Transportation problems are considered as a type of operation research problems. In fact, they deal with scheduling transportation of goods from their source to delivery sites in the minimum cost.

Such problems can be solved by the available traditional methods, which include; North-West corner, Least cost and Vogel’s method. As well as if this transportation problem is considered as a linear program it can also be solved by using Simplex method

The goal of the present study is to compare different research methods to provide the optimal and minimum cost.

This study was applied to resolve a transportation problem related to land Transportation Company, w

Jurisprudential rules, a broad section to lower the legitimacy of the ruling on the developments of politics, which is increasing day by day, and developments of complex judicial issues, provided that the rule derived from the texts of the legitimate or verbal significance.
      And the introduction of jurisprudence rules in the provisions concerning the legitimate policy, the first to take from the provisions of the situation; because the rules of jurisprudence is a summary of the provisions reached by the scholars of the nation after study and scrutiny, which is qualified and largely to cover the need of the owners of public mandates.

In this paper we use Bernstein polynomials for deriving the modified Simpson's 3/8 , and the composite modified Simpson's 3/8 to solve one dimensional linear Volterra integral equations of the second kind , and we find that the solution computed by this procedure is very close to exact solution.

In this paper, the computational method (CM) based on the standard polynomials has been implemented to solve some nonlinear differential equations arising in engineering and applied sciences. Moreover, novel computational methods have been developed in this study by orthogonal base functions, namely Hermite, Legendre, and Bernstein polynomials. The nonlinear problem is successfully converted into a nonlinear algebraic system of equations, which are then solved by Mathematica®12. The developed computational methods (D-CMs) have been applied to solve three applications involving well-known nonlinear problems: the Darcy-Brinkman-Forchheimer equation, the Blasius equation, and the Falkner-Skan equation, and a comparison between the met

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par