Image thresholding is very useful for keeping the significant part of an image and getting rid of the unimportant part or noise. This holds true under the assumption that a reasonable threshold value is chosen. The study of image thresholding techniques in earthquake engineering, remote sensing, geology and geophysics seems to be extremely important for recognition of certain patterns such as f...

predicting different behaviors in computer networks is the subject of many data mining researches. providing a balanced intrusion detection system (ids) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. many of the proposed methods perform well in one of the two aspects, and concentrate on a su...

Fuzzy regression analysis using fuzzy linear models with symmetric triangular fuzzy number coefficient has been introduced by Tanaka et al. The goal of this regression is to find the coefficient of a proposed model for all given input-output data sets. In this paper, we propose a new 1716 E. Pasha et al method for computation of fuzzy regression. The method is constructed on the basis of minimi...

Eutrophication phenomenon is one of the most common water quality problems in reservoirs in many regions. Determining the trophic status of the reservoirs is not a precise process and contains vagueness. Fuzzy set and entropy theories are concepts which can model uncertainty and imprecision in the data and the analysis. In this study, an Entropy-based Fuzzy Eutrophication Index model has been d...

A fuzzy decision tree is an important tool for knowledge acquisition in uncertain environments. Most of the existing fuzzy decision tree algorithms do not systematically consider the impact of the non-linear characteristics of the membership degree of fuzzy sets; they are therefore unable to integrate uncertainty processing preferences into the selection of extended attributes. This paper initi...

In this paper, a quantum neuro-fuzzy classifier (QNFC) for classification applications is proposed. The proposed QNFC model is a five-layer structure, which combines the compensatory-based fuzzy reasoning method with the traditional Takagi–Sugeno–Kang (TSK) fuzzy model. The compensatory-based fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy lo...

in this paper, a maximum likelihood estimation and a minimum entropy estimation for the expected value and variance of normal fuzzy variable are discussed within the framework of credibility theory. as an application, a credibilistic portfolio selection model is proposed, which is an improvement over the traditional models as it only needs the predicted values on the security returns instead of...

This paper presents a comparison of the three fuzzy based image segmentation methods namely Fuzzy C-Means (FCM), TYPE-II Fuzzy C-Means (T2FCM), and Intuitionistic Fuzzy C-Means (IFCM) for digital images with varied levels of noise. Apart from qualitative performance, the paper also presents quantitative analysis of these three algorithms using four validity functions-Partition coefficient (Vpc)...

In this paper we propose an extension of classification algorithm based on ant colony algorithms to handle continuous valued attributes using the concepts of fuzzy logic. The ant colony algorithms transform continuous attributes into nominal attributes by creating clenched discrete intervals. This may lead to false predictions of the target attribute, especially if the attribute value history i...

s: In this paper, we define the portfolio return as fuzzy average yield and risk as hybrid-entropy and variance to deal with the portfolio selection problem with both random uncertainty and fuzzy uncertainty, and propose a mean-variance hybrid-entropy model (MVHEM). A multi-objective genetic algorithm named Non-dominated Sorting Genetic Algorithm II (NSGA-II) is introduced to solve the model. W...