Pressing the keys of a piano randomly does not create music. Music is created only when there is a pattern in the musical notes. This pattern gets embedded in the corresponding musical data, numerical in character. Given that statistics is the science of exploring and studying patterns in numerical data,we are motivated to perform a statistical analysis, with special emphasis on modeling wherein lies the strength of statistics, of one of India’s patriotic songs ‘Aye mere watanke logo’. The experimental results, which are encouraging, bear a direct comparison with those of India’s National Anthem.
The use of Cryptography meaning the art of secret writing for securing information shared over an open medium is an old methodology but its importance in the present scenario has increased tremendously. With the increase in the computing power of an eaves dropper, it has become unavoidable to look for a better and better cryptographic primitive that provides a level of security while keeping a check upon the time consumed to encrypt and decrypt the data. In this review article, we have analyzed some of the literatures related to cryptographic algorithms (both symmetric and asymmetric key), new trends in cryptography such as Musical Cryptography, Cryptography using genetic algorithm, neural networks and fuzzy logic. Apart from these some of the allied areas of cryptography such as cryptographically secure pseudo-random bits generator and integer factorization have also been reviewed.
Mobile membranes represent a model of computation inspired from the biological movement provided by endocytosis and exocytosis in the living cells. This paper presents a survey of the results treating the computational power of the mobile membranes, their efficiency in solving NP-complete problems, and connections with other formal approaches able to handle mobility.
Actuaries are often in search of heavy-tailed distributions to provide adequate fits to the\nheavy-tailed financial sciences data. In the present work, a class of distributions, called new\nextended family of heavy-tailed distributions is introduced. For the illustrative purposes, a\nspecial sub-case of the proposed family is considered. The model parameters are estimated\nvia the maximum likelihood estimation procedure. To prove the usefulness of the proposed\nmethod in financial sciences, a simulation study based on the actuarial measures such as Value\nat Risk and Tail Value at Risk is conducted. Based on the simulation study, we observe that\nthe proposed distribution may be a good candidate model suitable for modeling financial and\nactuarial sciences data. Finally, the flexibility and importance of the new model is illustrated\nempirically by means of two applications to insurance data.