This work implements the face recognition system based on two stages, the first stage is feature extraction stage and the second stage is the classification stage. The feature extraction stage consists of Self-Organizing Maps (SOM) in a hierarchical format in conjunction with Gabor Filters and local image sampling. Different types of SOM’s were used and a comparison between the results from these SOM’s was given.

The next stage is the classification stage, and consists of self-organizing map neural network; the goal of this stage is to find the similar image to the input image. The proposal method algorithm implemented by using C++ packages, this work is successful classifier for a face database consist of 20

Abstract:

      In this research we discussed the parameter estimation and variable selection in Tobit quantile regression model in present of multicollinearity problem. We used elastic net technique as an important technique for dealing with both multicollinearity and variable selection. Depending on the data we proposed Bayesian Tobit hierarchical model with four level prior distributions . We assumed both tuning parameter are random variable and estimated them with the other unknown parameter in the model .Simulation study was used for explain the efficiency of the proposed method and then we compared our approach with (Alhamzwi 2014 & standard QR) .The result illustrated that our approach

Software testing is a vital part of the software development life cycle. In many cases, the system under test has more than one input making the testing efforts for every exhaustive combination impossible (i.e. the time of execution of the test case can be outrageously long). Combinatorial testing offers an alternative to exhaustive testing via considering the interaction of input values for every t-way combination between parameters. Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). IOR combinatorial testing only tests for the important combinations selected by the tester. Most of the researches in combinatorial testing appli

Software testing is a vital part of the software development life cycle. In many cases, the system under test has more than one input making the testing efforts for every exhaustive combination impossible (i.e. the time of execution of the test case can be outrageously long). Combinatorial testing offers an alternative to exhaustive testing via considering the interaction of input values for every t-way combination between parameters. Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). IOR combinatorial testing only tests for the important combinations selected by the tester. Most of the researches in combinatorial testing

conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. 

Root-finding is an oldest classical problem, which is still an important research topic, due to its impact on computational algebra and geometry. In communications systems, when the impulse response of the channel is minimum phase the state of equalization algorithm is reduced and the spectral efficiency will improved. To make the channel impulse response minimum phase the prefilter which is called minimum phase filter is used, the adaptation of the minimum phase filter need root finding algorithm. In this paper, the VHDL implementation of the root finding algorithm introduced by Clark and Hau is introduced.
VHDL program is used in the work, to find the roots of two channels and make them minimum phase, the obtained output results are

The paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. The objective which can be presented in a mathematical model is optimizing the space usage in a container. Besides the interaction effect between students and teacher, this algorithm also observes the learning process between students in the classroom which does not need any control parameters. Thus, TLBO provides the teachers phase and students phase as its main updating process to find the best solution. More precisely, to validate the algorithm effectiveness, it was implemented in three sample cases. There was small data which had 5 size-types of items with 12 units, medium data which had 10 size-types of items w