We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). Standard Deviation, Mean, Energy and Entropy are extorted using the histogram approach for each merger space. These features are found to be higher in occurrence in the tumor region than the non-tumor one. MRI scans of the five brains with 60 slices from each are utilized for testing the proposed method’s authenticity. These brain images (230 slices as normal and 70 abnormal) are accessed from the Internet Brain Segmentation Repository (IBSR) dataset. 60% images for training and 40% for testing phase are used. Average classification accuracy as much as 98.02% (training) and 98.19% (testing) are achieved.
Alongside the development of high-speed rail, rail flaw detection is of great importance to ensure railway safety, especially for improving the speed and load of the train. Several conventional inspection methods such as visual, acoustic, and electromagnetic inspection have been introduced in the past. However, these methods have several challenges in terms of detection speed and accuracy. Combined inspection methods have emerged as a promising approach to overcome these limitations. Nondestructive testing (NDT) techniques in conjunction with artificial intelligence approaches have tremendous potential and viability because it is highly possible to improve the detection accuracy which has been proven in various conventional nondestr
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The Mishrif Formation is one of the most important geological formations in Iraq consisting of limestone, marl, and shale layers since it is one of the main oil producing reservoirs in the country, which contain a significant portion of Iraq's oil reserves. The formation has been extensively explored and developed by the Iraqi government and international oil companies, with many oil fields being developed within it. The accurate evaluation of the Mishrif formation is key to the successful exploitation of this field. However, its geological complexity poses significant challenges for oil production, requiring advanced techniques to accurately evaluate its petrophysical properties.
This study used advanced well-logging analysi
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In cognitive radio system, the spectrum sensing has a major challenge in needing a sensing method, which has a high detection capability with reduced complexity. In this paper, a low-cost hybrid spectrum sensing method with an optimized detection performance based on energy and cyclostationary detectors is proposed. The method is designed such that at high signal-to-noise ratio SNR values, energy detector is used alone to perform the detection. At low SNR values, cyclostationary detector with reduced complexity may be employed to support the accurate detection. The complexity reduction is done in two ways: through reducing the number of sensing samples used in the autocorrelation process in the time domain and through using the Slid
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Abstract :- In this paper, silver nanoparticles had been prepared by chemical reduction method. Many tests had been done to it such as UV-Visible spectrophotometer, XRD, AFM&SEM test. finally an attempt had been done to get the optimum condition to control the grain size of silver Nanoparticles by variation the heating period and other parameters which has an effect in silver Nanoparticles synthesis process. in this method we can get a silver nanoparticles in the size range from 52 to 97 nm.
The removal of boron from aqueous solution was carried out by electrocoagulation (EC) using magnesium electrodes as anode and stainless steel electrodes as cathode. Several operating parameters on the removal efficiency of boron were investigated, such as initial pH, current density, initial boron ion concentration, NaCl concentration, spacing between electrodes, electrode material, and presence of carbonate concentration. The optimum removal efficiency of 91. 5 % was achieved at a current density of 3 mA/cm² and pH = 7 using (Mg/St. St. ) electrodes, within 45 min of operating time. The concentration of NaCl was o. 1 g/l with a 0.5cm spacing between the electrodes. First and second order rate equation were applied to study adsorp
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This study is dedicated to solving multicollinearity problem for the general linear model by using Ridge regression method. The basic formulation of this method and suggested forms for Ridge parameter is applied to the Gross Domestic Product data in Iraq. This data has normal distribution. The best linear regression model is obtained after solving multicollinearity problem with the suggesting of 10 k value.
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In this work, PAni nanofibers (NFs) are successfully synthesized via hydrothermal method. The structural, surface morphological, optical, electrical and H2S gas sensing properties have been investigated for PAni thin films deposited by spin coating technique. The XRD pattern reveals crystalline nature of PAni NFs with crystallite size of 9.2 nm. The SEM image of Polyaniline clearly indicates that the polymer possesses nanofiber like structure. The optical properties show that the optical energy gap follows allowed direct electronic transition calculated using Tauc’s equation. Intense hotoluminescence (PL) peaks at 309, 340 and 605 nm are observed. The electrical properties such as D.C. conductivity and Hall effect have been studied wher
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