• A Bluetooth Routing Protocol Using Evolving Fuzzy Neural Networks

    تومان

    In this paper, a routing protocol which utilizes the characteristics of Bluetooth technology is proposed for Bluetooth-based mobile ad hoc networks. The routing tables are maintained in the master devices and the routing zone radius for each table is adjusted dynamically by
    using evolving fuzzy neural networks

  • A Bluetooth Routing Protocol Using Evolving Fuzzy Neural Networks

    تومان

    In this paper, a routing protocol which utilizes the characteristics of Bluetooth technology is proposed for Bluetooth-based mobile ad hoc  networks. The routing tables are maintained in the master devices and the routing zone radius for each table is adjusted dynamically by using evolving fuzzy neural networks. Observing there exists some useless routing packets which are helpless to build the routing path and increase  the network loads in the existing ad hoc routing protocols, we selectively use multiple unicasts or one broadcast when the destination device is  out of the routing zone radius coverage of the routing table. The simulation results show that the dynamic adjustment of the routing table size  in each master device results in much less reply time of routing request, fewer request packets and useless packets compared with two  representative protocols, Zone Routing Protocol and Dynamic Source Routing

  • A comparison of neural network models, fuzzy logic, and multiple linear regression for prediction of hatchability

    تومان

    Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age

  • A comparison of neural network models, fuzzy logic, and multiple linear regression for prediction of hatchability

    تومان

    Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best  decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age. Twenty-eight  data lines representing 4 inputs consisting of egg weight, eggshell thickness, egg sphericity, and yolk/albumin ratio and 1 output, hatchability,  were obtained from the literature and used to train an artificial neural network (ANN). The prediction ability of ANN was compared with that of  fuzzy logic to evaluate the fitness of these 2 methods. The models were compared using R2, mean absolute deviation (MAD), mean squared  error (MSE), mean absolute percentage error (MAPE), and bias. The developed model was used to assess the relative importance of each  variable on the hatchability by calculating the variable sensitivity ratio. The statistical evaluations showed that the ANN-based model predicted  hatchability more accurately than fuzzy logic. The ANNbased model had a higher determination of coefficient (R2 = 0.99) and lower residual  distribution (MAD = 0.005; MSE = 0.00004; MAPE = 0.732; bias = 0.0012) than fuzzy logic (R2 = 0.87; MAD = 0.014; MSE = 0.0004; MAPE =  2.095; bias = 0.0046). The sensitivity analysis revealed that the most important variable in the ANN-based model of hatchability was egg weight (variable sensitivity ratio, VSR = 283.11), followed by yolk/albumin ratio (VSR = 113.16), eggshell thickness (VSR = 16.23), and egg sphericity  (VSR = 3.63). The results of this research showed that the universal approximation capability of ANN made it a powerful tool to approximate  complex functions such as hatchability in the incubation process

  • A Configurational Comparative Method to Identify Multiple Pathways to Improve Patient-Centered Medical Home Models

    تومان

    This brief focuses on using fuzzy set Qualitative Comparative Analysis (fsQCA) to evaluate patientcentered medical home (PCMH) models. It is  part of a series commissioned by the Agency for Healthcare Research and Quality (AHRQ) and developed by Mathematica Policy Research under contract, with input from other nationally recognized thought leaders in research methods and PCMH models. The series is designed to expand  the toolbox of methods used to evaluate and refine PCMH models. The PCMH is a primary care approach that aims to improve quality, cost, and  patient and provider experience. PCMH models emphasize patient-centered, comprehensive, coordinated, accessible care, and a systematic  focus on quality and safety

  • A Framework for Reasoning with Expressive Continuous Fuzzy Description Logics

    تومان

    In the current paper we study the reasoning problem for fuzzy SI (f-SI) under arbitrary continuous fuzzy operators. Our work can be seen as an extension of previous works that studied reasoning algorithms for f-SI, but focused on specific fuzzy operators, e.g. fKD-SI and of reasoning algorithms for less expressive fuzzy DLs, like fL-ALC
    and fP -ALC (fuzzy ALC under the Lukasiewicz and product fuzzy operators, respectively).

  • A FUZZY AHP APPROACH FOR SUPPLIER SELECTION PROBLEM: A CASE STUDY IN A GEARMOTOR COMPANY

    تومان

    Supplier selection is one of the most important functions of a purchasing department. Since by deciding the best supplier, companies can save  material costs and increase competitive advantage. However this decision becomes complicated in case of multiple suppliers, multiple conflicting  criteria, and imprecise parameters. In addition the uncertainty and vagueness of the experts’ opinion is the prominent characteristic of the  problem. Therefore an extensively used multi criteria decision making tool Fuzzy AHP can be utilized as an approach for supplier selection  problem. This paper reveals the application of Fuzzy AHP in a gear motor company determining the best supplier with respect to selected  criteria.  The contribution of this study is not only the application of the Fuzzy AHP methodology for supplier selection problem, but also releasing  a comprehensive literature review of multi criteria decision making problems. In addition by stating the steps of Fuzzy AHP clearly and  numerically, this study can be a guide of the methodology to be implemented to other multiple criteria decision making problems

  • A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing

    تومان

    Fishing has become a major conservation threat to marine fishes. Effective conservation of threatened species requires timely
    identification of vulnerable species.

  • A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing

    تومان

    Fishing has become a major conservation threat to marine fishes. Effective conservation of threatened species requires timely identification of  vulnerable species. However, evaluation of extinction risk using conventional methods is difficult for the majority of fish species because the  population data normally required by such methods are unavailable. This paper presents a fuzzy expert system that integrates life history and  ecological characteristics of marine fishes to estimate their intrinsic vulnerability to fishing. We extract heuristic rules (expressed in IF–THEN  clauses) from published literature describing known relationships between biological characteristics and vulnerability. Input and output variables  are defined by fuzzy sets which deal explicitly with the uncertainty associated with qualitative knowledge. Conclusions from different lines of  evidence are combined through fuzzy inference and defuzzification processes. Our fuzzy system provides vulnerability estimates that correlate  with observed declines more closely than previous methods, and has advantages in flexibility of input data requirements, in the explicit  representation of uncertainty, and in the ease of incorporating new knowledge. This fuzzy expert system can be used as a decision support tool  in fishery management and marine conservation planning

  • A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning

    تومان

    As a nearly global language, English as a Foreign Language (EFL) programs are essential for people wishing to
    learn English.

  • A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning

    تومان

    As a nearly global language, English as a Foreign Language (EFL) programs are essential for people wishing to learn English. Researchers have  noted that extensive reading is an effective way to improve a person’s command of English. Choosing suitable articles in accordance with a  learner’s needs, interests and ability using an elearning system requires precise learner profiles. This paper proposes a personalized English article recommending system, which uses accumulated learner profiles to choose appropriate English articles for a learner. It employs fuzzy inference mechanisms, memory cycle updates, learner preferences and analytic hierarchy process (AHP) to help learners improve their English ability in an extensive reading environment. By using fuzzy inferences and personal memory cycle updates, it is possible to find an article best suited for both a learner’s ability and her/his need to review vocabulary. After reading an article, a test is immediately provided to enhance a learner’s memory for the words newly learned in the article. The responses of tests can be used to explicitly update memory cycles of the newly-learned  vocabulary. In addition, this paper proposes a methodology that also implicitly modifies memory cycles of words that were learned before. By intensively reading articles recommended through the proposed approach, learners comprehend new words quickly and review words that they knew implicitly as well, thereby efficiently improving their vocabulary volume. Analyses of learner achievements and questionnaires have  confirmed that the adaptive learning method presented in this study not only enhances the English ability of learners but also helps maintaining  their learning interest

  • A fuzzy pert approach to evaluate plant construction project scheduling risk under uncertain resources capacity

    تومان

    A plant construction project always involves lots of activities. Precise
    information about the activities duration is unfortunately unavailable due to the uncertain
    resources capacity

  • A fuzzy pert approach to evaluate plant construction project scheduling risk under uncertain resources capacity

    تومان

    A plant construction project always involves lots of activities. Precise information about the activities duration is unfortunately unavailable due to  the uncertain resources capacity. The fuzzy program evaluation and review technique (PERT) has been widely applied to solve the fuzzy project scheduling problem. This paper presents an extended fuzzy PERT approach with four major improvement aspects to support the construction  project scheduling management: 1) Evaluate operation fuzzy times based on available working volumes, resources quantity and fuzzy capacity  of resources, 2) Adopting a maximal  i  levelcut method to compare the fuzzy precedent activities times to determine the reasonable earliest  starting times of each activity, 3) Using fuzzy algebra method instead of fuzzy subtraction method to compute the fuzzy latest starting times  and 4) Developing a project scheduling risk index (PSRI) to assist the decision maker to evaluate the project scheduling risk. Simulations  experiments are conducted and demonstrated satisfactory results

  • A fuzzy set-based accuracy assessment of soft classi®cation

    تومان

    Despite the sizable achievements obtained, the use of soft classi®ers is still limited by the lack of well-assessed and adequate methods for evaluating the accuracy of their outputs.