Uncertain Autoregressive Model via LASSO Procedure Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201125
Ziqian Zhang; Xiangfeng Yang; Jinwu GaoUncertain time series analysis is a method to predict future values based on imprecisely observed values. As a basic model of uncertain time series, an uncertain autoregressive model has been presented. However, the existing paper ignores the temporal dependence information embedded in timeseries data. In dealing with this issue, this paper adds a least absolute shrinkage and selection operator penalty

Mining Fuzzy Common Sequential Rules with Fuzzy TimeInterval in Quantitative Sequence Databases Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201125
Do Van Thanh; Truong Duc PhuongThere are two kinds of sequential rules. They are classical sequential rules and common sequential rules. The common sequential rules present the relationship between unordered itemsets in which all the items in the antecedent part have to appear before the ones in the consequent part. All existing algorithms for mining common sequential rules can not apply to quantitative sequence databases. Furthermore

A ContentBased Approach to Profile Expansion Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201125
Diego Fernández; Vreixo Formoso; Fidel Cacheda; Victor CarneiroCollaborative Filtering algorithms suffer from the socalled coldstart problem. In particular, when a user has rated few items, recommendations offered by these algorithms are not too accurate. Profile Expansion techniques have been described as a way to tackle this problem without bothering the user with additional information requests by increasing automatically the size of the user profile. Up

On the Core, Bargaining Set and the Set of Competitive Allocations of Fuzzy Exchange Economy with a Continuum of Agents Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201125
Xia Zhang; Hao Sun; Moses Olabhele EsangbedoIn this paper, we present a new model closer to the reallife — called the fuzzy exchange economy with a continuum of agents (FXECA) — that combines fuzzy consumption and fuzzy initial endowment with the agent’s fuzzy preference in the fuzzy consumption set. To characterize the fuzzy competitive allocations of the FXECA, we define the indifference fuzzy core of a FXECA as the set of all fuzzy allocations

FMLBased Reinforcement Learning Agent with Fuzzy Ontology for HumanRobot Cooperative Edutainment Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201125
ChangShing Lee; MeiHui Wang; YiLin Tsai; WeiShan Chang; Marek Reformat; Giovanni Acampora; Naoyuki KubotaThe currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that humanrobot cooperation is of special importance. Various types of robots have been applied to the socalled field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzybased system for a humanrobot cooperative Edutainment

Interval Methods in Knowledge Representation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20201125
Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA…

Robust Fuzzy Clustering Algorithms for ChangePoint Regression Models Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
KangPing Lu; ShaoTung ChangThis article presents a robust fuzzy procedure for estimating changepoint regression models. We propose incorporating the fuzzy changepoint algorithm with the Mestimation technique for robust estimations. The fuzzy c partitions concept is embedded into the changepoint regression model so the fuzzy cregressions and fuzzy cmeans clustering can be employed to obtain the estimates of changepoints

A General Cipher for Individual Data Anonymization Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
Nicolas RuizOver the years, the literature on individual data anonymization has burgeoned in many directions. While such diversity should be praised, it does not come without some difficulties. Currently, the task of selecting the optimal analytical environment is complicated by the multitude of available choices and the fact that the performance of any method is generally dependent of the data properties. In

UncertaintyAware Dissimilarity Measures for IntervalValued Fuzzy Sets Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
Emilio TorresManzanera; Pavol Král; Vladimír Janiš; Susana MontesDissimilarities are a very usual way to compare two fuzzy sets and also two intervalvalued fuzzy sets. In both cases, the dissimilarity between two sets is a number. In this work, we introduce a generalization of the notion of dissimilarity for intervalvalued fuzzy sets such that it assumes values on the set of subintervals instead of the set of numbers. This seems to be more realistic taking into

CrossValidation for the Uncertain ChapmanRichards Growth Model with Imprecise Observations Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
Zhe Liu; Lifen JiaRegression analysis estimates the relationships among variables which has been widely used in growth curves, and crossvalidation as a model selection method assesses the generalization ability of regression models. Classical methods assume that the observation values of variables are precise numbers while in many cases data are imprecisely collected. So this paper explores the ChapmanRichards growth

MPE Computation in Bayesian Networks Using MiniBucket and Probability Trees Approximation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
Andrés Cano; Manuel GómezOlmedo; Serafín Moral; Serafín MoralGarcíaGiven a set of uncertain discrete variables with a joint probability distribution and a set of observations for some of them, the most probable explanation is a set or configuration of values for nonobserved variables maximizing the conditional probability of these variables given the observations. This is a hard problem which can be solved by a deletion algorithm with max marginalization, having

On Generating of tNorms and tConorms on Bounded Lattices Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
Gül Deniz ÇaylıIn this paper, we study tnorms and tconorms on bounded lattices. We propose new methods for generating these operators, applicable on any bounded lattice M by use of the presence of a tnorm on [0M, k] and a tconorm on [k, 1M] for an element k ∊ M\{0M, 1M}. In addition, some corresponding examples are provided for well understanding the structure of new tnorms and tconorms.

Modal Interval Probability: Application to BonusMalus Systems Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
Romàn Adillon; Lambert Jorba; Maite MármolClassical intervals have been a very useful tool to analyze uncertain and imprecise models, in spite of operative and interpretative shortcomings. The recent introduction of modal intervals helps to overcome those limitations. In this paper, we apply modal intervals to the field of probability, including properties and axioms that form a theoretical framework applied to the Markovian analysis of BonusMalus

DistanceBased Consistency Measure and Priority Weights of BestWorst MultiCriteria Decision Making Method Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
XiangQian Feng; XiaoDong Pang; CuiPing WeiBestWorst method (BWM) is a new multicriteria decisionmaking method based on pairwise comparisons, but only the comparisons concerning the best and the worst alternatives or criteria. This method shows some significant advantages in the simplicity with a less requirement of comparison data and reliability with better consistency. This paper proposes a new consistency measure method based on the

Interval Methods in Knowledge Representation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200930
Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA.

Why BlackScholes Equations Are Effective Beyond Their Usual Assumptions: SymmetryBased Explanation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Warattaya Chinnakum; Sean AguilarNobelPrizewinning BlackScholes equations are actively used to estimate the price of options and other financial instruments. In practice, they provide a good estimate for the price, but the problem is that their original derivation is based on many simplifying statistical assumptions which are, in general, not valid for financial time series. The fact that these equations are effective way beyond

Uncertainty in Information Market Games Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Saadia El Obadi; Silvia MiquelA new product can be produced and sold in a market thanks to the entrance of a patent holder into the market. This market is divided into submarkets controlled by only some firms and the profit attainable in each submarket is uncertain. In this paper, this situation is studied by means of cooperative games under interval uncertainty. We consider different ways of allocating the interval profit among

Beyond Deep Learning: An Econometric Example Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Ruofan Liao; Paravee Maneejuk; Songsak SriboonchittaIn the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learningbased predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural

How to Take Both NonLinearity and Asymmetry (Skewness) into Account in Binary Decision Making: SkewProbit and SkewLogit in Binary Kink Regression Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Paravee ManeejukIn many practical situations, it is desirable to predict binary (“yes”–“no”) decisions made by people. The traditional approach to this prediction assumes that the utility linearly depends on the corresponding parameters, and that the distribution of the difference between predicted and actual utility is symmetric — usually normal or logistic; the corresponding techniques are known as, correspondingly

Comparing Medical Care Costs using Bayesian Credible Intervals for the Ratio of Means of DeltaLognormal Distributions Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Patcharee Maneerat; SaAat NiwitpongWhen considering the medical care costs data with a high proportion of zero items of two inpatient groups, comparing them can be estimated using confidence intervals for the ratio of the means of two deltalognormal distributions. The Bayesian credible intervalbased uniformbeta prior (BCIhUB) is proposed and compared with the generalized confidence interval (GCI), fiducial GCI (FGCI), the method

Ruin Probabilities of ContinuousTime Risk Model with Dependent Claim Sizes and Interarrival Times Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Nguyen Huy Hoang; Bao Quoc TaIn this paper we investigate an insurance continuoustime risk model when the claim sizes and interarrival times are mdependent random variables. We provide an upper exponential bound for the ruin probability.

Why the Use of Convex Combinations Works Well for Interval Data: A Theoretical Explanation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Rungrapee Phadkantha; Woraphon YamakaOne of the main objectives of econometrics is to predict future values of important economicsrelated quantities, such as unemployment level, stock prices, currency exchange rates, etc. — and especially to predict how different possible economyboosting measures will affect these quantities. To perform this prediction, we design a model of such effect and train it on the available data. Usually, the

On Statistics of Random Sets for Partial Identification of Econometric Structures Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Jirakom Sirisrisakulchai; Chon Van Le; Uyen PhamIn this paper, we emphasize and elaborate on two important and relatively new aspects in uncertainty analysis in order to increase the credibility of empirical results in statistics in general, and in econometrics in particular, namely, the problem of partial identification, and the use of random set statistics. We elaborate on the current interests in partially identified models, exemplified by econometric

The BlackLitterman Model for Portfolio Optimization on Vietnam Stock Market Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Bao Quoc Ta; Thao VuongThe BlackLitterman asset allocation model is an extended portfolio management model to construct optimal portfolios by combining the market equilibrium with investor views into asset allocation decisions. In this paper we apply BlackLitterman model for portfolio optimization on Vietnames stock market. We chose ARIMA methodology utilized in financial econonometrics to predict the views of investor

A Mixed CopulaBased Vector Autoregressive Model for Econometric Analysis Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200828
Woraphon Yamaka; Sukrit ThongkairatIn many practical applications, the dynamics of different quantities is reasonably well described by linear equations. In economics, such linear dynamical models are known as vector autoregressive (VAR) models. These linear models are, however, only approximate. The deviations of the actual value of each quantity from the predictions of the linear model are usually well described by normal or Studentt

Intuitionistic Fuzzy Partial Logistic Regression Model Using Ridge Methodology Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Gholamreza Hesamian; Mohammad Ghasem Akbari; Mehdi RoozbehThis paper applies a ridge estimation approach in an existing partial logistic regression model with exact predictors, intuitionistic fuzzy responses, intuitionistic fuzzy coefficients and intuitionistic fuzzy smooth function to improve an existing intuitionistic fuzzy partial logistic regression model in the presence of multicollinearity. For this purpose, ridge methodology is also involved to estimate

Project TimeCostQuality Tradeoff Problem: A Novel Approach Based on Fuzzy Decision Making Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Esmaeil Keshavarz; Abbas ShoulTradeoff problems concentrate on balancing the main parameters of a project as completion time, total cost and quality of activities. In this study, the problem of project timecostquality tradeoff is formulated and solved from a new standpoint. For this purpose, completion time and crash cost of project are illustrated as fuzzy goals, also the dependency of implementing time of each activity and

A WeightedLogic Representation of CRevising Ordinal Conditional Functions Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Safia Laaziz; Younes Zeboudj; Salem Benferhat; Faiza Haned KhellafThe problem of belief change is considered as a major issue in managing the dynamics of an information system. It consists in modifying an uncertainty distribution, representing agents’ beliefs, in the light of a new information. In this paper, we focus on the socalled multiple iterated belief revision or Crevision, proposed for conditioning or revising uncertain distributions under uncertain inputs

A New Method for Ranking Interval Type2 Fuzzy Numbers Based on Mellin Transform Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Yanbing Gong; Lin Xiang; Shuxin Yang; Hailiang MaInterval type2 fuzzy sets provide us with additional degrees of freedom to represent the uncertainty and the fuzziness of the real word than traditional type1 fuzzy sets. Interval type2 fuzzy numbers ranking has an important role in the decision making analysis. In this paper, the probatilistic mean value and variance of interval type2 fuzzy numbers are proposed based on the Mellin transform for

Covering Problem for Solutions of MaxArchimedean Bipolar Fuzzy Relation Equations Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Vijay Lakshmi Tiwari; Antika ThaparThis paper discusses the resolution of maxArchimedean bipolar fuzzy relation equations. In the literature, many methods have been proposed based on 01 integer programming problem or reduction methods for the optimization with bipolar fuzzy relation equations. A new concept based on the idea of covering and the notions of leading, nonleading variables are introduced in the present paper for finding

Vectorized KernelBased Fuzzy CMeans: a Method to Apply KFCM on Crisp and NonCrisp Numbers Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Hadi Mahdipour HosseinAbad; Mohsen Shabanian; Iman Abaspur KazerouniKernel methods are a class of algorithms for pattern analysis to robust them to noise, overlaps, outliers and also unequal sized clusters. In this paper, kernelbased fuzzy cmeans (KFCM) method is extended to apply KFCM on any crisp and noncrisp input numbers only in a single structure. The proposed vectorized KFCM (VKFM) algorithm maps the input (crisp or noncrisp) features to crisp ones and applies

On the Use of mProbabilityEstimation and Imprecise Probabilities in the Naïve Bayes Classifier Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Javier G. Castellano; Serafín MoralGarcía; Carlos J. Mantas; Joaquín AbellánWithin the field of supervised classification, the naïve Bayes (NB) classifier is a very simple and fast classification method that obtains good results, being even comparable with much more complex models. It has been proved that the NB model is strongly dependent on the estimation of conditional probabilities. In the literature, it had been shown that the classical and Laplace estimations of probabilities

MultiLevel FineScaled Sentiment Sensing with Ambivalence Handling Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Zhaoxia Wang; SengBeng Ho; Erik CambriaSocial media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users’ sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening

Interval Methods in Knowledge Representation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200824
Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA.

A Hybrid Multilingual FuzzyBased Approach to the Sentiment Analysis Problem Using SentiWordNet Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200521
Youness Madani; Mohammed Erritali; Jamaa Bengourram; Francoise SailhanSentiment Analysis or in particular social network analysis (SNA) is a new research area which is increased explosively. This domain has become a very active research issue in data mining and natural language processing. Sentiment analysis (opinion mining) consists in analyzing and extracting emotions, opinions or attitudes from product’s reviews, movie's reviews, etc., and classify them into classes

SelfAdaptive Optimization for Improved Data Sanitization and Restoration Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200521
Geeta S. Navale; Suresh N. MaliNowadays, Data Sanitization is considered as a highly demanded area for solving the issue of privacy preservation in Data mining. Data Sanitization, means that the sensitive rules given by the users with the specific modifications and then releases the modified database so that, the unauthorized users cannot access the sensitive rules. Promisingly, the confidentiality of data is ensured against the

A Lexicographic Approach to Fuzzy Linear Assignment Problems with Different Types of Fuzzy Numbers Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200521
Boris PérezCañedo; Eduardo R. ConcepciónMoralesThe fuzzy linear assignment problem (FLAP) is an extension of the classical linear assignment problem (LAP) to situations in which uncertainty in the cost coefficients is represented by fuzzy numbers. FLAP applications range from the assignment of workers to tasks to multiplecriteria decision analysis in fuzzy environments and many other engineering applications. Most FLAP formulations assume that

Hyperintensional Reasoning Based on Natural Language Knowledge Base Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200521
Marie Duží; Aleš HorákThe success of automated reasoning techniques over large naturallanguage texts heavily relies on a finegrained analysis of natural language assumptions. While there is a common agreement that the analysis should be hyperintensional, most of the automatic reasoning systems are still based on an intensional logic, at the best. In this paper, we introduce the system of reasoning based on a finegrained

Numerical Linear Programming under NonProbabilistic Uncertainty Models — Interval and Fuzzy Sets Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200521
Keivan Shariatmadar; Mark VersteyheThis paper considers a linear optimisation problem under uncertainty with at least one element modelled as a nonprobabilistic uncertainty. The uncertainty is expressed in the coefficient matrices of constraints and/or coefficients of goal function. Previous work converts such problems to classical (linear) optimisation problems and eliminates uncertainty by converting the linear programming under

A New FineKinney Method Based on Clustering Approach Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200521
Cansu Dagsuyu; Murat Oturakci; Esra Sarac EssizIn this study, a new approach to FineKinney risk assessment method is developed in order to overcome the limitations of the conventional method with clustering algorithms. New risk level of classes are attempted to determine with KMeans and Hierarchical clustering algorithms with using two different distance functions which are Euclidean and Manhattan distances. According to the results, KMeans

Interval Methods in Knowledge Representation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 20200521
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