Int. J. Fuzzy Syst. Appl.最新文献

{"title":"An Intuitionistic Fuzzy Approach With Rough Entropy Measure to Detect Outliers in Two Universal Sets","authors":"T. Sangeetha, Geetha Mary Amalanathan","doi":"10.4018/ijfsa.2020070105","DOIUrl":"https://doi.org/10.4018/ijfsa.2020070105","url":null,"abstract":"The process of recognizing patterns, collecting knowledge from massive databases is called data mining. An object which does not obey and deviates from other objects by their characteristics or behavior are known as outliers. Research works carried out so far on outlier detection were focused only on numerical data, categorical data, and in single universal sets. The main goal of this article is to detect outliers significant in two universal sets by applying the intuitionistic fuzzy cut relationship based on membership and non-membership values. The proposed method, weighted density outlier detection, is based on rough entropy, and is employed to detect outliers. Since it is unsupervised, without considering class labels of decision attributes, weighted density values for all conditional attributes and objects are calculated to detect outliers. For experimental analysis, the Iris dataset from the UCI repository is taken to detect outliers, and comparisons have been made with existing algorithms to prove its efficiency.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115146140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Optimized Intuitionistic Fuzzy Associative Memories (OIFAM) to Identify the Complications of Type 2 Diabetes Mellitus (T2DM)","authors":"A. Felix, D. DhivyaA.","doi":"10.4018/ijfsa.2020070102","DOIUrl":"https://doi.org/10.4018/ijfsa.2020070102","url":null,"abstract":"Fuzzy associative memories (FAM) is a recurrent neural network, consisting of two layers. Since points of the fuzzy set are defined in a cube, it maps between cubes. That is, it maps from input fuzzy set into an output fuzzy set. While the input layer is deliberated as the cause infusing agent the output layer influences the requisite effect. It is a powerful technique to analyze the cause and effect of any problem. Determining the most influential factors in the cause and effect group of any problem is a challenging task. To quench such a task, this present study constructs an optimized intuitionistic fuzzy associative memory using an intuitionistic fuzzy set and a variance of fitness formula. To check the validity of the proposed model, Type 2 diabetes mellitus is taken for diagnosing the early complications of T2DM patients from the risk factors.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121994314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fuzzy Decision-Making System for the Impact of Pesticides Applied in Agricultural Fields on Human Health","authors":"S. Karthik, S. K. Dash, N. Punithavelan","doi":"10.4018/ijfsa.2020070103","DOIUrl":"https://doi.org/10.4018/ijfsa.2020070103","url":null,"abstract":"Farmers are widely applying chemical pesticides to the agricultural lands to kill weeds to reduce crop losses and to prevent diseases created by insects. By applying pesticides to the lands, typically have greater agricultural yield. As pesticides have toxic ingredients, they can create so many health problems to humans and will degrade the environment gradually. Since each pesticide is linked to some health hazards when the composition of the pesticides exceeds its limits, uncertainty arises in determining the human health hazards. Hence, fuzzy logic-based decision-making model is designed to diagnose the human health hazards. In the model, the linguistic terms are used to represent the association between pesticides and human health hazards with the aid of chemists and physicians. Fuzzy numbers are used to represent the values for the linguistic terms. Therefore, the intent of the paper is to analyze the human health hazards induced by applying different pesticides in the agricultural lands through the proposed fuzzy decision-making system.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123149222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approaches for Measurement System Analysis Considering Randomness and Fuzziness","authors":"Liang-Hsuan Chen, Chia-Jung Chang","doi":"10.4018/ijfsa.2020040105","DOIUrl":"https://doi.org/10.4018/ijfsa.2020040105","url":null,"abstract":"For some quality inspection practices, subjective judgements based on the inspectors' experience and knowledge, such as visual inspection, may be required for some particular quality characteristics. This kind of measurement system, including its associated randomness and fuzziness, should be assessed by Measurement system analysis (MSA) before its application. For such purpose, this article represents observations with randomness and fuzziness from MSAs as fuzzy random variables, and then two pairs of descriptive parameters, i.e., expected value and variance, are derived. Then, the relationship of the total sum of squares of factors is proven to hold, so that fuzzy analysis of variance (FANOVA) in terms of gauge repeatability and reproducibility can be developed. The proposed approach has the advantage that FANOVA is developed based on the relationship of the total sum of squares of factors, considering randomness and fuzziness. A real case in the semiconductor packaging industry is used to demonstrate the applicability of the proposed approaches to MSA.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128030874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Logarithmic Entropy Measures for Fuzzy Rough Set and their Application in Decision Making Problem","authors":"Omdutt Sharma, Priti Gupta","doi":"10.4018/ijfsa.2020040104","DOIUrl":"https://doi.org/10.4018/ijfsa.2020040104","url":null,"abstract":"Decision-making is a critical problem in various circumstances where some vagueness and ambiguity is found in information. To handle these types of problems, entropy is an important measure of information theory which is exploited to evaluate the uncertain degree of any data. There are two methodologies to determine the entropy, one is probabilistic in nature and other is non-probabilistic. It is shown that for every probabilistic measure there is a corresponding non-probabilistic measure. In this article, some logarithmic non-probabilistic entropy measures have been proposed for the fuzzy rough set corresponding to existing probabilistic entropy measures. The proposed measures are employed in a decision-making problem, which is related to the agriculture. Finally, these proposed measures are compared with the existing trigonometric entropy measures for fuzzy rough sets.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"41 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131909930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rule Extraction From Neuro-fuzzy System for Classification Using Feature Weights: Neuro-Fuzzy System for Classification","authors":"Heisnam Rohen Singh, S. Biswas","doi":"10.4018/ijfsa.2020040103","DOIUrl":"https://doi.org/10.4018/ijfsa.2020040103","url":null,"abstract":"Recent trends in data mining and machine learning focus on knowledge extraction and explanation, to make crucial decisions from data, but data is virtually enormous in size and mostly associated with noise. Neuro-fuzzy systems are most suitable for representing knowledge in a data-driven environment. Many neuro-fuzzy systems were proposed for feature selection and classification; however, they focus on quantitative (accuracy) than qualitative (transparency). Such neuro-fuzzy systems for feature selection and classification include Enhance Neuro-Fuzzy (ENF) and Adaptive Dynamic Clustering Neuro-Fuzzy (ADCNF). Here a neuro-fuzzy system is proposed for feature selection and classification with improved accuracy and transparency. The novelty of the proposed system lies in determining a significant number of linguistic features for each input and in suggesting a compelling order of classification rules using the importance of input feature and the certainty of the rules. The performance of the proposed system is tested with 8 benchmark datasets. 10-fold cross-validation is used to compare the accuracy of the systems. Other performance measures such as false positive rate, precision, recall, f-measure, Matthews correlation coefficient and Nauck's index are also used for comparing the systems. It is observed from the experimental results that the proposed system is superior to the existing neuro-fuzzy systems.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130410223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment","authors":"Ranjan Kumar, S. Jha, Ramayan Singh","doi":"10.4018/ijfsa.2020040106","DOIUrl":"https://doi.org/10.4018/ijfsa.2020040106","url":null,"abstract":"Theauthorspresentanewalgorithmforsolvingtheshortestpathproblem(SPP)inamixedfuzzy environment.Withthisalgorithm,theauthorscansolvetheproblemswithdifferentsetsoffuzzy numberse.g.,normal,trapezoidal,triangular,andLR-flatfuzzymembershipfunctions.Moreover,the authorscansolvethefuzzyshortestpathproblem(FSPP)withtwodifferentmembershipfunctions suchasnormalandafuzzymembershipfunctionunderreal-lifesituations.Thetransformationof thefuzzylinearprogramming(FLP)modelintoacrisplinearprogrammingmodelbyusingascore functionisalsoinvestigated.Furthermore,theshortcomingsofsomeexistingmethodsarediscussed andcomparedwiththealgorithm.Theobjectiveoftheproposedmethodistofindthefuzzyshortest path(FSP)forthegivennetwork;however,thisisalsocapableofpredictingthefuzzyshortestpath length(FSPL)andcrispshortestpathlength(CSPL).Finally,somenumericalexperimentsaregiven toshowtheeffectivenessandrobustnessofthenewmodel.Numericalresultsshowthatthismethod issuperiortotheexistingmethods. KEywoRDS Fuzzy Linear Programming, Normal Fuzzy Number, Score Function, Shortest Path Problem","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"59 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116433703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling","authors":"Sharifa Rajab","doi":"10.4018/ijfsa.2020040102","DOIUrl":"https://doi.org/10.4018/ijfsa.2020040102","url":null,"abstract":"Neuro-fuzzysystemsbasedonafuzzymodelproposedbyTakagi,SugenoandKangknownasthe TSK fuzzymodelprovideapowerfulmethod formodellinguncertainandhighlycomplexnonlinearsystems.TheinitialfuzzyrulebaseinTSKneuro-fuzzysystemsisusuallyobtainedusing datadrivenapproaches.Thisprocessinducesredundancyintothesystembyaddingredundantfuzzy rulesandfuzzysets.Thisincreasescomplexitywhichadverselyaffectsgeneralizationcapabilityand transparencyofthefuzzymodelbeingdesigned.Inthisarticle,theauthorsexplorethepotentialof TSKfuzzymodelling indevelopingcomparatively interpretableneuro-fuzzysystemswithbetter generalizationcapabilityintermsofhigherapproximationaccuracy.Theapproachisbasedonthree phases,thefirstphasedealswithautomaticdatadrivenrulebaseinductionfollowedbyrulebase simplificationphase.Rulebasesimplificationusessimilarityanalysistoremovesimilarfuzzysets andresultingredundantfuzzyrulesfromtherulebase,therebysimplifyingtheneuro-fuzzymodel. Duringthethirdphase,theparametersofmembershipfunctionsarefine-tunedusingaconstrained hybridlearningtechnique.Thelearningprocessisconstrainedwhichpreventsuncheckedupdatesto theparameterssothatahighlycomplexrulebasedoesnotemergeattheendofmodeloptimization phase.Anempiricalinvestigationofthismethodologyisdonebyapplicationofthisapproachtotwo well-knownnon-linearbenchmark forecastingproblemsanda real-world stockprice forecasting problem.The results indicate that rulebase simplificationusinga similarity analysis effectively removesredundancyfromthesystemwhichimprovesinterpretability.Theremovalofredundancy alsoincreasedthegeneralizationcapabilityofthesystemmeasuredintermsofincreasedforecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated betteraccuracy-interpretabilitytradeoffascomparedtotwowell-knownTSKneuro-fuzzymodels forfunctionapproximation. rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116398572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems","authors":"N. Chiadamrong, Noppasorn Sutthibutr","doi":"10.4018/ijfsa.2020040101","DOIUrl":"https://doi.org/10.4018/ijfsa.2020040101","url":null,"abstract":"This study uses an integrated optimization method by applying a weighted additive multiple objective linear model with Possibilistic Linear Programming (PLP) to fuzzy Aggregate Production Planning (APP) problems under an uncertain environment. The uncertainty conditions include uncertainties of operating times and costs, customer demand, labor level, as well as machine capacity. The aim of this study is to minimize total costs of the plan that consist of the production cost and costs of changes in labor level. The proposed hybrid approach minimizes the most possible value of the imprecise total costs, maximizes the possibility of obtaining lower total costs, and minimizes the risk of obtaining higher total costs from PLP as multiple objectives for the fuzzy multiple objective linear model optimization. The outcome of the proposed approach shows that the solution is closer to the ideal solution obtained from Linear Programming than a typical solution obtained from PLP. There is also a higher overall satisfaction value.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122521389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a New Approach to Solve Solid Assignment Problems Under Intuitionistic Fuzzy Environment","authors":"P. Senthil Kumar","doi":"10.4018/ijfsa.2020010101","DOIUrl":"https://doi.org/10.4018/ijfsa.2020010101","url":null,"abstract":"When people solve real-life SAP they tend to face the uncertainty state as well as hesitation due to many uncontrollable factors. To deal with uncertainty and hesitation many authors have suggested the intuitionistic fuzzy representation for the data. In this article, the author tried to categorise the SAP under uncertain environment. He formulates the IFSAP and utilizes the TIFN to deal with uncertainty and hesitation. The SAP has uncertainty and hesitation in cost/time/profit/production is known as FIFSAP. The PSK (P. Senthil Kumar) method for finding an optimal solution for FIFAP is extended to solve the FIFSAP and the optimal objective value of FIFSAP is obtained in terms of TIFN. The main advantage of this method is that the optimal solution/assignment of FIFSAP is obtained without using the Hungarian method and intuitionistic fuzzy reduction method. Moreover, the proposed method is computationally very simple and easy to understand. The numerical example is presented to demonstrate computing procedure. The results affirm efficiency of the proposed method.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123706239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}