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Deep Learning in Genomic Sequencing: Advanced Algorithms for HIV/AIDS Strain Prediction and Drug Resistance Analysis
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Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and identifying variants. A patient's HIV strain can be classified as susceptible or resistant to 17 different treatments. The FGD-MCNN transforms DNA genotype and HIV data into mathematical metrics, providing valuable insights into treatment-resistant HIV strains through pooling analysis. With remarkable accuracy, the FGD-MCNN deep learning system predicts HIV medication resistance using behavioral and genome-wide data from the HIV database. DNA patterns can be classified as resistant or susceptible by 17 antiretroviral drugs, providing valuable information for treatment planning and medical judgment. The model's parameter values illustrate the connections between neurons and the complex webs observed in the data have been examined. This study improves treatment effectiveness and expands the knowledge of HIV/AIDS.

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Publication Date
Sun Aug 01 2021
Journal Name
Journal Of Composites For Construction
Prediction of Concrete Cover Separation in Reinforced Concrete Beams Strengthened with FRP
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Publication Date
Sun Dec 01 2013
Journal Name
Journal Of Al-nahrain University
Spectrophotometric Determination of Ketotifen Drug in Pure form and Pharmaceutical Preparation Using Bromothymol Blue Via Ion-Pair Formation
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Publication Date
Fri Jul 01 2022
Journal Name
Journal Of Molecular Structure
Interaction behavior of curcumin encapsulated onto functionalized SBA-15 as an efficient carrier and release in drug delivery
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In this work, mesoporous silica SBA-15 was prepared and functionalized with amine groups (i.e., NH2) to form NH2/SBA-15. The curcumin (CUR) was encapsulated into the surface and pore of NH2/SBA-15 to create CUR@NH2/SBA-15 as an efficient carrier in drug delivery systems (DDSs). The three samples (i.e., SBA-15, NH2/SBA-15, and CUR@NH2/SBA-15) were characterized. The study investigated the effect of the carrier dose, initial CUR concentration, pH, and contact time on the CUR loading efficiency (DLE%) via adsorption. The best DLE% for the SBA-15 and NH2/SBA-15 were found to be 45% and 89.7%, respectively. The Langmuir isotherm had a greater correlation coefficient (R2) of 0.998 for SBA-15. A pseudo-secondorder kinetic model seemed to fit well

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Publication Date
Sun Dec 22 2019
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Evaluation of Knowledge, Attitudes and Experience of off-label Drug Prescribing Practice among Physicians in Baghdad City Hospitals
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Abstract

The present study aims to assess the knowledge, attitude, and experience of off-label prescribing practice among physicians in Baghdad city hospitals. This cross-sectional study was performed through the period from November 1st 2018 to March 2019 at 17 hospitals, a self-administered questionnaire was utilized to collect data from the physicians, and the targeted hospitals were randomly selected at different regions in Baghdad City area. Out of the 400 distributed questionnaires to the physicians, 383 of them were returned completed, 57.2% indicated that they were reasonably familiar with the term “off label drug”, 57.7% mentioned that the most common medical reasons for the prescribing o

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Publication Date
Wed Oct 02 2024
Journal Name
International Development Planning Review
DESIGNING AN AUXILIARY DEVICE AND ITS IMPACT ON LEARNING THE SKILLS OF ANGULAR SUPPORT AND OPEN SUPPORT FOR HANDSTAND PUSH-UPS ON THE PARALLEL APPARATUS IN ARTISTIC GYMNASTICS FOR BUDS
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Publication Date
Wed Jan 01 2014
Journal Name
Journal Of Engineering Geology And Hydrogeology
Assessing Pullout Resistance of Earth Reinforced Embankment Model
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- The sandy soil with high gypsum content (usually referred to as gypseous soil) covers vast area in south, east, middle and west regions of Iraq, such soil possess a type of cohesive forces when attached with optimum amount of water, then compacted and allowed to cure, but losses its strength when flooded with water again. Much work on earth reinforcement was published which concentrate on the gain in bearing capacity in the reinforced layer using different types of cohesive or cohesion less soil and various types of reinforcement such as plastic, metal, grids, and synthetic textile. Little attention was paid to there enforce gypseous soil. The objective of this work is to study the interaction between such soil and reinforcement strips

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Publication Date
Sun Mar 06 2011
Journal Name
Baghdad Science Journal
Laser Effect on Fatique Resistance of Carbon Steel
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Laser beam has been widely used to improve the mechanical properties of the metals. It used for cutting, drilling, hardening, welding……etc. The use of Laser beam has many features in accuracy and speeding in work, also in the treatment of metals locally, and in the places that is hard to reach by traditional ways. In this research a surface treatment was done to medium carbon steel (0.4%C) which is common kind of steel that is used in industry. Pulsing Neodymium -YAG Laser has been used and 1.06 micrometer wave length and 5 msec and the distance is about 30 centimeter between the exit area of the Laser beam from the system and the piece that treated . We are going to check the fatigue resistance for samples that is

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Publication Date
Wed Jun 01 2022
Journal Name
Journal Of Sport Science Technology And Physical Activities
The effect of using Daniel's model and Driver’s model in learning a kinetic chain on the uneven bars in the artistic gymnastics for female students
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The aim of this study to identity using Daniel's model and Driver’s model in learning a kinetic chain on the uneven bars in the artistic gymnastics for female students. The researchers used the experimental method to design equivalent groups with a preand post-test, and the research community was identified with the students of the third stage in the college for the academic year 2020-2021 .The subject was, (3) class were randomly selected, so (30) students distributed into (3) groups). has been conducted pretesting after implementation of the curriculum for (4) weeks and used the statistical bag of social sciences(SPSS)to process the results of the research and a set of conclusions was reached, the most important of which is t

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Publication Date
Sat Apr 15 2023
Journal Name
Journal Of Robotics
A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

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Publication Date
Sat Apr 15 2023
Journal Name
Journal Of Robotics
A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

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Scopus (6)
Crossref (4)
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