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jcoeduw-1355
Spatial Analysis of Soil Characteristics and its Effect on Determining the Susceptibility of lands of the RasheedRegion: A Study in Soil Geography
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Twelve pends were selected and distributed on three verticals transects paths on the Tigers river in Al Rasheed county.Passing through land covers, that classified and covers the whole region. Based on the 8 Landsat of the year 2015. It was oriental classified by using Erdas 10.2 . The pedons were distributed on the area of each varicty of these classes. the series of soil according of the transect series (DW74,MMg,DMu6 , Df96) respectively were represented P1 , P2 , P3 , P4  .

The second transits series(DM97,MM5,DM96,DF115) respectively were  represented P5 , P6 , P7 , P8  .The third  transits series(DM46,MMg,MF12,MM11) respectively were  represented P9 , P10 , P11 , P12  .The highest variation was the salinity (Ec) Electrical conductivity and the value of coefficient of variance c.v (112.2) and the lowest variation was for (Ph) soil reaction and its value of c.v (3.26).The land of the study area was classified into four classes of capability according to the USA classification of land capability classification (1960) Class I , Class II , Class III , Class IV . The largest area was the third class with (19672)ha . and the lowest area of the first class was (5224)ha , It was found that the most important determinates in subclass capability is the problem of salinity which was highly , and the watertable of Imperfectly drained type . The Capability Units category included internal drainage,W3 , Salinity , C3 and C2 .

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Publication Date
Thu Jul 02 2026
Journal Name
Innovative Construction And Petrochemical Technologies
Improving Arabic Text Classification Accuracy Using Lightweight NLP Techniques
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Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo

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