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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
Sat Jan 01 2022
Journal Name
Turkish Journal Of Physiotherapy And Rehabilitation
classification coco dataset using machine learning algorithms
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In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

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