This research shows the problem of the economic development of underdeveloped countries in an unconventional way, as these papers explain the problems of the economic development. This research not only reviews the problems, but it illustrates them in a philosophical way, basis of the data of modernity, this mean it is a process of connecting between the absence of the modernity values and the failure of development in underdeveloped countries. The Search follows the descriptive approach to get to the goal of search by four main axes. The first axis includes clarifying  modernity and its principles, the second axis includes clarifying the economic development , the third axis includes the features of the mod

تحليل الشراكات التجارية للعراق 2003-2013

Abstract                                                                             

The government spending in Iraq and witnessed the changes and developments, especially after 2003, which outweighed consumer spending at the expense of capital expenditure and increased support and diversity of trends towards improving pension conditions for member

This research study Blur groups (Fuzzy Sets) which is the perception of the most modern in the application in various practical and theoretical areas and in various fields of life, was addressed to the fuzzy random variable whose value is not real, but the numbers Millbh because it expresses the mysterious phenomena or uncertain with measurements are not assertive. Fuzzy data were presented for binocular test and analysis of variance method of random Fuzzy variables , where this method depends on a number of assumptions, which is a problem that prevents the use of this method in the case of non-realized.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Buckling analysis of composite laminates for critical thermal (uniform and linear) and mechanical loads is reported here. The objective of this work is to carry out theoretical investigation of buckling analysis of composite plates under thermomechanical loads, and experimental investigation under mechanical loads. The analytical investigation involved certain mathematical preliminaries, a study of equations of orthotropic elasticity for classical laminated plate theory (CLPT), higher order shear deformation plate theory (HSDT) , and numerical analysis (Finite element method), then the equation of motion are derived and solved using Navier method and Levy method for symmetric and anti-symmetric cross-ply and angle-ply laminated plates t

The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. T