Journal articles: 'Fuzzy synthetic decision'

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Liang, Ru, and Heap-Yih Chong. "A hybrid group decision model for green supplier selection: a case study of megaprojects." Engineering, Construction and Architectural Management 26, no. 8 (September 16, 2019): 1712–34. http://dx.doi.org/10.1108/ecam-10-2018-0462.

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Purpose Green supplier selection is one of the crucial activities in green supply chain management. However, limited studies have addressed the vagueness and complexities during the selection process, particularly in multi-criterion decision-making (MCDM) circumstances. Hence, the purpose of this paper is to develop a group decision model using a modified fuzzy MCDM approach for green supplier selection under a complex situation. Design/methodology/approach The proposed study develops a framework for sorting decisions in green supplier selection by using the hesitant fuzzy qualitative flexible multiple attributes method (QUALIFLEX). The synthetic consistent or inconsistent indexes were used to calculate all alternative suppliers by normalizing the hesitant fuzzy decision matrix. Findings The proposed framework has been successfully applied and illustrated in the case example of CB02 contract section in Hong Kong–Zhuhai–Macau Bridge project. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The synthetic (in)consistent indexes are able to calculate all alternative suppliers by normalizing the hesitant fuzzy decision matrix. Originality/value The research contributes to improving accuracy and reliability decision-making processes for green supplier selection, especially under vagueness and complex situations in megaprojects.

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Wang, Jian Wei, and Jian Ming Zhang. "A Synthetic Evaluation Method of Cell Formation Schemes." Applied Mechanics and Materials 278-280 (January 2013): 2238–43. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2238.

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A decision support system based on scenario for multi-attribute selection of cell formation schemes is developed. The system combines fuzzy set theory and group decision with the AHP to decrease the influence of decision makers’ subjective preferences and control the uncertain and imprecise variations during evaluation process. The importance weights of different criteria and the ratings of various alternatives under different criteria are evaluated in linguistic terms represented by fuzzy numbers. The intangible criteria and criteria weights are determined by group decision which can integrate all decision makers’ subjective opinions based on different scenarios. Besides, fuzzy value of each of the alternatives is computed by making use of standard fuzzy arithmetic. The degree of confidence and risk index are also joined, so that decision makers can adjust them to match real context. Finally, a case of individual selection about cell formation is given, and the results demonstrate the proposed approach is both effective and robust.

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Cheng, J., and Z. Y. Yang. "Fuzzy synthetic decision-making system in ferrographic analysis." Wear 222, no. 1 (November 1998): 1–20. http://dx.doi.org/10.1016/s0043-1648(98)00271-3.

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Zhang, Yin Dong, and Yang Liu. "The Skimmer Selection Based on Fuzzy Decision in Response to Oil Spill." Applied Mechanics and Materials 597 (July 2014): 472–75. http://dx.doi.org/10.4028/www.scientific.net/amm.597.472.

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The decision method based on Fuzzy Synthetic Evaluation is presented to achieve skimmer selection in oil spill response. Firstly, the evaluation index system of skimmer is determined. Secondly, the fuzzy set theory is introduced to achieve quantification of skimmer qualitative indexes and the evaluation matrix of skimmer is established by expert investigation. Then the alternative skimmers are evaluated by the method of Fuzzy Synthetic Evaluation, and the optimal selection of skimmer can be obtained.

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Olabanji, Olayinka Mohammed, and Khumbulani Mpofu. "Fusing Multi-Attribute Decision Models for Decision Making to Achieve Optimal Product Design." Foundations of Computing and Decision Sciences 45, no. 4 (December 1, 2020): 305–37. http://dx.doi.org/10.2478/fcds-2020-0016.

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Abstract Manufacturers need to select the best design from alternative design concepts in order to meet up with the demand of customers and have a larger share of the competitive market that is flooded with multifarious designs. Evaluation of conceptual design alternatives can be modelled as a Multi-Criteria Decision Making (MCDM) process because it includes conflicting design features with different sub features. Hybridization of Multi Attribute Decision Making (MADM) models has been applied in various field of management, science and engineering in order to have a robust decision-making process but the extension of these hybridized MADM models to decision making in engineering design still requires attention. In this article, an integrated MADM model comprising of Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Pugh Matrix and Fuzzy VIKOR was developed and applied to evaluate conceptual designs of liquid spraying machine. The fuzzy AHP was used to determine weights of the design features and sub features by virtue of its fuzzified comparison matrix and synthetic extent evaluation. The fuzzy Pugh matrix provides a methodical structure for determining performance using all the design alternatives as basis and obtaining aggregates for the designs using the weights of the sub features. The fuzzy VIKOR generates the decision matrix from the aggregates of the fuzzified Pugh matrices and determine the best design concept from the defuzzified performance index. At the end, the optimal design concept is determined for the liquid spraying machine.

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Yang, Xiao Xia, Xiao Ping Zhang, and Min Yan. "Fuzzy Comprehensive Evaluation of the Garment CAD." Advanced Materials Research 267 (June 2011): 843–47. http://dx.doi.org/10.4028/www.scientific.net/amr.267.843.

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The fuzzy synthesis judgment is a very effective multi-factor decision method to make the comprehensive appraisal to the things affected by many factors. Affected by many uncertain factors, the garment CAD is suited with the fuzzy synthesis judgment theory to carry on the quality synthetic evaluation. Based on the fuzzy comprehensive evaluation theory and step, it is established that the second-level fuzzy synthesis judgment model of garment CAD, by carrying on the questionnaire survey to the educational circles, the garment CAD software developer, the enterprise's experts, and the rich experienced personnel . The judgment factor and the factor weight evaluated by the experts also can be regarded as the clothing enterprises’ reference when they select and purchase the garment CAD.

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Deng, Pu Jun, Jian Dong Lu, and Wei Fang. "Research on Applying Fuzzy Theory into Printer Quality Evaluation." Applied Mechanics and Materials 469 (November 2013): 362–67. http://dx.doi.org/10.4028/www.scientific.net/amm.469.362.

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From printing technology’s perspective, printing quality is determined by the comprehensive effects of all the printing product appearance features. The factors which dominate to comprehensive effects of all the printing product appearance features include definition of image, tone rendering, color rendering, uneven, and gloss, etc. In the print quality evaluation, this factor is affected by many other factors. Therefore, printing quality evaluations are indeterminate. Most of printing quality evaluations belongs to fuzzy theory, which brings a lot of troubles to the printing quality evaluation. The paper mainly research that the synthesis decision-making method in fuzzy mathematics theory is used to tone reproduction of printing quality, and analyze the feasibility of applying Fuzzy Synthetic Evaluation Model in printing quality evaluation. The synthesis decision-making method is used to determine the tone indicator system of printing, establish Fuzzy Evaluation Mode, and analyze the influencing factors. Through the evaluation of numerous experts, the weight will be offered for the specified factors. On the basis of weights, printing quality can be evaluated by determining the membership function of all the factors and calculating the score through fuzzy algorithm. The paper studies have shown that the synthesis decision-making method in fuzzy mathematics theory can be used to solve the printer quality evaluation problem and is valid for evaluating the results in actual application.

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Lee, Yueh-Hua, and Hsiao-Chien Chang. "The application of fuzzy synthetic decision approach to brand evaluation." Journal of Statistics and Management Systems 8, no. 2 (January 2005): 343–53. http://dx.doi.org/10.1080/09720510.2005.10701163.

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Hsieh, Ching-Sen, Yu-Wen Chen, Chih-Hung Wu, and Tao Huang. "Characteristics of fuzzy synthetic decision methods for measuring student achievement." Quality & Quantity 46, no. 2 (November 27, 2010): 523–43. http://dx.doi.org/10.1007/s11135-010-9384-y.

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Zhang, Yan, and Oh Kyoung Kwon. "Location Decision of City Logistics Facility Based on Fuzzy Synthetic Evaluation." Applied Mechanics and Materials 33 (October 2010): 351–55. http://dx.doi.org/10.4028/www.scientific.net/amm.33.351.

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The concept of city logistics facilities is to help establish more efficient urban logistics systems for both private companies and society, reduce the total social and environmental costs of urban freight transport and improve the urban traffic conditions on the road network. This study adopts a fuzzy synthetic evaluation method to determine the optimal location of city logistics facilities.

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Gan, Xu Sheng, Jing Shun Duanmu, and Jian Guo Gao. "Fuzzy Synthetic Evaluation for Aviation Maintenance Support Ability." Applied Mechanics and Materials 307 (February 2013): 429–32. http://dx.doi.org/10.4028/www.scientific.net/amm.307.429.

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To improve the evaluation level for aviation maintenance support system and provide a good decision-making for aircraft maintenance support, a quantitative evaluation method for aviation maintenance support ability is proposed. first the evaluation index system for aviation maintenance support ability is given on the basis of factor analysis for aviation maintenance support system, and then using Analytic Hierarchy Process to determine the weight of each influence factor, finally, based on fuzzy mathematics method, the three-level fuzzy synthetic evaluation model is established. The actual example validates is feasibility and effectiveness.

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Guan, Xin, Guidong Sun, Xiao Yi, and Jing Zhao. "Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/7436054.

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Due to the superiority in expressing the uncertain and vague information, the hesitant fuzzy set (HFS) is regarded as an important tool to deal with multiattribute decision-making (MADM) problems. Quantitative and qualitative fuzzy measures have been proposed to solve such problems from different points. However, most of the existing information measures for HFSs are related to such fuzzy measures as distance, similarity, entropy, and correlation coefficients. The grey relational analysis is omitted. Besides, the existing grey relational analysis for HFSs only considers the range or distance between HFSs data which is only a partial measure of the HFSs. Therefore, in this paper, we improve the grey relational analysis for HFSs and explore a novel slope grey relational degree by considering another factor of HFSs data: the slope. Further, we combine both the distance and slope factors of HFSs data to construct a synthetic grey relational degree that describes the closeness and variation tendency of HFSs simultaneously, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs MADM problems. Finally, combining with two practical MADM examples about energy policy selection and multisensor target recognition, we obtain the most desirable decision results. Compared with the previous methods, the validity, comprehensiveness, and discrimination of the proposed synthetic grey relational degree for HFSs are demonstrated in detail.

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Liu, Yutao, Hao Hu, and Di Zhang. "A RULE BASED FUZZY SYNTHETIC EVALUATION METHOD FOR RISK ASSESSMENT IN PIPELINE TRANSPORT." TRANSPORT 29, no. 4 (December 16, 2014): 355–62. http://dx.doi.org/10.3846/16484142.2014.981858.

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Integrity Management Program (IMP) for oil and gas pipeline system demands a robust risk assessment method, which needs fully utilize the expert knowledge on the empirical data and the ambiguous cause(s) – effect(s) failure mechanism and then keep a good balance between precision and practicality. A new method, Rule Based Fuzzy Synthetic Evaluation (RB-FSE) which combines Fuzzy Synthetic Evaluation (FSE) with Fuzzy Logic (FLo) is proposed in this paper. It is applied to the pipeline risk assessment for Third-Party Damage (TPD). The proposed method is compared with scoring-type method in a case study. Results indicate that the relative assessment values from RB-FSE model could better support risk-ranking and decision-making in the IMP.

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Leśniak, Agnieszka, Daniel Kubek, Edyta Plebankiewicz, Krzysztof Zima, and Stanisław Belniak. "Fuzzy AHP Application for Supporting Contractors’ Bidding Decision." Symmetry 10, no. 11 (November 16, 2018): 642. http://dx.doi.org/10.3390/sym10110642.

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This paper proposes the author’s model based on the Fuzzy Analytic Hierarchy Process (FAHP) to improve the efficiency of contractor bidding decisions. The essence of the AHP method is to make pairwise comparisons of available options against all evaluation criteria. The results of these comparisons are recorded in a square matrix in which symmetrical elements are reciprocal. In the expert opinion, a 9-step, bipolar verbal scale was used so that the symmetry of the response was maintained. For contractors from countries where the tendering system is commonly used, the choice of the right tender in which to participate influences their image, financial condition, and their aspiration to succeed. The bid/no bid decision depends on numerous factors associated with the company itself, the environment, and the project concerning the tender. When facing tough competition, contractors search for a solution which increases their chances of winning the tender. The proposed model was based on factors selected by Polish contractors. The original element of the model involves 4 original criteria and 15 sub-criteria for the assessment of investment decision projects to the selection of the most advantageous contract, i.e., the contractor’s participation in the bid. For verbal evaluations describing the criteria, symmetric triangular fuzzy numbers were assigned. The authors performed an extended analysis method combined with FAHP in the model. Fuzzy evaluations underwent elaborate analysis, the aim of which was to specify the synthetic priority weights for each criterion. As a result of the application of the method, to prove that the model works, an example from the Polish construction market was presented in which a bid/no bid decision about four possible tenders was to be taken. Despite the considered example applying to Polish conditions, the proposed model can be used also in other countries. The authors’ rationale is to produce new and more flexible methodologies in order to realistically model a variety of concrete decision problems.

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Nayebpur, Hamid, and Mohsen Nazem Bokaei. "Portfolio selection with fuzzy synthetic evaluation and genetic algorithm." Engineering Computations 34, no. 7 (October 2, 2017): 2422–34. http://dx.doi.org/10.1108/ec-03-2017-0084.

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Purpose The purpose of this paper is to present a new technique to portfolio selection using a genetic algorithm (GA) and fuzzy synthetic evaluation (FSE). Portfolio selection is a multi-objective/criteria decision-making problem in financial management. Design/methodology/approach The proposed approach solves the problem in two stages. In the first stage, by using a GA and FSE, the weight of criteria will be calculated. Euclidean distance between the computed overall performance evaluation and the surveyed overall performance evaluation is used to determine the weight of criteria. In the second stage, by using a GA and FSE, portfolios will be prioritized. A multi-objective GA is used to determine return and risk in the efficient frontier. A decision making approach is based on FSE to select the best portfolio from among the solutions obtained by a multi objective GA. Findings The main advantage of the proposed approach is to help an investor to find a portfolio which has best performance, and portfolio selection does not rely on expert knowledge. Originality/value The value of the paper is in it using a new approach to determine the weight of criteria and portfolio selection. It surveys firms’ performance in the stock market, based on which the weight of criteria will be determined and portfolios will be prioritized.

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Fang Hao, 方浩, 李艾华 Li Aihua, 王涛 Wang Tao, and 常红伟 Chang Hongwei. "Synthetic Evaluation Method of Hyperspectral Camouflage Effect Based on Intuitionistic Fuzzy Decision." Laser & Optoelectronics Progress 56, no. 5 (2019): 051003. http://dx.doi.org/10.3788/lop56.051003.

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Luo, Li Zhe, Xue Hong Yang, Wen Zhen Yu, and Shao Hua Pan. "Diversion System Scenarios Optimization Considering Indicators Correlation." Advanced Materials Research 1065-1069 (December 2014): 2554–60. http://dx.doi.org/10.4028/www.scientific.net/amr.1065-1069.2554.

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The decision-making indicators of hydropower engineering diversion scenario are always related with diversion risk, and for determining the optimal scenario, correlation analysis and decoupling for indicators is the key problem. Using k-additive fuzzy measure to characterize and decouple the relevance of indicators on the base of the analysis and quantization of indicators, and determining the weights of indicators according to maximum fuzzy measure entropy principle, finally the synthetic appraisal value for diversion scenarios can be calculated with choquet integral to rank and select optimal scenario. The case study shows that the decision-making method is effective for characterizing and decoupling the relevance of the indicators and enhancing the decision-making veracity to provide an effective method for the diversion scenario decision of hydropower engineering.

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Revathy, S., B. Parvathavarthini, and S. Shiny Caroline. "Decision Theory, an Unprecedented Validation Scheme for Rough-Fuzzy Clustering." International Journal on Artificial Intelligence Tools 25, no. 02 (April 2016): 1650003. http://dx.doi.org/10.1142/s0218213016500032.

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Cluster validation is an essential technique in all cluster applications. Several validation methods measure the accuracy of cluster structure. Typical methods are geometric, where only distance and membership form the core of validation. Yao's decision theory is a novel approach for cluster validation, which evolved loss calculations and probabilistic based measure for determining the cluster quality. Conventional rough set algorithms have utilized this validity measure. This paper propagates decision theory, an unprecedented validation scheme for Rough-Fuzzy clustering by resolving loss and probability calculations to predict the risk measure in clustering techniques. Experiments with synthetic and UCI datasets have been performed, proven to deduce the optimal number of clusters overcoming the downsides of traditional validation frameworks. The proposed index can also be applied to other clustering algorithms and extends the usefulness in business oriented data mining.

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Hu, Wei, and Ou Li. "Research on Fault Diagnosis Strategy of Belt Conveyor." Applied Mechanics and Materials 312 (February 2013): 607–10. http://dx.doi.org/10.4028/www.scientific.net/amm.312.607.

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In view of the inadequacy of the fault diagnosis of the belt conveyor, the paper takes advantage of the application of fuzzy information fusion technology to fault diagnosis, based on the fuzzy set theory, a fault diagnosis method based on Multi-sensor fuzzy information fusion is developed. The obtain information of many sensors will fuzzy, again its fusion based on the synthetic operation and decision-making rules of the fusion center, in order to gain the accurate state estimation and judgment of belt conveyor. The experimental result indicates that the credibility of diagnosis is improved markedly and the uncertainty is reduced significantly after the multi-sensor fuzzy information fusion, the accurate diagnosis to belt conveyor is realized.

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Liu, Han Bing, Yi Ming Xiang, Hui Wang, and Yan Yi Sun. "Relative Distance Measure Model for the Fuzzy Synthetic Evaluation of Subgrade Stability in Seasonal Frozen Area." Applied Mechanics and Materials 178-181 (May 2012): 1213–17. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1213.

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Based on the fuzziness and uncertainty of the subgrade stability in seasonal frozen area, the relative distance measure model with evaluation indexes and weights in the form of interval numbers is presented for the fuzzy synthetic evaluation of the subgrade stability. Firstly, the relative distance measure of each single index between the evaluated subgrade stability and the grading standards is defined. Then, the fuzzy synthetic evaluation model, which considers the functionality and proportionality of evaluation indexes, is established to calculate the comprehensive relative distance measure by using the Monte Carlo simulation method and the sequential relation analysis. Finally, a new decision index of the comprehensive relative distance measure is defined considering the concept of structural reliability, and the stability grade of seasonal frost soil subgrade can be determined by the minimum decision index from the corresponding grading standards. A practical example is given to demonstrate the feasibility and practicability of the proposed model.

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Tian, Fenglan, Zhongzhao Jing, Huan Zhao, Enze Zhang, and Jiefeng Liu. "A Synthetic Condition Assessment Model for Power Transformers Using the Fuzzy Evidence Fusion Method." Energies 12, no. 5 (March 5, 2019): 857. http://dx.doi.org/10.3390/en12050857.

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Condition-based maintenance decision-making of transformers is essential to electric enterprises for avoiding financial losses. However, precise transformer condition assessment was tough to accomplish because of the negligence of the influence of bushing and accessories, the difficulty of fuzzy grade division, and the lack of reasonable fuzzy evidence fusion method. To solve these problems, a transformer assessing model was proposed in the paper. At first, an index assessing system, considering the main body, the bushing and the accessories components, was established on the basis of components division of transformers. Then, a Cauchy membership function was employed for fuzzy grades division. Finally, a fuzzy evidence fusion method was represented to handle the fuzzy evidences fusion processes. Case studies and the comparison analysis with other methods were performed to prove the effectiveness of this model. The research results confirm that the proposed model could be recommendation for condition based maintenance of power transformers for electric enterprises.

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Abdul-Rahman, Hamzah, Chen Wang, and Yee Lin Lee. "DESIGN AND PILOT RUN OF FUZZY SYNTHETIC MODEL (FSM) FOR RISK EVALUATION IN CIVIL ENGINEERING." Journal of Civil Engineering and Management 19, no. 2 (April 18, 2013): 217–38. http://dx.doi.org/10.3846/13923730.2012.743926.

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Most of the current construction risk assessment tools deliver unsatisfactory results because the prerequisite for their effective applications rely on the availability of high quality data especially during the early stage of a project. Unfortunately, such data are limited, ambiguous or even not exist due to the great uncertainty inherent in construction projects. Based on Fuzzy Synthetic Analysis (FSA), a model development team was formed among construction engineers, IT professionals, and Mathematicians in developing a holistic risk assessment model to estimate the construction risks especially for the situations with incomplete data and vague environments. Through qualitative scales defined by triangular fuzzy numbers used in pairwise comparisons to capture the vagueness in the linguistic variables, a risk assessment model using Analytic Hierarchy Process (AHP) was developed. The Pilot Run revealed the developed Fuzzy Synthetic Model (FSM) could accelerate the decision-making process and provide optimal allocation of project resources to mitigate possible risks detrimental to the success of a project in terms of time, cost, and quality.

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Chaudhuri, Arindam. "Fuzzy Rough Support Vector Machine for Data Classification." International Journal of Fuzzy System Applications 5, no. 2 (April 2016): 26–53. http://dx.doi.org/10.4018/ijfsa.2016040103.

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In this paper, classification task is performed by FRSVM. It is variant of FSVM and MFSVM. Fuzzy rough set takes care of sensitiveness of noisy samples and handles impreciseness. The membership function is developed as function of cener and radius of each class in feature space. It plays an important role towards sampling the decision surface. The training samples are either linear or nonlinear separable. In nonlinear training samples, input space is mapped into high dimensional feature space to compute separating surface. The different input points make unique contributions to decision surface. The performance of the classifier is assessed in terms of the number of support vectors. The effect of variability in prediction and generalization of FRSVM is examined with respect to values of C. It effectively resolves imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. Experimental results on both synthetic and real datasets support that FRSVM achieves superior performance in reducing outliers' effects than existing SVMs.

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Haddache, Mohamed, Allel Hadjali, and Hamid Azzoune. "Skyline refinement exploiting fuzzy formal concept analysis." International Journal of Intelligent Computing and Cybernetics 14, no. 3 (April 29, 2021): 333–62. http://dx.doi.org/10.1108/ijicc-11-2020-0181.

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PurposeThe study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.Design/methodology/approachThe problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.FindingsAn appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.Research limitations/implicationsThe authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implicationsThe tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.Social implicationsThis work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.Originality/valueThis study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.

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Wang, Chunyan, Aigong Xu, Chao Li, and Xuemei Zhao. "INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 385–91. http://dx.doi.org/10.5194/isprsarchives-xli-b7-385-2016.

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Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

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Wang, Chunyan, Aigong Xu, Chao Li, and Xuemei Zhao. "INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 385–91. http://dx.doi.org/10.5194/isprs-archives-xli-b7-385-2016.

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Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

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Chowdhury, Shakhawat, and Pascale Champagne. "Selecting Water Disinfection Processes using Fuzzy Synthetic Evaluation Technique." Water Quality Research Journal 43, no. 1 (February 1, 2008): 1–10. http://dx.doi.org/10.2166/wqrj.2008.002.

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Abstract Drinking water treatment strategies generally involve treatment processes such as screening, coagulation/flocculation, sedimentation, and filtration/adsorption followed by disinfection. Disinfection approaches include chlorine/chlorine (chloramine), granular activated carbon with post chlorine (chloramine), ozone/chlorine (chloramine), chlorine dioxide/ chlorine (chloramine), chloramine/chloramine, and ultraviolet radiation/chlorine (chloramine). However, comparative evaluation of these disinfection methodologies and their application to a particular source of water is rare. In this study, a framework for multicriteria decision making has been developed. Human health risk, cost, technical feasibility, and disinfection performance have been incorporated as the criteria for evaluation of the disinfection approach. A fuzzy synthetic evaluation technique has been incorporated where fuzzy triangular membership functions were developed to capture the uncertainties of the basic attributes. This paper compares three disinfection approaches: chlorination, granular activated carbon with post chlorination, and chloramination through a multistage hierarchy risk management model in which the analytical hierarchy process has been used to determine the relative importance of various attributes at different hierarchy levels. The evaluation process was found to be sensitive to the assignment of relative importance of the attributes. Chlorination was evaluated as the best disinfection approach in most of the cases.

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Sadiq, Rehan, Tahir Husain, Brian Veitch, and Neil Bose. "Risk-based decision-making for drilling waste discharges using a fuzzy synthetic evaluation technique." Ocean Engineering 31, no. 16 (November 2004): 1929–53. http://dx.doi.org/10.1016/j.oceaneng.2004.05.001.

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Et.al, Sunit Kumar. "An Extension of TODIM with VIKOR approach based on Gini Simpson Index of Diversity under Picture fuzzy framework to Evaluate Opinion Polls." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 3715–30. http://dx.doi.org/10.17762/turcomat.v12i3.1654.

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Cuong and Kreinovich was the first who gives the idea of Picture fuzzy set (PFS), which is an extension of intuitionistic fuzzy set (IFS) by cosidering positive, negative and neutral membership of element. In this paper, we have been worked on new entropy measure of PFS from the probabilistic view point and it’s properties are examined from mathematical point of view. A hybrid aproach is presented with the assistance of TODIM (Portuguese abbreviation for Interactive Multi-Criteria Decision Making) and VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) methods. Further, we applied it to MCDM (multi criterion decision making) problems with picture fuzzy numbers (PFNs), where the information about criteria synthetic weights is partially known and completely unknown and show its existence with the help of some practical cases. After getting the output, we are able to infer that the proposed hybrid approach is comparatively better so as to handle the uncertainty and vulnerabilities for the decision making problems. Based upon these two approaches we can determine the opinion poll of voting outcomes and then, we compare its result with other MCDM approaches that exists in the literature.

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Ji, Xiang Jun, Zan Zhang, and Fa Chao Li. "Comprehensive Evaluation Model Based on Synthetic Importance Measure." Applied Mechanics and Materials 380-384 (August 2013): 987–90. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.987.

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To solve the problem of attributes interdependence and correlation in comprehensive evaluation, the paper proposes to identify the relationships among the attributes from the decision information system based on the covering rough sets. It first shows a metric with the characteristics of fuzzy measure to measure the hidden importance of attributes, which can be seen as a reflection of the attributes correlation. Then the synthetic importance measure is given by incorporating it into the basic importance. Furthermore, the comprehensive valuation model is constructed by taking Choquet integral as an aggregation operator.

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Wu, Yunna, Ting Zhang, and Liqi Yi. "Regional energy internet project investment decision making framework through interval type-2 fuzzy number based Choquet integral fuzzy synthetic model." Applied Soft Computing 111 (November 2021): 107718. http://dx.doi.org/10.1016/j.asoc.2021.107718.

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Zyoud, Shaher H., and Daniela Fuchs-Hanusch. "An Integrated Decision-Making Framework to Appraise Water Losses in Municipal Water Systems." International Journal of Information Technology & Decision Making 19, no. 05 (August 2020): 1293–326. http://dx.doi.org/10.1142/s0219622020500297.

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To mitigate the acute water shortage problems, water utilities are combating to find potential solutions. Water losses management in Water Supply Networks (WSNs) is amongst the prominent solutions. This work intends to develop a decision support framework to diagnose the criticality of WSNs according to an associated Water Loss Risk Index (WLRI) at pipe and zone levels. It utilized the Fuzzy Analytic Hierarchy Process (FAHP) to evaluate the influential factors that contribute to water losses. The Fuzzy Synthetic Evaluation Technique (FSET) has been utilized to assess WLRI at pipe level and Ordered Weighted Averaging (OWA) operator has been used to aggregate the individual WLRI values for each zone. The framework is extended by incorporating Monte Carlo simulation model to generate the final ranking of zones. The outcomes of this simulation showed fair stability in terms of ranking the scrutinized zones. The integration of this framework in water losses management practices and planning policies of water utilities has a large potential in improving water supply services and the performance of WSNs.

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Goyal, Mukta, Alka Tripathi, and Divakar Yadav. "Intuitionistic Group Decision Making to Identify the Status of Student's Knowledge Acquisition in E-Learning Systems." International Journal of Fuzzy System Applications 5, no. 3 (July 2016): 14–29. http://dx.doi.org/10.4018/ijfsa.2016070102.

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Learner's performance evaluation in an E-learning environment is a multi-criteria decision problem and important to personalize the sequence of learning concepts according to their knowledge level. Crisp responses, leads uncertainty in the evaluation process due to successful guesses or choosing a more probable answer. Analysis of learner's response to a complex/subjective questions needs more effort. Moreover, due to uncertainty and imprecise nature of learner, traditional methods are inadequate to assess, how much time he has spent on studying the learning contents and also the number of backtracking he followed. This paper proposes an intuitionistic fuzzy multicriteria decision making that investigates the use of an intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS method) for the evaluation of student in an E-learning environment. Synthetic data are created for the criteria's that affect the student evaluation in E-learning domain and thereafter results are evaluated.

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Olabanji, Olayinka Mohammed, and Khumbulani Mpofu. "Adopting hybridized multicriteria decision model as a decision tool in engineering design." Journal of Engineering, Design and Technology 18, no. 2 (September 20, 2019): 451–79. http://dx.doi.org/10.1108/jedt-06-2019-0150.

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Purpose The purpose of this paper is to determine the suitability of adopting hybridized multicriteria decision-making models as a decision tool in engineering design. This decision tool will assist design engineers and manufacturers to determine a robust design concept before simulation and manufacturing while all the design features and sub features would have been identified during the decision-making process. Design/methodology/approach Fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) are hybridized and applied to obtain optimal design of a reconfigurable assembly fixture (RAF) from a set of alternative design concepts. Design features and sub features associated with the RAF are identified and compared using fuzzified pairwise comparison matrices to obtain weights of their relative importance in the optimal design. The FAHP obtained the fuzzy synthetic extent (FSE) values of the design features and sub features. The FSE values are used as weights of the design features and sub features in generating the decision matrix. FTOPSIS and FTOPSIS based on left and right scores were adopted to predict effects of the weights. Results were obtained for normalized and unnormalized weights of the design features and its effects on the relative closeness coefficients of the design alternatives. Findings The improved performance of the FTOPSIS based on left and right scores is due to the involvement of the left and right scores of weights of the design features in the computation of distances from positive and negative ideal solutions. Embedding the weights of the design features in the normalized decision matrix before estimating the distances of the design concepts from ideal solutions reduces the dependency of the closeness coefficients on the weights of the design features. This also decreases the difference in the final values of the design concepts. In essence, the weights of the design features have an impact in the closeness coefficient. There is reduction in the closeness coefficients of the design concepts due to normalization of the weights of the design features. However, normalizing the weights of the design features did not affect the variations in the final values of the design concept. As the final value of the design concepts can be influenced by the normalized weights of the design features, it can be implied that normalization of weights of the sub features will also affect the decision matrix. The study has been able to proof that hybridizing FAHP and FTOPSIS can produce effective results for decisions on optimal design by the application of FTOPSIS based on left and right scores rather than the general FTOPSIS. Originality/value This research develops a hybridized multicriteria decision-making model for decision-making in engineering design. It presents a detailed extension of hybridized FAHP and FTOPSIS based on left and right scores as a useful tool for considering the relative importance of design features and sub features in optimal design selection.

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Shi, Xiao Long, Gui Jie Liu, Qiang Wang, and Ren Ke Kang. "Study on a Grinding Quality Assessing Method Based on Fuzzy Decision Combined with Analytic Hierarchy Process (AHP)." Key Engineering Materials 359-360 (November 2007): 543–47. http://dx.doi.org/10.4028/www.scientific.net/kem.359-360.543.

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The grinding quality assessing is a complex decision-making process, which must optimize and balance multi-influence factors. A grinding quality assessing method based on fuzzy synthetic evaluation theory combined with analytic hierarchy process (AHP) is presented in this paper, and used to quality assessing for grinding process. The result of analyzing example indicates that this method can be used to estimate grinding quality based on the monitoring result of grinding process and grinding condition, and to assist grinding worker to select optimum grinding parameters for the steady grinding quality.

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Xu, Yelin, Yujie Lu, Albert P. C. Chan, Miroslaw J. Skibniewski, and John F. Y. Yeung. "A COMPUTERIZED RISK EVALUATION MODEL FOR PUBLIC-PRIVATE PARTNERSHIP (PPP) PROJECTS AND ITS APPLICATION." International Journal of Strategic Property Management 16, no. 3 (October 2, 2012): 277–97. http://dx.doi.org/10.3846/1648715x.2012.686928.

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PPP projects usually involve more risks than other traditional procurement models because of their complexity. This paper presents the third stage of a funded study, which aims to develop a practical and computerized risk evaluation model for PPP projects. In the first and second stages, a risk hierarchal structure composed of 17 weighted risk factors is developed to describe risk profiles of PPP projects. The weightings and membership functions for risk factors are established using the Delphi survey technique and Fuzzy Set Theory. The risk evaluation model is then developed using a fuzzy synthetic evaluation approach. In the third stage, an automated decision support tool based on the risk evaluation model is designed for PPP practitioners by using Visual Basic for Application (VBA). The computerized tool can not only assist PPP participants to assess a PPP project's overall risk level for auxiliary investment decision, but can also help practitioners to identify the most risky areas of a PPP project for effective risk response. To demonstrate the applicability of the computerized model, an illustrative case is finally provided.

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Liu, Shihu, Fusheng Yu, and Patrick S. P. Wang. "Granularity Approach for Multi-Criteria Decision Making About Hybrid Evaluation Information." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11 (July 24, 2018): 1859020. http://dx.doi.org/10.1142/s0218001418590206.

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In this study, a new version of TOPSIS method is reconstructed to deal with the problem of multi-criteria decision making. Here, the data representation of all alternatives is varied according to different criteria, such as real number, interval-valued number, set-valued number and intuitionistic fuzzy-valued number, etc. Because the distinguishing ability of each criterion can be reflected by its knowledge granularity, naturally, a knowledge granularity method is constructed to measure the criteria weights. Besides, the approach of how to select the ideal solution is redefined, especially for the case that the content of criterion according to all alternatives is not a totally ordered set anymore. What is more, the decision maker’s personal preference is considered, and the concrete indicator value can be calculated by the convex combination of the distance from possible alternatives to ideal solutions. Finally, the validity of the proposed decision-making algorithm is illustrated by a synthetic example.

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Sun, Guidong, Xin Guan, Xiao Yi, and Zheng Zhou. "Multi-Attribute Decision Making with Interval-Valued Hesitant Fuzzy Information, a Novel Synthetic Grey Relational Degree Method." Informatica 29, no. 3 (January 1, 2018): 517–37. http://dx.doi.org/10.15388/informatica.2018.179.

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Cohen, A., D. Hegg, M. de Michele, Q. Song, and N. Kasabov. "An intelligent controller for automated operation of sequencing batch reactors." Water Science and Technology 47, no. 12 (June 1, 2003): 57–63. http://dx.doi.org/10.2166/wst.2003.0628.

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In this paper the results are presented of original research into the automatic and “intelligent” detection of breakpoints in Dissolved Oxygen (DO) profiles. The research has been based on a large body of data collected from laboratory SBRs operating on synthetic wastewater. Two different approaches were followed to identify the endpoints. The paper analyses and evaluates the results of automatic breakpoint detection on the basis of geometric features in the DO profiles. This was followed by classification of the detected breakpoints using different soft computing techniques based on Neural Network (NN), Fuzzy Neural Network (FuNN) and Evolving Fuzzy Neural Network (EfuNN) software systems for breakpoint classification. A high rate of successful detection and classification was obtained with up to 96% of the decisions made correctly. In order to overcome the limitations of this system to adapt to dynamically changing process conditions, an intelligent control model was developed by a combination between an Evolving Fuzzy Neural Net (EfuNN) combined with a logic decision unit. This system has the ability to “learn on-the-fly” and adjust its response pattern in order to maintain a high rate of successful breakpoint detection under varying changing process conditions. This software system has been successfully embedded on a small programmable controller for integration into larger process control systems for the operation of SBR plants.

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Yang, Kai, and Ming Li Jiao. "A Novel Elastomer Simulated Skin Applied in Measurement of Extravehicular Liquid Cooling Garment." Advanced Materials Research 239-242 (May 2011): 908–11. http://dx.doi.org/10.4028/www.scientific.net/amr.239-242.908.

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A novel thermoplastic elastomer was applied in the measurement of extravehicular liquid cooling garment and the satisfying result in improving the heat dissipating capacity of liquid cooling garment was achieved. Firstly, five kinds of elastomer were selected, which were ethylene-vinyl acetate copolymer (EVA), high temperature vulcanized silicone rubber (HTV), medical used silicon rubber (MSR), natural rubber latex (NRL) and thermoplastic SBS complex (TPR). To evaluate the synthetic performance of these materials, a series of experiments characterizing material’s mechanical and thermodynamic properties was performed, including tensile and compression test, heat property test and so on. Meanwhile, fuzzy mathematic method was employed to get parameters’ weight distribution, and then fuzzy decision-making method was adopted to perform synthetic evaluation, which showed that TPR had satisfying optimization performance and can be used as simulated skin in thermal manikin. Finally, to verify the function of TPR simulated skin, comparative experiments were performed in climate chamber when thermal manikin covered with TPR simulated skin and did not cover with TPR simulated skin. Results show that TPR simulated skin can effectively improve the heat dissipating capacity of liquid cooling garment.

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Wu, Chen, Qi, Jiang, Gao, and Tseng. "Sustainable Development Performance for Small and Medium Enterprises Using a Fuzzy Synthetic Method-DEMATEL." Sustainability 11, no. 15 (July 30, 2019): 4119. http://dx.doi.org/10.3390/su11154119.

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Small and medium enterprises (SMEs) are forced to adhere to sustainable development (SD) regulations and standards. However, SMEs encounter difficulty in assessing their performance due to the lack of an efficient and effective approach to deal with the uncertainties in hierarchical relationships and interrelationships. Moreover, interrelations exist among the proposed attributes that increase the difficulty of the assessment. To overcome these issues, exploratory factor analysis is used to screen out the less important attributes and build a hierarchal structure. The fuzzy synthetic method addresses the hierarchical structure and decision-making, and a trial evaluation laboratory assesses the interrelationships among the attributes by providing a visual interrelationship map. The results indicate that strategic and financial management are the major problems for SMEs. SD relies on enhancing sustainable supply chain performance, sustainable human resources and environmental management. This study contributes by not only filling the information gap for SD for SMEs but also providing a guideline for improvement. The theoretical and managerial implications are discussed.

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Lan *, H., Y. Ding, and J. Hong. "Decision support system for rapid prototyping process selection through integration of fuzzy synthetic evaluation and an expert system." International Journal of Production Research 43, no. 1 (January 2005): 169–94. http://dx.doi.org/10.1080/00207540410001733922.

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Eraslan, Ergün, and Kumru Didem Atalay. "A Comparative Holistic Fuzzy Approach for Evaluation of the Chain Performance of Suppliers." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/109821.

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The competition between the companies in the dynamic market conditions has made the Supply Chain Management (SCM) a more important issue. The companies which have organized their supply chain effectively have obtained more flexibility in their manufacturing processes in addition to delivery of the customer demands. In this study, two different multicriteria decision making algorithms composed of the FAHP and a holistic hybrid method using FTOPSIS were utilized for an electronic company in wholly fuzzy processes. The FAHP is used for determination of the global weights of the factors and the performances of alternative suppliers are evaluated by using both FAHP-based and FAHP-FTOPSIS hybrid methods for synthetic extent values of pairwise comparisons. The sequences of the suppliers differed for the algorithms. The performances of the proposed approaches are quite successful and flexible in a narrow interval. The managerial advantages obtained from the proposed fuzzy algorithms are also analyzed and interpreted.

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Jasiulewicz-Kaczmarek, Małgorzata, Patryk Żywica, and Arkadiusz Gola. "Fuzzy set theory driven maintenance sustainability performance assessment model: a multiple criteria approach." Journal of Intelligent Manufacturing 32, no. 5 (January 27, 2021): 1497–515. http://dx.doi.org/10.1007/s10845-020-01734-3.

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AbstractDuring the last five decades, manufacturing has radically changed not only due to the technology development but also because of the new market and environmental requirements. Nowadays, companies are focused not only on cost-reduction and effectiveness or realized processes but also on reducing the negative impact on natural and social environment. Therefore, the maintenance is also transforming its role in order to better support value creation, both contributing to the economic dimension as well as extending its care for the environmental and social aspects. The paper presents a new method to solve the problem of the initial maintenance indicator merge into a new synthetic index that allows measuring the level of maintenance sustainability. The proposed approach allows to look at the process of combining indicators from a different perspective (i.e. through interactions between criteria) to help decision-makers in improving economic, social and environmental results of maintenance system. To justify effectiveness of proposed approach, it was applied to Composite Maintenance Sustainability Indicator, which was therefore generalized and expanded, so that it could be used in decision support system. The included case study shows the real benefit of using the proposed approach to analyse the actual results of maintenance system from sustainability point of view and forecasting future actions.

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Christoforou, Andreas, and Andreas S. Andreou. "Investigating Cloud Adoption Using Influence Diagrams as a Decision Support Model." International Journal on Artificial Intelligence Tools 24, no. 06 (December 2015): 1560005. http://dx.doi.org/10.1142/s0218213015600052.

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The fact that Cloud Computing is steadily becoming one of the most significant fields of Information and Communication Technology (ICT) has led many organizations to consider the benefits of migrating their business operations to the Cloud. Decision makers are facing strong challenges when assessing the feasibility of the adoption of Cloud Computing for their organizations. Cloud adoption is a multi-level decision which is influenced by a number of intertwined factors and concerns thus characterizing it as a complex and difficult to model real-world problem. In this paper we propose two decision support modeling approaches based on Influence Diagrams (ID) aiming to model the answer to the question “Adopt Cloud Services or Not?” Two models are developed and tested, the first is a generic ID with nodes interacting in a probabilistic manner, while the second is a more flexible version that utilizes Fuzzy Logic. Both models combine several factors that influence the decision to be taken, which were identified through literature review and input received from field experts. The proposed approaches are validated using five experimental scenarios, two synthetic and three real-world cases, and their performance suggests that they are highly capable of supporting the right decision.

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Ren, Min, Peiyu Liu, Zhihao Wang, and Jing Yi. "A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2647389.

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For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

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JUNEJA, DIMPLE, S. S. IYENGAR, and VIR V. PHOHA. "FUZZY EVALUATION OF AGENT-BASED SEMANTIC MATCH-MAKING ALGORITHM FOR CYBERSPACE." International Journal of Semantic Computing 03, no. 01 (March 2009): 57–76. http://dx.doi.org/10.1142/s1793351x09000653.

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Intelligent agents help to automate time and resource consuming tasks such as anomaly detection, pattern recognition, monitoring and decision-making. One of the major issues in automation of cyberspace is the discordance between the concept people use and the elucidation of the corresponding data by existing algorithms. Moreover, the measurement and computation of relevance referred to as degree of match-making is a crucial task and presents one of the most important challenges in unknown and uncertain environments of multi-agent systems. Optimal algorithms that generate the best matches for a user input are desired. This paper overcomes the challenges listed by proposing an agent-based semantic match-making algorithm that addresses the problem of heterogeneous ontology at user end and semantically enhances the user-input. A degree of match-making evaluation scheme based on fuzzy logic is proposed and evaluated using synthetic data from the web. The results are found to be consistent on the scale provided by the existing algorithms.

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Hemalatha, C. Sweetlin, Vignesh Sankaran, Vaidehi V, Shree Nandhini S, Sharmi P, Lavanya B, Vasuhi S, and Ranajit Kumar. "Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition." International Journal of Intelligent Information Technologies 14, no. 4 (October 2018): 77–97. http://dx.doi.org/10.4018/ijiit.2018100105.

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Face recognition from a large video database involves more search time. This article proposes a symmetric uncertainty based search space reduction (SUSSR) methodology that facilitates faster face recognition in video, making it viable for real time surveillance and authentication applications. The proposed methodology employs symmetric uncertainty based feature subset selection to obtain significant features. Further, Fuzzy C-Means clustering is applied to restrict the search to nearest possible cluster, thus speeding up the recognition process. Kullback Leibler's divergence based similarity measure is employed to recognize the query face in video by matching the query frame with that of stored features in the database. The proposed search space reduction methodology is tested upon benchmark video face datasets namely FJU, YouTube celebrities and synthetic datasets namely MIT-Dataset-I and MIT-Dataset-II. Experimental results demonstrate the effectiveness of the proposed methodology with a 10 increase in recognition accuracy and 35 reduction in recognition time.

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Sun, Jianguo, Peiqi Ge, and Zhenchang Liu. "Two-grade fuzzy synthetic decision-making system with use of an analytic hierarchy process for performance evaluation of grinding fluids." Tribology International 34, no. 10 (October 2001): 683–88. http://dx.doi.org/10.1016/s0301-679x(00)00152-3.

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POŚPIECH, Ewa. "Comparison of profits of effective portfolios with non-effective portfolios taking into account the fuzzy approach." Scientific Papers of Silesian University of Technology. Organization and Management Series 2021, no. 150 (2021): 199–212. http://dx.doi.org/10.29119/1641-3466.2021.150.15.

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Purpose: The aim of the article is to compare the results of the constructed effective portfolios and non-effective portfolios build on the basis of the value of the indicator constituting a synthetic assessment of decision variants. Design/methodology/approach: The article uses the multi-criteria TOPSIS method in the standard and fuzzy approach. It was used to evaluate listed companies that were examined in terms of selected fundamental and market characteristics. Taking into account the fuzzy method made it possible to treat the values of criteria from three years as triangular fuzzy numbers, and the values of the measure on the basis of which the ranking was created were also used to build non-effective portfolios. Findings: A multi-criteria evaluation of selected listed companies was performed and, on the basis of the obtained rankings, the sets constituting the basis for the construction of effective and non-effective portfolios were selected. The designated effective portfolios (after pre-selection using the FTOPSIS method) were in most cases more profitable than the market portfolio, while the non-effective portfolios, using TOPSIS as the pre-selection method, were (with one exception) more profitable than the effective portfolios. Research limitations/implications: It was not possible to unequivocally recommend the approach used, although the results appear promising. Practical implications: Taking into account the proposed approach, one can methodically build more profitable and more attractive portfolios. Originality/value: Non-standard approach to criteria assessments and the use of metacriterion values to determine the portfolio structure. The considerations may be of interest to stock market investors.

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Journal articles on the topic 'Fuzzy synthetic decision'

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