In this paper, we apply the new implementation of reproducing kernel Hilbert space method to give the approximate solution to some third-order boundaryvalue problems with variable coefficients. In this method, the analytical solution is expressed in the form of a series. At the end, two examples are given to illustrate implementation, accuracy and effectiveness of the method.

Forecasting the number of students who are going to take a special course in next semester in Computer Engineering field at Payam Noor University is the subject. To do this, many neural network structures have been tested with MATLAB software by existing data and were compared to real data, networks like feedforward backpropagation 3 and 4-layared, RBF network, etc. To achieve a network with optimum structure, various parameters and criteria like MAE, MSE and MSEREG, have been examined. At last, a 3-layered feedback neural network in the form of 20-n-1 was chosen for this problem. Comparing experiential results with real data, it is shown that the obtained model can effectively forecast enrolments of students. So it can be used for forecasting tasks especially when a forecast with high accuracy is needed.

Error Correction Code is very important in modern communication systems. BCH (Bose, Chaudhuri, and Hocqunghem) codes are being widely used in variety communication and storage systems. In this paper the construction and decoding BCH codes which are based on finite field arithmetic is introduced and also an improved algorithm for finding roots of polynomials over finite fields is proposed. This makes possible significant speed up of the decoding process of BCH codes.

Genetic algorithm is one of evolutionary algorithms which have been used widely to solve many problems such as data clustering. There are lots of genetic data clustering algorithms which have worked on fitness function to improve the accuracy of algorithm in evaluation of generated chromosomes and have used simple and all purpose crossover and mutation operators such as one point crossover and random change mutation. Mutation process randomly modifies the gene values at selected locations to increase genetic diversity, by forcing the algorithm to search areas other than the current area. Simple non heuristic mutations such as random change mutation increase genetic diversity but they also increase execution time and decrease fitness of population. In this paper we introduce some new heuristic mutation operators for genetic data clustering. Experimental results show that all of proposed mutation operators creates better offspring than random change mutation and increases the fitness of population.

In this paper we study the approximation of common fixed points of a finite family of nonexpansive mappings in uniformly smooth Banach spaces. Also we show that the convergence of the proposed algorithm can be proved under some types of control conditions.

Text Clustering is a text mining technique which divides the given set of text documents into significant clusters. It is used for organizing a huge number of text documents into a well-organized form. In the majority of the clustering algorithms, the number of clusters must be specified apriori, which is a drawback of these algorithms. The aim of this paper is to show experimentally how to determine the number of clusters based on cluster quality. Since partitional clustering algorithms are well-suited for clustering large document datasets, we have confined our analysis to a partitional clustering algorithm.

In this paper, the security of key agreement protocol based on Sylvester Hadamard matrices proposed by Chang-hui Choe and Moon Ho Lee has been improved. Applying new changes, the weakness of their protocol was introduced and its security was increased. In short, new symmetric key agreement protocol will be suitable for insecure communication when two users want to share a common secret key with the low computing power.

In this research, both kinematic interaction (KI) and inertial interaction (II) effects of soil-structure interaction (SSI) on inelastic seismic demands of structures are investigated. Site effect is also considered only by applying ground motions recorded at site classes D and E (as defined in NEHRP[1] and FEMA-440 [2]) that on them SSI effect is considerable. Carrying out a parametric study, the structure and underlying soil are modeled as a Single Degree Of Freedom (SDOF) structure with elasto-plastic behavior and a mathematical simplified 3DOF system, based on the concept of Cone Models, respectively. Also the foundation is considered as a rigid cylinder embedded in the soil with different embedment ratios. Then the whole soil-structure systems are analyzed under 30 ground motion recorded at site classes D and E and a comprehensive parametric study is performed for a wide range of non-dimensional parameters defining SSI problem. Results indicated that ignoring SSI causes considerable and in some cases un-conservative differences in seismic demands of structures. In the case of embedded foundation, it is observed that rocking input motion due to KI plays the main role and increase the structural demands especially in deep foundation embedment and slender buildings located on soft soils. Consequently, comparing the results with and without inclusion of SSI effects reveals that both II and KI effects of SSI play an important role in analyses or design procedures and ignoring them may cause un-conservative results in cases of deep embedded foundation and slender structures.