During the period 1970-2006 the Marshes-land region was exposed to several change into many sides of vanishing steeling and growth as follow :
1- Drying this region after 1991,and re-flooded again during 2003. Many other large Marsh-land, like al-Hawaisa was dried by cutting off inlet-water fed from Iran sources .As asqunance there are transferred to different kinds of swamps ,followed by major change in the structure of the region as the rural settlement concerned. Rural settlement in this area starts new push of migration. The research did not take that in consideration in this thesis because some others took it in details in some thesis and researches. The situation of Marsh-land is getting anew face after those major changes which need an urgent action to over-come this issue . In this thesis the researcher focus on finding the relative important of variable affect those changes of rural settlement in the study area . Settlement this region was a parted tow kind :First settlement flooded big Marsh-land water while the second group of settlements found on the off shore of marsh-land .Beside that a remarkable change in (the second group).
Socio economic and environmental factors distinguished between the two groups which attract rural settlement of in the flooded area of Marsh-land were translated gradually to the second group ( on the off shore side ) . Both of those group were utile the advantage of the second group of settlement (off shore group ) were attracting the other sectored in the floodedarea for many reason :ie : Saw shore economic and environmental condition plus the high precipitins of all kind of serves, social and technical infrastructure . Researcher adept descriptive and quantities analysis for all variable collected by questioner for selective sample of household with personal interview and inventory. The thesis found very remarkable circulation and recommendation relatively given to

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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
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