The estimation of the stressÙ€ strength reliability of Invers Kumaraswamy distribution will be introduced in this paper based on the maximum likelihood, moment and shrinkage methods. The mean squared error has been used to compare among proposed estimators. Also a Monte Carlo simulation study is conducted to investigate the performance of the proposed methods in this paper.

In this paper, we used maximum likelihood method and the Bayesian method to estimate the shape parameter (θ), and reliability function (R(t)) of the Kumaraswamy distribution with two parameters l , θ (under assuming the exponential distribution, Chi-squared distribution and Erlang-2 type distribution as prior distributions), in addition to that we used method of moments for estimating the parameters of the prior distributions. Bayes

Face recognition is one of the most applications interesting in computer vision and pattern recognition fields. This is for many reasons; the most important of them are the availability and easy access by sensors. Face recognition system can be a sub-system of many applications. In this paper, an efficient face recognition algorithm is proposed based on the accuracy of Gabor filter for feature extraction and computing the Eigen faces. In this work, efficient compressed feature vector approach is proposed. This compression for feature vector gives a good recognition rate reaches to 100% and reduced the complexity of computing Eigen faces. Faces94 data base was used to test method.

    In this paper we introduce a brief review about Box-Jenkins models. The acronym ARIMA stands for “autoregressive integrated moving average”. It is a good method to forecast for stationary and non stationary time series. According to the data which obtained from Baghdad Water Authority, we are modelling two series, the first one about pure water consumption and the second about the number of participants. Then we determine an optimal model by depending on choosing minimum MSE as criterion.

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

<span>Distributed denial-of-service (DDoS) attack is bluster to network security that purpose at exhausted the networks with malicious traffic. Although several techniques have been designed for DDoS attack detection, intrusion detection system (IDS) It has a great role in protecting the network system and has the ability to collect and analyze data from various network sources to discover any unauthorized access. The goal of IDS is to detect malicious traffic and defend the system against any fraudulent activity or illegal traffic. Therefore, IDS monitors outgoing and incoming network traffic. This paper contains a based intrusion detection system for DDoS attack, and has the ability to detect the attack intelligently, dynami