Fractal geometry is receiving increase attention as a quantitative and qualitative model for natural phenomena description, which can establish an active classification technique when applied on satellite images. In this paper, a satellite image is used which was taken by Quick Bird that contains different visible classes. After pre-processing, this image passes through two stages: segmentation and classification. The segmentation carried out by hybrid two methods used to produce effective results; the two methods are Quadtree method that operated inside Horizontal-Vertical method. The hybrid method is segmented the image into two rectangular blocks, either horizontally or vertically depending on spectral uniformity criterion; otherwise the block is segmented by the quadtree. Then, supervised classification is carried out by means the Fractal Dimension. For each block in the image, the Fractal Dimension was determined and used to classify the target part of image. The supervised classification process delivered five deferent classes were clearly appeared in the target part of image. The supervised classification produced about 97% classification score, which ensures that the adopted fractal feature was able to recognize different classes found in the image with high accuracy level.
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Accuracy in multiple objects segmentation using geometric deformable models sometimes is not achieved for reasons relating to a number of parameters. In this research, we will study the effect of changing the parameters values on the work of the geometric deformable model and define their efficient values, as well as finding out the relations that link these parameters with each other, by depending on different case studies including multiple objects different in spacing, colors, and illumination. For specific ranges of parameters values the segmentation results are found good, where the success of the work of geometric deformable models has been limited within certain limits to the values of these parameters.
In this research we study one of the pollutants(Gelbstoff ) such as Humic and Fulvic Acids in tap waters by using the technique of Raman, Flora to some regions of Baghdad , the results appear that the tap waters were pollutants which know yellow substance or Gelbstoff instant of suspending waters, which appear through the scattering of the incident light to same the wave length of Raman ,also calculate Raman shift which was 3640 cm-1 and force constant to band (O – H ) was 743 N/m, where the peak of Raman was at the wave length 441 nm after used the excitation wave length 380 nm . The results were in an agreement with lectures [8][9][10].
This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance
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Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.
Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are
... Show MoreSupport vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa
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Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.
In this paper we have been focus for the comparison between three forms for classification data belongs
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