iosr-Jm   Volume-18 ~ Issue-4 ~ Jul. – Aug. 2022

Abstract : Any positive integer 𝑛 other than 10 with abundancy ind.......ex

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Abstract: The present paper deals numerical analysis of heat transfer of nanofluid flow over a flat stretching sheet. Two set of boundary conditions, a constant and a linear stream wise variation of nano particle volume fraction and wall temperature were analyzed. The governing equations were reduced to a set of nonlinear ordinary differential equations, ODE's,. The dependencies of solutions on Prandtl number Pr, Lewis number Le, Brownian motion number Nb and thermophoresis number Nt were studied in detail. The results showed that the reduced Nusselt number and the reduced Sherwood number increased for the of compared to. The increase of the Nb, Nt and Le numbers caused decrease of the reduced Nusselt number; while the reduced Sherwood number increased with increase of the Nb.....

Key words stretching sheet, nanofluid, laminar boundary layer, Brownian motion, Thermophoresis, partial differential equations, numerical solution

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[4]. Andersson H. I., Bech, K. H. and Dandapat B.S. Magnetohydrodynamic Flow of a power-law fluid over a stretching sheet, Int. J. Non-Linear Mechanics, 27(6), 929–936 (1992)
[5]. Magyari E., and Keller, B. Exact solutions for self-similar boundary-layer flows induced by permeable stretching walls, Eur. J. Mechanics-B/Fluids, 19(1), 109–122 (2000)

Abstract: In this paper, for calculating the VaR, we have employed the Monte Carlo simulation approach, which is a semi-parametric method. Using Microsoft Excel and R, we estimated VaR estimates for several assets Over three years Jan 2019 to Dec 2021. The asset data is downloaded from yahoo finance. we have estimated one-day VaR for these assets and back-tested it. The duration involves before covid-19 and after the covid-19 situation in the Indian market. The percentage of failure is done by the Binary Proportion of Failures for this strategy..

Key words Value at Risk (VaR), Monte Carlo simulation,COVID, Back-testing

[1]. Abad, P., Benito, S., & López, C. (2014). A comprehensive review of Value at Risk methodologies. The Spanish Review of Financial Economics, 12(1), 15-32.
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[4]. Baltaev, A., &Chavdarov, I. (2013). Econometric Methods and Monte Carlo Simulations for Financial Risk Management.
[5]. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
[6]. Eriksson, B., &Billinger, O. (2009). Star Vars: Finding the optimal Value-at-Risk approach for the banking industry.

Abstract: In today's world, with the introduction of 5G (5th Generation) allied technologies, information transmission has increased rapidly. The use of the image in daily life has increased as information technology is developing. Therefore, the demand for a system of security in sending images through the internet is necessary. There are many types of securities, but encryption is one of the best techniques.It makes an image readable only to the authorized access. Data security.....

Key words Gauss map, Linear Feedback Shift Register (LFSR), Image Encryption-Decryption

[1]. Bangar, S. S. (2016). Security of Image Processing Over a Network.
[2]. Rahmawati, W. M., & Liantoni, F. (2019). Image Compression and Encryption Using DCT and Gaussian map. InIOP Conference Series: Materials Science and Engineering (Vol. 462, No. 1, p. 012035). IOP publishing.
[3]. Rohith, S., Bhat, K. H., & Sharma, A. N. (2014, October). Image encryption and decryption using chaotic key sequence generated by a sequence of logistic map and sequence of states of Linear Feedback Shift Register. In 2014 international conference on advances in electronics computers and communications (pp. 1-6). IEEE.
[4]. Sahay, A., & Pradhan, C. (2017, April). Gauss iterated map based RGB image encryption approach. In the 2017 International Conference on Communication and Signal Processing (ICCSP) (pp. 0015-0018). IEEE.
[5]. Srushti, G., and Gor, R. (2022). Digital Image Encryption using RSA and LFSR. International Journal of Engineering Science Technologies, 6(4), 1-16. doi: 10.29121/IJOEST.v6.i4.2022.351

Abstract: This work deals with the application of asymptotic iteration method in solving Black-Scholes equation. The assumptions are first relaxed followed by the derivation of the closed-form solutions of the general Black-Scholes equation under option pricing for constant interest rate, the case where the interest rate is a linear function and where the interest rate is a reciprocal function..

Key words Asymptotic iteration method, Black-Scholes equation, option pricing, interest rates.

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[2]. Merton, R.C. (1973). Theory of Rational Option Pricing. Bell Journal of Economics and Management Science. The RAND Corporation. 4(1): 141-183.
[3]. Han, H. & Wu, X. (2003). A Fast Numerical Method for the Black-Scholes Equation of American Options. SIAM J. Numer. Anal., 41(6).
[4]. Ehrhardt, M. & Mickens, R. (2008). Fast, Stable and Accurate Method for the Black-Scholes Equation of American Options. International Journal of Theoretical and Applied Finance 11(5), 471-501.
[5]. Jeong, D., Kim, J., & Wee, I. (2009). An Accurate and Efficient Numerical Method for Black-Scholes Equations. Commun. Korean Math. Soc. 24(4), 617-628.