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bsj-9740
Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge
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Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of Alzheimer's disease. The system employs MRI and feature extraction methods to categorize images. This paper adopts the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset includes functional MRI and Positron-Version Tomography scans for Alzheimer's patient identification, which were produced for people with Alzheimer's as well as typical individuals. The proposed technique uses MRI brain scans to discover and categorize traits utilizing the Histogram Features Extraction (HFE) technique to be combined with the Canny edge to representing the input image of the Convolutional Neural Networks (CNN) classification. This strategy keeps track of their instances of gradient orientation in an image. The experimental result provided an accuracy of 97.7% for classifying ADNI images.

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Publication Date
Wed Jan 01 2025
Journal Name
Journal Of Computer Sciences And Informatics
Edge Detection Methods: A Review
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This article studies a comprehensive methods of edge detection and algorithms in digital images which is reflected a basic process in the field of image processing and analysis. The purpose of edge detection technique is discovering the borders that distinct diverse areas of an image, which donates to refining the understanding of the image contents and extracting structural information. The article starts by clarifying the idea of an edge and its importance in image analysis and studying the most noticeable edge detection methods utilized in this field, (e.g. Sobel, Prewitt, and Canny filters), besides other schemes based on distinguishing unexpected modifications in light intensity and color gradation. The research as well discuss

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Publication Date
Sun Sep 06 2009
Journal Name
Baghdad Science Journal
Cultivation of Entamoeba histolytica in vitro and diagnose the bacterial growths in culture media
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The parasite was isolated from a stool sample, cultivated and maintained in vitro using Locke-egg medium (LEM) and Liver infusion agar medium (LIAM) . The culture was maintained for up to 21 months, and the best time to maintain the parasite was every 48 hours, although the growth in the culture media continued for 13 days without a maintenance. Additionally, no cyst formation was observed during cultivation of parasite in the two culture media. Although, was observe young cyst formed in LEM media were deletion of maintained. The diagnosis of bacteria growth in the culture media, bacterial content (Escherichia coli) was an dominance and essential requirement for a successful cultivation of Entamoeba histolytica in the two culture media.

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Publication Date
Tue Sep 01 2020
Journal Name
Journal Of Engineering
Identify and Diagnose the Causes of Financial Funding using the Root Cause Analysis Technique
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The analysis of the root cause techniques is a reasonable option to be made to assess the root causes of the funding of construction projects. There are a variety of issues related to financing in construction industries in Iraq. The root,cause analysis is the impact of security and social conditions on financial funding. Variety tools of root cause analysis have originated from literature, as common methods for the detection of root causes. The purpose of this study was to identify and diagnose causes that lead to obstruction of financial funding in the construction projects in the republic of Iraq from the contractors' point of view and their interaction with a number of variables. The study diagnosed nine causes of fi

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Publication Date
Fri Jan 01 2021
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
BotDetectorFW: an optimized botnet detection framework based on five features-distance measures supported by comparisons of four machine learning classifiers using CICIDS2017 dataset
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<p><span>A Botnet is one of many attacks that can execute malicious tasks and develop continuously. Therefore, current research introduces a comparison framework, called BotDetectorFW, with classification and complexity improvements for the detection of Botnet attack using CICIDS2017 dataset. It is a free online dataset consist of several attacks with high-dimensions features. The process of feature selection is a significant step to obtain the least features by eliminating irrelated features and consequently reduces the detection time. This process implemented inside BotDetectorFW using two steps; data clustering and five distance measure formulas (cosine, dice, driver &amp; kroeber, overlap, and pearson correlation

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Publication Date
Sat Apr 01 2023
Journal Name
Baghdad Science Journal
Carbon Nanotubes: Synthesis via Flame Fragment Deposition (FFD) Method from Liquefied Petroleum Gas
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The current study uses the flame fragment deposition (FFD) method to synthesize carbon nanotubes (CNTs) from Iraqi liquefied petroleum gas (LPG), which is used as a carbon source. To carry out the synthesis steps, a homemade reactor was used. To eliminate amorphous impurities, the CNTs were sonicated in a 30 percent hydrogen peroxide (H2O2) solution at ambient temperature. To remove the polycyclic aromatic hydrocarbons (PAHs) generated during LPG combustion, sonication in an acetone bath is used. The produced products were investigated and compared with standard Multi-walled carbon nanotube MWCNTs (95%), Sigma, Aldrich, using X-ray diffraction (XRD), thermo gravimetric analysis (TGA), Raman spectroscopy, scanning el

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Publication Date
Thu Oct 01 2020
Journal Name
Biochemical & Cellular Archives
EXTRACTING PECTIN FROM TOMATOES AND BEET PEELS AND USING IT IN THE MANUFACTURE OF YOGURT AND COMPARING THEM
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Pectin is available in many plants and in this study, the peels of tomatoes and beet were used to be an economical source of pectin production instead of dumping it with waste or using it as animal feed. The pectin extracted from the peels using different solutions, namely citric acid (2 M), oxalic acid (2%) and hydrochloric acid (0.5 M) the outcome of the extraction methods, 7. 1%, 6% and 11% respectively for tomatoes peels, while the pectin of beet peels were 8%, 6.5%, and 8.3%, and the highest percentage obtained in the manner of hydrochloric acid adopted in the manufacture of yogurt.Yogurt was manufactured with four treatments, in the first treatment standard pectin was added and the second treatment in addition to the pectin extracted

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Publication Date
Tue Sep 10 2019
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
A classification model on tumor cancer disease based mutual information and firefly algorithm
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Publication Date
Tue Jan 31 2023
Journal Name
Journal Of Legal Sciences
Compensation for Psychological Damage Caused by Negligence - A Comparative Study
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Psychological damage is one of the damages that can be compensated under the fault of negligence in the framework of English law, where the latter intends to include an enumeration of civil errors on the basis of which liability can be determined, and aims under each of these errors to protect a specific interest (for example, defamation protects Among the damage to reputation and inconvenience are the rights contained on the land), and the same is true for the rest of the other errors. Compensation for psychological damage resulting from negligence has raised problems in cases where the psychological injury is "pure", that is, those that are not accompanied by a physical injury, which required subjecting them to special requirements by the

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Engineering
Water Quality Assessment and Total Dissolved Solids Prediction using Artificial Neural Network in Al-Hawizeh Marsh South of Iraq
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The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The develope

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Publication Date
Thu Aug 01 2024
Journal Name
Water Practice &amp; Technology
Artificial neural network and response surface methodology for modeling oil content in produced water from an Iraqi oil field
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ABSTRACT<p>The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value &lt;0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, tempe</p> ... Show More
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