Minimum Distance Estimation of Milky Way Model Parameters and Related Inference

We propose a method to estimate the location of the Sun in the disk of the Milky Way using a method based on the Hellinger distance and construct confidence sets on our estimate of the unknown location using a bootstrap based method. Assuming the Galactic disk to be two-dimensional, the sought solar location then reduces to the radial distance separating the Sun from the Galactic center and the angular separation of the Galactic center to Sun line, from a pre-fixed line on the disk. On astronomical scales, the unknown solar location is equivalent to the location of us earthlings who observe the velocities of a sample of stars in the neighborhood of the Sun. This unknown location is estimated by undertaking pairwise comparisons of the estimated density of the observed set of velocities of the sampled stars, with densities estimated using synthetic stellar velocity data sets generated at chosen locations in the Milky Way disk according to four base astrophysical models. The "match" between the pair of estimated densities is parameterized by the affinity measure based on the familiar Hellinger distance. We perform a novel cross-validation procedure to establish a desirable "consistency" property of the proposed method.Comment: 25 pages, 10 Figures. This version incorporates the suggestions made by the referees. To appear in SIAM/ASA Journal on Uncertainty Quantificatio

Securing Text Transmission in E-learning through Natural Language Steganography: An Object Oriented Approach

With the increasing availability of Internet, the e-learning is also getting popularity at a high speed. But for a secure and efficient e-learning system security is a great matter of concern. Some of the important documents transmit between the participants of e-learning are text files like the user id of the learner or the teacher, password, private keys etc. If the hacker can reach these documents, they can get full access of the system which resulting fake marks sheet or admit card, which is very harmful for any e-learning institute. So text steganography is a good technique through which confidential and valuable data may be transacted securely by integrating text steganography along with AES block cipher. In this paper, we proposed a model for securing the transmission of text documents from the sender to receiver in an e-learning system wrapped with AES encryption algorithm, which provide better security

OBJECT ORIENTED METRIC BASED ANALYSIS OF TEXT TRANSMISSION IN E-LEARNING THROUGH NATURAL LANGUAGE STEGANOGRAPHY

E-learning is an application of information and communication technology in the field of learning. Through steganography the e-learning institution can provide security to other participants of e-learning like teacher and learner. Here we use text steganography with modified SNOW algorithm while passing secret texts from the administrator to the learner in an e-learning system. In this paper, we calculate the object oriented metric based analysis of CK and MOOD metrics of our proposed model, which ensures the advantages of code redundancy, code reusability, and cost effectiveness and so on