April 2011 Issue Vol.1 No.3
Abstract: The main objective of the paper is to study and develop an efficient method for Hard Disk Drive(HDD) Security using Full Disk Encryption (FDE) with Advanced Encryption Standards(AES) for data security specifically for Personal Computers(PCS) and Laptops . The focus of this work is to authenticate and protect the content of HDD from illegal use. The paper proposes an adaptive methods for protecting a HDD based on FDE. The proposed method is labeled as DiskTrust. FDE encrypts entire content or a single volume on your disk. DiskTrust implements Symmetric key cryptography with, Advanced Encryption Standards. Finally, the applicability of these methodologies for HDD security will be evaluated on a set of data files with different key sizes.
Keywords: Information Security, Integrity, confidentiality, Authentication, Encryption.
In this paper, we study the unsteady hydromagnetic flow of a Walter’s fluid (Model B') down an open inclined channel of width 2a and depth d under gravity, the walls of the channel being normal to the surface of the bottom under the influence of a uniform transverse magnetic field. A uniform tangential stress is applied at the free surface in the direction of flow. We have evaluated the velocity distribution by using Laplace transform and finite Fourier Sine transform technique. The velocity distribution has been obtained taking different form of time dependent pressure gradient g(t), viz., i) constant ii) exponential decreasing function of time and iii) Cosine function of time. The effects of magnetic parameter M, Reynolds number R and the viscoelastic parameter K are discussed on the velocity distribution in three different cases.
Keywords: Walter’s B' fluid, open inclined channel, Laplace transform and finite Fourier Sine transform technique.
Abstract: The growing volume of data usually creates an interesting challenge for the need of data analysis tools that discover regularities in these data. Data mining has emerged as disciplines that contribute tools for data analysis, discovery of hidden knowledge, and autonomous decision making in many application domains. The purpose of this study is to compare the performance of two data mining techniques viz., factor analysis and multiple linear regression for different sample sizes on three unique sets of data. The performance of the two data mining techniques is compared on following parameters like mean square error (MSE), R-square, R-Square adjusted, condition number, root mean square error(RMSE), number of variables included in the prediction model, modified coefficient of efficiency, F-value, and test of normality. These parameters have been computed using various data mining tools like SPSS, XLstat, Stata, and MS-Excel. It is seen that for all the given dataset, factor analysis outperform multiple linear regression. But the absolute value of prediction accuracy varied between the three datasets indicating that the data distribution and data characteristics play a major role in choosing the correct prediction technique.
Keywords: Data mining, Multiple Linear Regression, Factor Analysis, Principal Component Regression, Maximum Liklihood Regression, Generalized Least Square Regression
Abstract: This document is part of original research work by the authors in a bid to explore new fields for applying Data Mining Techniques. The sample data is part of a large data set from University of Maryland (UMD) and outlines how more meaningful patterns can be discovered by preprocessing the data in the form of OLAP cubes.
Keywords: GTD, OLAP, Data Mining, Terror Databases.
Abstract: The rapid growth of web has resulted in vast volume of information. Information availability at a rapid speed to the user is vital. English language (or any for that matter) has lot of ambiguity in the usage of words. So there is no guarantee that a keyword based search engine will provide the required results. This paper introduces the use of dictionary (standardised) to obtain the context with which a keyword is used and in turn cluster the results based on this context. These ideas can be merged with a metasearch engine to enhance the search efficiency.
Keywords: Clustering, concept mining, information retrieval, metasearch engine.
Abstract: In the info-tech age E-Methods of learning are becoming the most important vehicle in disseminating knowledge in higher education institutions. This sector is growing and changing at a rapid speed due to developments in technologies. But teaching is an art. Can there be fun learning with raw and dry technology? How can we make the best use of E- Methods, can we make the required information and data available to the students in a flexible manner, at ease all the time? What are the advantages of traditional methods of teaching and learning? Is E-learning a progressive stage incubating all the benefits of the Manual learning or it is only a window dressing on the face of advancement? Can we convert the boring, tedious subjects into interactive, monotony breaking joyous learning? In this paper the researchers have focused on the modernization of E- Pedagogy vis-à-vis the traditional method of learning. They have highlighted the effectiveness of using the E- learning elements and various E- Methods. This work has used the decision tree algorithms particularly Classifiers.trees.J48 The obtained results show that using online examination attribute plays major role in increasing the average grade of the class in higher education. The novelty of this work is that the researchers have focused on the teaching methodology used by the faculty members and the tools available in the universities. We believe that this work will play a constructive role in building higher education system. Our generated rules/output can be used by the decision makers in the improvement of higher education system processes.
Keywords: E- learning, higher education systems, modernization, decision tree algorithm.