Abstract

 This research deals will the declared production planning operation in the general company of planting oils, which have  great role in production operations management who had built mathematical model for correct non-linear programming according to discounting operation during raw materials or half-made materials purchasing operation which concentration of six main products by company but discount included just three products of raw materials, and there were six months taken from the 1^st half of 2014 as a planning period has been chosen . Simulated annealing algorithm  application on non-linear model which been more difficulty than possible solution when imposed restric

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

This research aims to know the effectiveness of teaching with a proposed strategy according to the common Knowledge construction modelin mathematical proficiency among students of the second middle class. The researchers adopted the method of the experimental approach, as the experimental design was used for two independent and equal groups with a post-test. The experiment was applied to a sample consisting of (83) students divided into two groups: an experimental comprising (42) students and a control group, the second comprising (41) students., from Badr Shaker Al-Sayyab Intermediate School for Boys, for the first semester of the academic year (2021-2022), the two groups were rewarded in four variables: (chronological age calculated in mo

Abstract

   Suffering the human because of pressure normal life of exposure to several types of heart disease as a result of due to different factors. Therefore, and in order to find out the case of a death whether or not, are to be modeled using binary logistic regression model

    In this research used, one of the most important models of nonlinear regression models extensive use in the modeling of applications statistical, in terms of heart disease which is the binary logistic regression model. and then estimating the parameters of this model using the statistical estimation methods, another problem will be appears in estimating its parameters, as well as when the numbe

Abstract

The traffic jams taking place in the cities of the Republic of Iraq in general and the province of Diwaniyah especially, causes return to the large numbers of the modern vehicles that have been imported in the last ten years and the lack of omission for old vehicles in the province, resulting in the accumulation of a large number of vehicles that exceed the capacity of the city's streets, all these reasons combined led to traffic congestion clear at the time of the beginning of work in the morning, So researchers chose local area network of the main roads of the province of Diwaniyah, which is considered the most important in terms of traffic congestion, it was identified  fuzzy numbers for

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi

Abstract

The population is sets of vocabulary common in character or characters and it’s study subject or research . statistically , this sets is called study population (or abridgement population ) such as set of person or trees of special kind of fruits or animals or product  any country for any commodity through infinite temporal period term ... etc.

The population maybe finite if we can enclose the number of its members such as the students of finite school grade . and maybe infinite if we can not enclose the number of it is members such as stars or aquatic creatures in the sea . when we study any character for population the statistical data is concentrate by two metho

In this paper, the error distribution function is estimated for the single index model by the empirical distribution function and the kernel distribution function. Refined minimum average variance estimation (RMAVE) method is used for estimating single index model. We use simulation experiments to compare the two estimation methods for error distribution function with different sample sizes, the results show that the kernel distribution function is better than the empirical distribution function.

This study aims to numerically simulate the flow of the salt wedge by using computational fluid dynamics, CFD. The accuracy of the numerical simulation model was assessed against published laboratory data. Twelve CFD model runs were conducted under the same laboratory conditions. The results showed that the propagation of the salt wedge is inversely proportional to the applied freshwater discharge and the bed slope of the flume.  The maximum propagation is obtained at the lowest discharge value and the minimum slope of the flume. The comparison between the published laboratory results and numerical simulation shows a good agreement. The range of the relative error varies between 0 and 16% with an average of 2% and a roo