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Detection of Polyomavirus BK and JC in kidney Transplant Recipients
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objectives: To investigate the polyomaviruses (BK, JC) in asymptomatic kidney transplant recipients and healthy persons as control. It is one of the first reports on serological detection and molecular characterization that describes the circulation of polyomaviruses (BKV, JCV) have been done in Iraq recently. Methodology: The present study was designed as prospective case control study was done during the period from November 2015 to August 2016. Total of 97 serum and urine samples were collected randomly from 25 healthy control person and 72 renal transplant recipients, attending Iraqi Renal Transplantation Society. “New experimental indirect ELISA kit" had been designed and synthesized manually for detection Anti-JCV IgM and IgG, Urea and creatinine level had been measured for total study groups for estimation of renal functions. Results: All of the samples were submitted to a Nested-PCR. A significantly difference had been seen (P < 0.01) of PyV was found in renal transplant patients 2(2.8%) in comparison to the control group 4(16.0%). JCV was detected in.6 (6.2%) samples (2 patients/ 4 controls).by conventional PCR, BKV was detected in 2(2.8%) of renal transplant group and not detected with any one of control group. result for IgM revealed positivity for 25(25.8%) out of 72cases of renal transplant subject and 8(8.2%) person out of 25control groups. for IgG the result show that 21(29.2%) out of 72 cases of renal transplant Subjects have a positive result for IgG but this antibody was not detected in the control. Recommendations: Success diagnosis required sensitive assay typically Nested-PCR to detect extremely low viral load present in potential kidney donor before access as a donor, Because of the potentially harmful effects of polyomavirus which may cause loss of the transplanted kidney

 
 

 

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Publication Date
Mon Jan 01 2018
Journal Name
Matec Web Of Conferences
Brain Tumour Detection using Fine-Tuning Mechanism for Magnetic Resonance Imaging
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In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

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Publication Date
Thu Apr 01 2021
Journal Name
Telkomnika (telecommunication Computing Electronics And Control)
Automatic human ear detection approach using modified adaptive search window technique
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Publication Date
Tue Sep 23 2025
Journal Name
Journal Of Plant Protection Research
Smart sprayer for weed control using an object detection algorithm (yolov5)
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Spraying pesticides is one of the most common procedures that is conducted to control pests. However, excessive use of these chemicals inversely affects the surrounding environments including the soil, plants, animals, and the operator itself. Therefore, researchers have been encouraged to...

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
GIS as A Tool for Expansive Soil Detection at Sulaymaniyah City
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Geotechnical engineering like any other engineering field has to develop and cope with new technologies. This article intends to investigate the spatial relationships between soil’s liquid limit (LL), plasticity index (PI) and Liquidity index (LI) for particular zones of Sulaymaniyah City. The main objective is to study the ability to produce digital soil maps for the study area and determine regions of high expansive soil. Inverse Distance Weighting (IDW) interpolation tool within the GIS (Geographic Information System) program was used to produce the maps. Data from 592 boreholes for LL and PI and 245 boreholes for LI were used for this study. Layers were allocated into three depth ranges (1 to 2, 2 to 4 and 4 to 6)

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Publication Date
Sat Jun 03 2023
Journal Name
Journal Of Electronic Materials
Thiophosgene Detection by Ag-Decorated AlN Nanotube: A Mechanical Quantum Survey
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The density functional B3LYP is used to investigate the effect of decorating the silver (Ag) atom on the sensing capability of an AlN nanotube (AlN-NT) in detecting thiophosgene (TP). There is a weak interaction between the pristine AlN-NT and TP with the sensing response (SR) of approximately 9.4. Decoration of the Ag atom into the structure of AlN-NT causes the adsorption energy of TP to decrease from − 6.2 to − 22.5 kcal/mol. Also, the corresponding SR increases significantly to 100.5. Moreover, the recovery time when TP is desorbed from the surface of the Ag-decorated AlN-NT (Ag@AlN-NT) is short, i.e., 24.9 s. The results show that Ag@AlN-NT can selectively detect TP among other gases, such as N2, O2, CO2, CO, and H2O.

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Publication Date
Tue Oct 01 2019
Journal Name
2019 International Conference On Electrical Engineering And Computer Science (icecos)
An Evolutionary Algorithm for Community Detection Using an Improved Mutation Operator
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Publication Date
Sat May 24 2025
Journal Name
Iraqi Journal For Computer Science And Mathematics
Intrusion Detection System for IoT Based on Modified Random Forest Algorithm
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An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
An Effective Hybrid Deep Neural Network for Arabic Fake News Detection
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Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

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Publication Date
Sat Oct 20 2018
Journal Name
Journal Of Economics And Administrative Sciences
Value added tax and its technical organization Lebanon Case Study
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Abstract

      Value Added Tax (VAT) is one of the most important types of indirect taxes because of its advantages in achieving financial, economic and financial objectives. The introduction of VAT is part of the reform of the structure of the Lebanese public tax system aimed at reducing the fiscal deficit and resulting inflation, which still lacks a general consumption tax. There is also an urgent need to increase treasury revenues , Because of its broad tax base, as it imposes on the consumption of locally produced and imported goods, in addition to the role played by this tax in support of the local product              &nbs

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Publication Date
Wed Dec 13 2017
Journal Name
Al-khwarizmi Engineering Journal
Digital Image Watermarking Using Arnold Scrambling and Berkeley Wavelet Transform
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Embedding an identifying data into digital media such as video, audio or image is known as digital watermarking. In this paper, a non-blind watermarking algorithm based on Berkeley Wavelet Transform is proposed. Firstly, the embedded image is scrambled by using Arnold transform for higher security, and then the embedding process is applied in transform domain of the host image. The experimental results show that this algorithm is invisible and has good robustness for some common image processing operations.

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