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On the Performance of Metamaterial based Printed Circuit Antenna for Blood Glucose Level Sensing Applications: A Case Study
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Due to the urgent need to develop technologies for continuous glucose monitoring in diabetes individuals, poten tial research has been applied by invoking the microwave tech niques. Therefore, this work presents a novel technique based on a single port microwave circuit, antenna structure, based on Metamaterial (MTM) transmission line defected patch for sensing the blood glucose level in noninvasive process. For that, the proposed antenna is invoked to measure the blood glu cose through the field leakages penetrated to the human blood through the skin. The proposed sensor is constructed from a closed loop connected to an interdigital capacitor to magnify the electric field fringing at the patch center. The proposed an tenna sensor is found to operate excellently at the first mode, 0.6GHz, with S11 impedance matching less than -10dB. The proposed sensor performance is tested experimentally with 15 cases, different patients, through measuring the change in the S11 spectra after direct touching to the sensor a finger print of a patient. The proposed sensor is found to be effectively very efficient detector for blood glucose variation with a low manu facturing cost when printed on an FR-4 substrate. The experi mental measurements are analyzed mathematically to obtain the calibration equation of the sensor from the curve fitting. Finally, the theoretical and the experimental results are found to be agreed very well with a percentage of error less than 10%.

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Publication Date
Sat Oct 30 2021
Journal Name
Iraqi Journal Of Science
The Effects of Conductance on Metastable Switches in Memristive Devices Based on Anti-Hebbian and Hebbian (AHaH) Learning Rules
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     In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather t

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
Rock facies classification and its effect on the estimation of original oil in place based on petrophysical properties data
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The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri

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Publication Date
Sat Apr 30 2022
Journal Name
Revue D'intelligence Artificielle
Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

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Publication Date
Sat May 29 2021
Journal Name
Journal Of Legal And Political Thought
The Possibility of Compensation for the Loss of View Caused by Public Projects – A Legal Study in Light of the Approach of U.S. Courts
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Despite the great economic and commercial importance given to real estate by virtue of its view of the landscape or public roads, US courts have differed in their position on compensation for damages resulting from blocking that view or vision by public projects. Some courts compensated for such damages, other courts approved such compensation. Hence, this research came to shed light on the extent of the possibility of compensation for blocking the view or vision as a result of public projects, and the research has supported us with many judicial decisions.

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Educational And Psychological Researches
Mental-math level of intermediate school students
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The study seeks to explore the mental-math level among intermediate-school students. To this end, the researcher raised a question. What the level of mental math among students in the intermediate stage? Total of (576) male and female students were chosen from different schools at Al-karkh side \ general directorate of Baghdad education. So as to answer research question and hypothesis, a questionnaire of (30) items designed by the authors was administered to the study sample. T-test was used to analyze the collected data at the level of significance (0.05). Expectancy means (15) showed significant difference comparing to the achieved mean (10, 10) on mental-math test. The study concluded with students has a low level of mental math. The

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Publication Date
Wed Dec 30 2015
Journal Name
Al-kindy College Medical Journal
Results and predictive factors for single level L4-5, and L5-S1 disc herniation surgery
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Background: The disc prolapse is a common condition especially in young adults. Different levels are affected in the lumber region; the L4/L5 disc is more susceptible to longitudinal load and is the most common site of lumbar disc prolapse. The L5/S1 disc is protected from torsion load by strong ilio-lumbar ligaments but it is more susceptible to axial compressive forces. Many factors affect the result and outcome of surgery in these levels.Objective: The aim of this study is to correlate operative data, short-term results, complications, and prognostic factors (age, gender, mobility, hospital stay, and level of pain) for one-level lumber discectomybetween different levels (L4–L5 vs. L5–S1).Methods In this prospective study, 32 patie

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Publication Date
Sun Feb 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
effect work stress in performance of employees In The general company for vegetable oil industry
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This research theme of the pressures of work , which is one of the important topics in order to recognize the reality of( influencing the pressures of work in the performance of employees in the General Company for Vegetable Oil Industry in Baghdad )through the statement of the existence of the correlation and influence whether or not the statement of the strength of this relationship and its impact in the case of its existence has been provided as part of my Search for variables and their removal in front of the Sub- scientific aspect has been the distribution of the questionnaire on a sample of( 62) people working in the company Mint distributors on several sections where.

Formed resolution of two sets

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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

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Publication Date
Mon Jan 01 2018
Journal Name
Matec Web Of Conferences
Assessing the performance of commercial Agisoft PhotoScan software to deliver reliable data for accurate3D modelling
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3D models delivered from digital photogrammetric techniques have massively increased and developed to meet the requirements of many applications. The reliability of these models is basically dependent on the data processing cycle and the adopted tool solution in addition to data quality. Agisoft PhotoScan is a professional image-based 3D modelling software, which seeks to create orderly, precise n 3D content from fixed images. It works with arbitrary images those qualified in both controlled and uncontrolled conditions. Following the recommendations of many users all around the globe, Agisoft PhotoScan, has become an important source to generate precise 3D data for different applications. How reliable is this data for accurate 3D mo

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Publication Date
Mon Nov 18 2024
Journal Name
Molecular Crystals And Liquid Crystals
Synthesis and liquid crystal properties of a new class of calamitic mesogens based on twin 1,3,4-thiadiazole derivatives with imine linkage
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