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Classification of brain tumors using the multilayer perceptron artificial neural network
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Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect on how the network perform when predicting cases of brain tumor, contrast accounted for 64.3 %, correlation accounted for 56.7 %, and entropy accounted for 54.8 %. All remaining characteristics accounted for 21.3-46.8 % of normalized importance. The output of the neural networks showed that sensitivity and specificity were scored remarkably high level of probability as it approached % 96.

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Publication Date
Wed Dec 30 2020
Journal Name
Al-kindy College Medical Journal
Measurement of plasma fibrinogen and D-dimer in a sample of Iraqi patients with solid malignant tumors
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Background: Multifactor affect the pathogenesis of thrombosis in solid malignancy; however, a significant role is attributed to the cancer cells ability to interact with and activate the host hemostatic system. [1] 

Hemostasis is highly correlated to tumor growth, angiogenesis and metastasis, modulation of these pathways reflects interesting and promising treatment options in the future. [1]

Most patients with cancer frequently suffer from chronic compensated DIC and have abnormal laboratory coagulation tests without clinical manifestations of thrombosis, which is a subclinical hypercoagulable state that can be detected by varying degrees of activation of blood clotting. The results of laboratory tests in th

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Publication Date
Tue Mar 01 2022
Journal Name
International Journal Of Nonlinear Analysis And Applications
Semi-parametric regression function estimation for environmental pollution with measurement error using artificial flower pollination algorithm
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Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin

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Publication Date
Wed Mar 01 2023
Journal Name
Al-khwarizmi Engineering Journal
A Methodology for Evaluating and Scheduling Preventive Maintenance for a Thermo-Electric Unit Using Artificial Intelligence
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Flow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relativel

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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Publication Date
Sun Apr 23 2017
Journal Name
International Conference Of Reliable Information And Communication Technology
Classification of Arabic Writer Based on Clustering Techniques
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Arabic text categorization for pattern recognitions is challenging. We propose for the first time a novel holistic method based on clustering for classifying Arabic writer. The categorization is accomplished stage-wise. Firstly, these document images are sectioned into lines, words, and characters. Secondly, their structural and statistical features are obtained from sectioned portions. Thirdly, F-Measure is used to evaluate the performance of the extracted features and their combination in different linkage methods for each distance measures and different numbers of groups. Finally, experiments are conducted on the standard KHATT dataset of Arabic handwritten text comprised of varying samples from 1000 writers. The results in the generatio

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Publication Date
Wed Oct 04 2023
Journal Name
Environmental Progress & Sustainable Energy
Removal of <scp>E133</scp> brilliant blue dye from artificial wastewater by electrocoagulation using cans waste as electrodes
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Abstract<p>Solid‐waste management, particularly of aluminum (Al), is a challenge that is being confronted around the world. Therefore, it is valuable to explore methods that can minimize the exploitation of natural assets, such as recycling. In this study, using hazardous Al waste as the main electrodes in the electrocoagulation (EC) process for dye removal from wastewater was discussed. The EC process is considered to be one of the most efficient, promising, and cost‐effective ways of handling various toxic effluents. The effect of current density (10, 20, and 30 mA/cm<sup>2</sup>), electrolyte concentration (1 and 2 g/L), and initial concentration of Brilliant Blue dye (15 and 30 mg/L) on </p> ... Show More
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Publication Date
Wed May 01 2013
Journal Name
Ieee Journal Of Biomedical And Health Informatics
Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography
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Publication Date
Fri Mar 31 2023
Journal Name
Oncology And Radiotherapy
The Effect of Green Low Laser (LLL) on the white blood cells on platelet on people on brain and prostate cancer
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The effect of Low-Level Laser (LLL) provided by green semiconductor laser with an emission wavelength of 532 nm on of human blood of people with brain and prostate cancer has been investigated. The effect of LLL on white blood cell (WBC), NEUT, LYMPH and MONO have been considered. Platelet count (PLT) has also been considered in this work. 2 ml of blood sample were irradiating by a green laser of the dose of 4.8 J/cm2. The results suggest a potential effect of LLL on WBC, PLT, NEUT, LYMPH, and MONO of people with brain and prostate cancer Key words: white blood cell , platelet , low-level laser therapy

Publication Date
Wed Feb 22 2023
Journal Name
Iraqi Journal Of Science
Extraction Drainage Network for Lesser Zab River Basin from DEM using Model Builder in GIS
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ArcHydro is a model developed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). Digital Elevation Model (DEM) data have been used to derive hydrological features, which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions. Detailed delineation of drainage networks is the first step for many natural resource management studies. Compared with interpretation from aerial photographs or topographic maps, auto

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Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering
Transient Stability Enhancement and Critical Clearing Time Improvement for Kurdistan Region Network using Fact Configuration
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The Electrical power system has become vast and more complex, so it is subjected to sudden changes in load levels. Stability is an important concept which determines the stable operation of the power system. Transient stability analysis has become one of the significant studies in the power system to ensure the system stability to withstand a considerable disturbance. The effect of temporary occurrence can lead to malfunction of electronic control equipment. The application of flexible AC transmission systems (FACTS) devices in the transmission system have introduced several changes in the power system. These changes have a significant impact on the power system protection, due to differences inline impedance, line curre

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