Malicious malware is a serious threat to end-user in the Internet. Run-time analysis of a program execution behavior is widely used to classify malware’s activities especially when its signature is not obtainable. Towards this end, most of the existing run-time malware detection techniques make use of the information available in the Application Programming Interface call sequence in Windows platform. This paper suggests a novel malware revealing model based on graph model by capturing system calls during the execution of a suspected executable. The implementation results confirm that the proposed call graph model has better detection accuracy rate and also solves the scalability problem when it is compared to existing methods.
Aims: Customer value can be used to segment customers, and to determine which customers should be the focus of marketing efforts and dollars. Attributes are reduced by rough set theory, redundant attributes are removed and the core attributes are gained. Information gain is used in the classification of objectives and the analysis of this information. The aim of this paper provides customer value evaluation model using rough sets theory and classification algorithm (generated decision tree) by attribute information gain. The contribute of this paper is useful for developing data pre-processing, removing redundant attributes, and extracting decision rule from an instance of the customer value analysis. Design: The purpose approach is (1) Data preparation (2) Finding an optimal reduces using rough set theory (3) Generating rule tree (4) Rule extraction module. Methodology: This paper solves customer value evaluation problem using Rough set theory, entropy theory, Generate-decision- tree, and Information gain. Results: The results are that The proposed model is useful for developing data preprocessing, removing redundant attributes, and extracting decision rule from an instance of the customer value analysis.
This paper is concerned with a class of nonlinear multi-order fractional differential equation. By constructing the upper and lower control functions of the nonlinear term without any monotone requirement and applying the method of upper and lower solutions and Schauder fixed point theorem, the existence and uniqueness of positive solution for the equation are investigated. Moreover, some properties concerning the maximal, minimal, and continuation of solutions are obtained.
Aims/ Objectives: To show modelling, simulation and visualization of the dynamics of heat equation in a rod. Other PDE models and Numerical approaches are also discussed. Study Design: This paper is simply about how to use MATLAB software to solve PDE model. Methodology: A Class of PDE was uniquely modelled, simulated, and visualized using certain algorithm and MATLAB routine for Elliptic/Hyperbolic PDE. Different diagrams showing the nature of the solution are discussed. Results: The nature of the PDE governing heat dynamic in a rod are shown in the figures. Other PDE models are also presented. Conclusion: A simple demostration of numerical simulation of PDE governing the popular heat equation is presented. Three other PDE models are considered. The results herein can be adapted and applied to other more complex PDE models.
The strong equality of classical tautologies and their proof complexities comparative analysis in certain proof systems were given by first author in previous studies. Here we introduce the analogous notions of strong equality for non-classical (intuitionistic and minimal) tautologies and investigate the relations between the proof complexity measures of strongly equal non-classical tautologies in some proof systems. We prove that 1) the strongly equal tautologies have the same proof complexities in some proof systems and 2) there are such proof systems, in which some measures of proof complexities for strongly equal tautologies are the same, while the other measures differ from each other only as a function of the sizes of tautologies.
The significant advances in information and communication technologies and the growing focus on E-business and modern supply chain management have pushed firms towards closer collaboration and more efficient integration of the supply chain functions. In this paper, we consider two known integrated supply chain inventory models. In the first model the vendor ships a batch for each buyer immediately after the completion of batch production, whereas in the second model, the buyer receives the batch only after the previous batch is consumed by demand. We derived the closed form solutions for both models using a simple algebraic approach that can be easily applied without differential calculus. The proposed solution is illustrated by a numerical example.
R is a programming language and environment which is mainly aimed to be used for statistical calculations and data analysis. Since a vast amount of human resource is consumed for its source code and packages; it is comprehensive, large in size and more common among others. Extending computer software with statistical calculation routines requires extra human resource, therefore, the need of wrapper libraries has been appeared. There are many software libraries that communicate R with other languages. Despite they render similar services, they have their own pros and cons. RCaller is a software library which comes into prominence with its simplicity. In this paper we introduce the library with some examples. We mention its abilities as well as its limitations. RCaller can be used in relatively small projects as it has painless start-up process and steep learning curve.
In this paper, we discuss the statistical learning theory based on intuitionistic fuzzy random sample. First of all, we introduce the definition of intuitionistic fuzzy number intuitionistic fuzzy random variables. Secondly, we give some properties of this concept. Thirdly, the definitions of intuitionistic fuzzy empirical risk functional, intuitionistic fuzzy expected risk functional, and intuitionistic fuzzy empirical risk minimization principle are presented. Finally, we prove the key theorem of intuitionistic fuzzy random sample and obtain the rate of uniform convergence of learning process based on the intuitionistic fuzzy random sample.
Aims/ objectives: To demontrate effectiveness of Zernike Moments for Image Classification. Zernike moment(ZM) is an excellent region-based moment which has attracted the attentions of many image processing researchers since its first application to image analysis. Many papers have been published on several works done on ZM but no single paper ever give a detailed information of how the computation of ZM is done from the time the image is captured to the computation of ZM. This work showed how to effectively apply ZM on RGB images. We have demonstrated the effectiveness of Zernike moment in image classification system. A neuro-genetic intelligent system has been built with PNN classifier. The feature extracted viz ZM and Geometric features were further subjected to GA to bring the best combinatorial features for optimal accuracy. The algebraic structure of our novel fitness function enabled the GA to select the best results. The 10-fold CV used enabled the whole system to be unbiased giving a classification accuracy of 90.05%. A demonstration of affine properties of ZM are comprehensively stated and explained. In summary, the ZM enabled the classifier to have improved accuracy of 91% as compared with Geometric features with 89% accuracy.