The goal beyond this Research is to review methods that used to estimate Logistic distribution parameters. An exact estimators method which is the Moment method, compared with other approximate estimators obtained essentially from White approach such as: OLS, Ridge, and Adjusted Ridge as a suggested one to be applied with this distribution. The Results of all those methods are based on Simulation experiment, with different models and variety of  sample sizes. The comparison had been made with respect to two criteria: Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE).  

In this paper, author’s study sub diffusion bio heat transfer model and developed explicit finite difference scheme for time fractional sub diffusion bio heat transfer equation by using caputo fabrizio fractional derivative. Also discussed conditional stability and convergence of developed scheme. Furthermore numerical solution of time fractional sub diffusion bio heat transfer equation is obtained and it is represented graphically by Python.

In this work, an analytical approximation solution is presented, as well as a comparison of the Variational Iteration Adomian Decomposition Method (VIADM) and the Modified Sumudu Transform Adomian Decomposition Method (M STADM), both of which are capable of solving nonlinear partial differential equations (NPDEs) such as nonhomogeneous Kertewege-de Vries (kdv) problems and the nonlinear Klein-Gordon. The results demonstrate the solution’s dependability and excellent accuracy.

The goal beyond this Research is to review methods that used to estimate Logistic distribution parameters. An exact estimators method which is the Moment method, compared with other approximate estimators obtained essentially from White approach such as: OLS, Ridge, and Adjusted Ridge as a suggested one to be applied with this distribution. The Results of all those methods are based on Simulation experiment, with different models and variety of sample sizes. The comparison had been made with respect to two criteria: Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE).

This paper investigates an effective computational method (ECM) based on the standard polynomials used to solve some nonlinear initial and boundary value problems appeared in engineering and applied sciences. Moreover, the effective computational methods in this paper were improved by suitable orthogonal base functions, especially the Chebyshev, Bernoulli, and Laguerre polynomials, to obtain novel approximate solutions for some nonlinear problems. These base functions enable the nonlinear problem to be effectively converted into a nonlinear algebraic system of equations, which are then solved using Mathematica®12. The improved effective computational methods (I-ECMs) have been implemented to solve three applications involving nonli

In this paper, an approximate solution of nonlinear two points boundary variational problem is presented. Boubaker polynomials have been utilized to reduce these problems into quadratic programming problem. The convergence of this polynomial has been verified; also different numerical examples were given to show the applicability and validity of this method.

There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that