In this article, we put a direct method to construct the rational solitary wave solutions for some nonlinear differential difference equations in mathematical physics which may be called the rational solitary wave difference method. We use the proposed method to construct the rational solitary exact solutions for some nonlinear differential difference equations via the lattice equation, the discrete nonlinear Klein Gordon equation. The proposed method is more effective and powerful to obtain many rational solitary exact solutions for nonlinear differential difference equations.

Ant colony Optimization (ACO) is a relatively new class of metaheuristic search techniques for hard optimization problems. In this paper we focus on the definition and minimization of the objective function of the VPR using an Ant Colony System – Node (ACS – N) algorithm. The (ACS – N) algorithm is implemented for an eight node graph with respective demands. Moreover, in this paper we study the effect of the number of the ants to the value of the objective function.

Medical images require large amounts of memory. This calls for image compression (block- based DCT compression) to reduce significantly the amount of data required to represent an image. One of the major drawbacks of the block-based DCT compression methods is that it may result in visible artifacts at block boundaries due to coarse quantization of the coefficients. This paper proposes a new adaptive post- filtering algorithm to remove coding artifacts in block-based DCT compressed medical images. The blocking artifact in the smooth and non-smooth regions are removed by modifying a few DCT coefficients while an edge preserving smoothing filter is applied to the intermediate region. Experimental results illustrating the performance of proposed method on the basis of PSNR, MSSIM, and SF indices are presented and evaluated. Compared with other methods, the proposed one achieves better detail preservation and artifact removal performance with lower computational complexity.

In this paper, the determination of the heat source and heat flux at x = in one-dimensional inverse heat conduction problem (IHCP) is investigated. First with an suitable transformation, the problem is reduced, then the method of fundamental solutions (MFS) is used to solve the problem. Due to ill-posed the IHCP, the Tikhonov regularization method with Generalized cross validation (GCV) criterion are employed in numerical procedure. Finally, some numerical examples are presented to show the accuracy and effectiveness of the algorithm.

In this paper, we present the Jacobian consistency property for generalized complementarity problems. The idea is based on a nonsmooth system of equations reformulation of the generalized complementarity problem and a smoothing approximation function for solving the nonsmooth system of equations.

Data mining is the extraction of the hidden information from large databases. It is a powerful technology to explore important information in the data warehouse. Privacy preservation is a significant problem in the field of data mining. It is more challenging when data is distributed among different parties. In this paper, we address the problem of privacy preserving three-layer Naïve Bayes classification over vertically partitioned data. Our approach is based on Secure Multiparty Computation (SMC). We use secure multiplication protocol to classify the new tuples. In our protocol, secure multiplication protocol allows to meet privacy constraints and achieve acceptable performance and our classification system is very efficient in term of computation and communication cost.

In this paper we study the concept of intuitionistic neutrosophic set of Bhowmik and Pal. We have introduced this concept in soft sets and defined intuitionistic neutrosophic soft set. Some definitions and operations have been introduced on intuitionistic neutrosophic soft set. Some properties of this concept have been established.

In this paper, Artificial Neural Networks (ANNs) are used to model the effect of atmospheric air-plasma treatment on fabric surfaces with various structures. In order to reduce the complexity of the models and increase the knowledge and comprehension of the underlying process, a fuzzy sensitivity variation criterion is used to select the most relevant parameters which are taken as inputs of the reduced neural models. The model outputs are the water contact angle and the capillarity of woven fabrics, characterizing the change of fabric surfaces. The early stopping and Bayesian regularization techniques are used for improving the network’s generalization capability. Two different network configurations are studied. One deals with two networks having each one output layer neuron and another with a single network combining the two outputs. Obtained results showed that the first configuration combined with the Bayesian regularization approach is the most suitable to achieve a good prediction accuracy.