The purpose of this paper is to shed light on the concept of fuzzy logic ,its application in linguistics ,especially in language teaching and the fuzziness of some lexical items in English.
Fuzziness means that the semantic boundaries of some lexical items are indefinite and ideterminate.Fuzzy logic provides a very precise approach for dealing with this indeterminacy and uncertainty which grows (among other reasons) out of human behavior and the effect of society.
The concept of fuzzy logic has emerged in the development of the theory of fuzzy set by Lotfi Zadeh(a professor of computer science at the university of California) in 1965.It can be thought of as the application side of the fuzzy set theory. In linguistics, few scholars

      The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin

The research aims to focus on the human rights guarantees as they are subjected to continuous violations due to changes, wars and conflicts between nations and people, especially in the Arab world and the third world due to political, social, economic and environmental conditions and the failure of democratic tracks under the current reality.

Keyword: Human rights, Reality, Ambition.

 Active worms have posed a major security threat to the Internet, and many research efforts have focused on them. This paper is interested in internet worm that spreads via TCP, which accounts for the majority of internet traffic. It presents an approach that use a hybrid solution between two detection algorithms: behavior base detection and signature base detection to have the features of each of them. The aim of this study is to have a good solution of detecting worm and stealthy worm with the feature of the speed. This proposal was designed in distributed collaborative scheme based on the small-world network model to effectively improve the system performance.

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

Discriminant between groups is one of the common procedures because of its ability to analyze many practical phenomena, and there are several methods can be used for this purpose, such as linear and quadratic discriminant functions. recently, neural networks is used as a tool to distinguish between groups.

In this paper the simulation is used to compare neural networks and classical method for classify observations to group that is belong to, in case of some variables that don’t follow the normal distribution. we use the proportion of number of misclassification observations to the all observations as a criterion of comparison.  

This study focused on the various forms of violence that aged people encounter in their late life, the significance of taking measures that enhance not only the welfare for aged people but also the influence of early stage of life and how the old age effects on people experience throughout their live. Further, the importance of raising the awareness among communities to the extent of seriousness of old age in order to teach children the right habits and traditions, which are faded away over time.  The researcher indicated that raising the avenues of collaboration among home members, social organization, and all governmental facilities will support and provide a good treatment for this group of people.

Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don’t introduce defects or don’t affect adversely the existing functionalities. However, as the software systems grow in complexity, the number of test cases in regression suite can become large which results into more testing time and resource consumption. In addition, the presence of redundant and faulty test cases may affect the efficiency of the regression testing process. Therefore, this paper presents a new Hybrid Framework to Exclude Similar & Faulty Test Cases in Regression Testing (ETCPM) that utilizes automated code analysis techniques and historical test execution data to

           In this paper, the homotopy perturbation method (HPM) is presented for treating a linear system of second-kind mixed Volterra-Fredholm integral equations. The method is based on constructing the series whose summation is the solution of the considered system. Convergence of constructed series is discussed and its proof is given; also, the error estimation is obtained. Algorithm is suggested and applied on several examples and the results are computed by using MATLAB (R2015a). To show the accuracy of the results and the effectiveness of the method, the approximate solutions of some examples are compared with the exact solution by computing the absolute errors.

The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T