Narrow Results

To strengthen the ideal theory in BCI-algebras, the more general concept $(\in ,\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $a$-ideals in BCI-algebras is proposed. It is shown that $(\in ,\in \vee q)$-fuzzy $a$-ideals are $(\in ,\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $a$-ideals; however, the converse is not valid. Following that, the concept of $(\in \vee ({\kappa }^{\ast },q_{\kappa }),\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $a$-ideals is introduced. We demonstrate that $(\in \vee ({\kappa }^{\ast },q_{\kappa }),\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $a$-ideals are $(\in ,\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $a$-ideals. The converse is not true, and an example is given to support it. An equivalent condition for $(\in ,\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $a$-ideals is provided. We prove that the $(\in ,\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $a$-ideals are $(\in ,\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $p$-ideals and $(\in ,\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $q$-ideals. Furthermore, $(\in ,\in \vee ({\kappa }^{\ast },q_{\kappa }))$-fuzzy $a$-ideals are characterized in terms $a$-ideals.

In this paper we introduce a fuzzy version of symport/antiport membrane systems. Our fuzzy membrane systems handle possibly inexact copies of reactives and their rules are endowed with threshold functions that determine whether a rule can be applied or not to a given set of objects, depending of the degree of accuracy of these objects to the reactives specified in the rule. We prove that these fuzzy membrane systems generate exactly the recursively enumerable finite-valued fuzzy subsets of ℕ.

This study evaluates the assignment system by FMCDM for Turkish Army Forces (TAF). TAF personnel are assigned to Garrisons (a headquarter or a certain geographical place in Turkey), for fixed periods. Selecting the optimal Garrison is an important problem for personnel, and is based on a number of alternatives and criteria. The system includes eight city centers, evaluated as alternatives. For defining the criteria, a questionnaire is designed and applied to army officers and sergeants. The criteria weights are determined by four randomly selected experts. Experts described the weights depending on their assessments, which may vary depending on their own necessities, experiences and priorities. They used linguistic weights, such as "very good, good, medium, low, very low". Hence, the assessments are away from certainty. This uncertainty implies fuzziness in weights. Besides, the criteria were determined by factor analysis, applied on the responses of the questionnaire, which also gives a sense of uncertainty. In the analysis under imprecision judgements, the criteria weights are presented as trapezoidal fuzzy numbers to get more reasonable and realistic solutions. Ideal and anti-ideal points are calculated for different optimism indexes and the final results are obtained based on these points. It is observed that different optimality indexes changed the rankings of alternatives.

Queuing models need well defined knowledge on arrivals and service times. However, in real applications, because of some measurement errors or some loss of information, it is hard to achieve deterministic knowledge. Non-deterministic knowledge interferes or complicates analysis of the queuing model. Additionally, when the customers are asked about their impressions on waiting times or service times, mostly the answers are linguistic expressions like "I waited too much", "service was fast", and that the responses are. Linguistic statements and ill defined data make the sense of imprecision in the model. In this study, arrivals and service times are defined as fuzzy numbers in order to represent this imprecision. Fuzzy multi-channel queuing systems and membership functions are introduced in defining the arrivals and service times. Besides, a new membership function based on a probability function is studied. Fuzzy queuing characteristics are calculated via different membership functions and the results are compared on simulations. Among models it is found that, Generalized Beta Distribution membership function is the one that minimized the queuing characteristics.

Nowadays, the capital cost of open-pit mining equipment is very high so any mistake in the selection of quantity, type and capacity of equipment may cause irreparable impact on the net present value of mining project. Mine planning engineers generally use their intuition and experience in decision making even though equipment selection is a complex multi criteria decision problem. Considering the tangible along with intangible factors in the mine equipment selection problem, this paper proposes a new method of multi criteria decision making (MCDM) that makes it possible to select the optimal equipment that satisfies the decision maker. In a real-world situation, because of incomplete or non-obtainable information, the data (attributes) are often not deterministic but they are usually fuzzy-imprecise. Our proposed model considers objective, critical, and subjective factors as the three main common factors in equipment selection analysis. The last two factors, critical and subjective factors, are defined by decision maker's judgments for more adoption with real world problems. A case study is presented to illustrate the use of the proposed model and to demonstrate the capability of the model. The result of this study shows significant reduction of time consumption of calculation and good precision compared to customary methods such as Chang's fuzzy AHP method.

The possibility for more confidential predictions, leaning on scientific methods and accomplishments of information technology leaves more time for the realization of logistic needs. Longstanding ambitions to acquire desired levels of efficiency within the system with minimal costs of resources, materials, energy and money are the features of executive structures of logistic systems. A successful logistic process is based on validation of technological development, indicating the need for a faster and more confidential integration of logistic systems and "instilling confidence" with military units that provide critical support (supply, transport and maintenance) will be reliably realized according to relevance and priority. Conclusions like these impose the necessity that the decision-making process of logistic organs is accessed carefully and systematically, since any wrong decision leads to a reduced state of readiness for military units. To facilitate the day-to-day operation of the Army of Serbia and the completion of both scheduled and unscheduled tasks it is necessary to satisfy the wide range of transport requirements. In this paper, the Adaptive Neuro Fuzzy Inference System (ANFIS) is described, thus making possible a strategy of coordination of transport assets to formulate an automatic control strategy. This model successfully imitates the decision-making process of the chiefs of logistic support. As a result of the research, it is shown that the suggested ANFIS, which has the ability to learn, has a possibility to imitate the decision-making process of the transport support officers and show the level of competence that is comparable with the level of their competence.

As most of the global economic activity takes place in the form of projects/programs, their effective management and governance is becoming more and more critical to the competitive position of organizations and societies. Project auditing and risk management, elaborate on methodologies that could be used for analyzing project progress, by identifying potential risks and liabilities, and finally recommending corrective and preventive actions. In relation to these fields, this paper proposes a fuzzy set based approach for project risk ranking in large-scale programs. The proposed approach defines a generic list of risk factors which is used for the ranking and risk assessment of all projects of a program. Data describing projects' progress as well as expert's evaluation of risk factors and project's risk exposure are being used as input parameters to the fuzzy set system. The relative probability of risk appearance due to risk factors for each of the projects is being calculated by providing valuable means for efficient decision-making and success of the program. Finally, this paper describes a case study called Operational Program "Road Axes, Ports, Urban Development" of Community Support Framework III in Greece, where the proposed approach was successfully applied.

A kriging method is presented as a spatial filter for smoothing gray-scale images degraded by Gaussian white noise. The concepts are based on the analysis of semivariances, the linear combination scheme of kriging, and fuzzy sets. Application of fuzzy sets allows a gradual transition between two boundaries of semivariance levels as a criterion for smoothing the pixel values. This fuzzy thresholding also allows some degree of flexibility to suit various desired results for particular problems. Experimental results obtained by the fuzzy kriging filter are smoother and still preserve edges compared with those by the adaptive Wiener filter.

Diabetes mellitus is a common chronic disease in recent years. According to the World Health Organization, the estimated number of diabetic patients will increase 56% in Asia from the year 2010 to 2025, where the number of anti-diabetic drugs that doctors are able to utilize also increase as the development of pharmaceutical drugs. In this paper, we present a recommendation system for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques, where fuzzy rules are used to represent knowledge to infer the usability of the classes of anti-diabetic drugs based on fuzzy reasoning techniques. We adopt the "Medical Guidelines for Clinical Practice for the Management of Diabetes Mellitus" provided by the American Association of Clinical Endocrinologists to build the ontology knowledge base. The experimental results show that the proposed anti-diabetic drugs recommendation system gets the same accuracy rate as the one of Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl.39(4) (2012) 3995–4006.) and it is better than Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl.39(4) (2012) 3995–4006.) due to the fact that it can deal with the semantic degrees of patients' tests and can provide different recommend levels of anti-diabetic drugs. It provides us with a useful way for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques.

A new technique for nonlinear sensitivity analysis of geophysical models for small size ensembles of model outputs has been developed. Such an analysis utilizes the following metrics: (a) Sobol–Saltelli sensitivity indices and cumulative distribution functions if perturbations of model parameters are random, and (b) a Hartley-like measure if perturbations of model parameters are nonrandom and parametrized through fuzzy sets. The indices and the Hartley-like measure allow for ranging model parameters along their significance to the model output. Our calculations demonstrate that accurate estimates of the sensitivity indices are possible even if an ensemble of random perturbations contains considerably less than 100 members. Some calculations were successfully provided for random ensembles with 20–30 members only but, in general 50–100 member ensembles are required to get robust and significant estimations of model sensitivity. The fuzzy set concept allows for robust estimations for small size nonrandom ensembles of model outputs (50–100 members) and accounts for additional a priori information on model sensitivity coming from different sources. The Lorenz 63 model (a few degrees of freedom) and the ocean component (POP) of the Community Climate System Model (CCSM3) (several thousand degrees of freedom) are used to illustrate the sensitivity analysis based on this approach.

Software measures (metrics) provide software engineers with an important means of quantifying essential features of software products and software processes such as software reliability, maintenance, reusability and alike. Software measures interact between themselves. Some of them may be deemed redundant. Software measures are used to construct detailed prediction models. The objective of this study is to pursue an association analysis of software measures by revealing dependencies (associations) between them. More specifically, the introduced association analysis is carried out at the local level by studying dependencies between information granules of the software measures. This approach is contrasted with a global level such as e.g., regression analysis. We discuss the role of information granules as meaningful conceptual entities that facilitate analysis and give rise to a user-friendly, highly transparent environment.

In this study, we highlight some fundamental issues of knowledge management and cast them in the setting of Granular Computing (GrC). We show how its formal constructs — information granules are instrumental in knowledge representation and specification of its level of abstraction.

This paper proposes a case-based classifier using a new approach that integrates rule-based and case-based reasoning approaches for enhanced accuracy. The rule-based reasoning component uses rules generated from a concept lattice of training data, binarized using fuzzy sets. These binarized data are stored as cases in the case-based classification component. The case-based component complements the rule-based component to enhance classification accuracy. Moreover, we designed the case-based component with an embedded similarity measure that uses a vector model for concept approximations. Thus, this design makes it possible to generate high quality rules and classify unseen new cases. In addition, the ability to build a knowledge base in lattice form is important for discovering hierarchical patterns, incrementing or updating the existing knowledge base, and inducing rules with our rule learning algorithm. The novel methodology was implemented and evaluated with benchmark datasets from the UCI repository and historic rubber prices in Thailand, demonstrating improvements in accuracy of classification calls. The results from the fact their several hierarchical datasets are very promising, with improved classification performance over prior reported methods.

In this paper we describe an approach to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. We also present a short survey of fuzzy and semantic approaches to knowledge extraction. The goal of such approaches is to address if and how some approaches met their goal.

Does a post with specific emotional content that is posted on Twitter by an influential user have the capability to affect and even alter the opinions of those who read it? Accordingly, “influential” users affected by this post can then affect their followers so that eventually a large number of users may change their opinions about the subject the aforementioned post was made on? Social Influence can be described as the power or even the ability of a person to yet influence the thoughts and actions of other users. So, User Influence stands as a value that depends on the interest of the followers (via replies, mentions, retweets, favorites). Our study focuses on identifying such phenomena on the Twitter graph of posts and on determining which users’ posts can trigger them. Furthermore, we analyze the Influence Metrics of all users taking part in specific discussions and verify the differences among them. Finally the percentage of Graph cover when the diffusion starts from the “influential” users, is measured and corresponding results are extracted. Hence, results show that the proposed implementations and methodology can assist in identifying “influential” users, that play a dominant role in information diffusion.

This paper proposes a recurrent neural network of fuzzy units, which may be used for approximating a hetero-associative mapping and also for pattern classification. Since classification is concerned with set membership, and objects generally belong to sets to various degrees, a fuzzy network seems a natural for doing classification. In the network proposed here each fuzzy unit defines a fuzzy set. The fuzzy unit in the network determines the degree to which the input vector to the unit lies in that fuzzy set. The fuzzy unit may be compared to a perceptron in which case the input vector is compared to the weighting vector associated with the unit by taking the dot product. The resulting membership value in case of the fuzzy unit is compared to a threshold. Training of a fuzzy unit is based on an algorithm for solving linear inequalities similar to the method used for Ho-Kashyap recording. Training of the whole network is done by training each unit separately. The training algorithm is tested by trying the algorithm out on representations of letters of the alphabet with their noisy versions. The results obtained by the simulation are very promising.

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

The purpose of the paper is to introduce a new type of fuzzy matrix games: fuzzy constrained matrix games. A computational method for its solution based on establishment of the auxiliary fuzzy linear programming for each player is proposed. The approach based on the multiobjective programming is establisched to solve these fuzzy linear programming. Effectiveness is illustrated with a numerical example.

The need for monotone approximation of scattered data often arises in many problems of regression, when the monotonicity is semantically important. One such domain is fuzzy set theory, where membership functions and aggregation operators are order preserving. Least squares polynomial splines provide great flexbility when modeling non-linear functions, but may fail to be monotone. Linear restrictions on spline coefficients provide necessary and sufficient conditions for spline monotonicity. The basis for splines is selected in such a way that these restrictions take an especially simple form. The resulting non-negative least squares problem can be solved by a variety of standard proven techniques. Additional interpolation requirements can also be imposed in the same framework. The method is applied to fuzzy systems, where membership functions and aggregation operators are constructed from empirical data.

Queries to a database can be made more powerful by allowing flexibility in the specification of what has to be retrieved, and by referring to cases either for expressing the request, or for computing the answer. In this paper, we present an implemented information system (applied to a database describing houses to let), based on an approach developed in the fuzzy set and possibility theory setting. This provides a unified framework for expressing users' preferences about what they are looking for, for weighting the importance of requirements, for referring to examples that they like and/or counter-examples that they dislike, and for making case-based predictions. Thus information querying goes beyond the retrieving of items from a database, and involves associated tools which help the user to figure out the actual contents of the database.