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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 Jan 01 2023
Journal Name
Revista Iberoamericana De PsicologÍa Del Ejercicio Y El Deporte Vol. 18 No 1 Pp. 117-121
THE EFFECT OF SPECIAL EXERCISES ACCORDING TO THE DIFFERENTIATED TEACHING METHOD ON MENTAL MOTIVATION AND LEARNING THE SKILLS OF BASKETBALL AND SHOOTING FOR FEMALE STUDENTS
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Publication Date
Mon Jun 15 2026
Journal Name
Wasit Journal Of Sports Sciences
The effect of RTX traning and plastic hurdles on some kinematic variables and learning the performance of the event of 100 mH for female students
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Publication Date
Sat Mar 30 2019
Journal Name
Journal Of Pure And Applied Microbiology
Antibiotic Resistance of Isolated Gram Negative Bacilli from Different Clinical Sample in a Central Teaching Hospital of Pediatric in Baghdad
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The number of infections caused by microorganisms is increasing significantly over the last few years. A total of 140 patients admitted to the central teaching hospital of pediatrics from the 1st of Jun 2017 to 31 October 2017. The Clinical samples was processed from culture and sensitivity testing. Antibiotic discs used for gram negative isolates. The most prevalent gram negative isolates included Escherichia coli 63 (45.0 %), Pseudomonas spp. 21 (15.0 %), Klebsiella spp. 19 (13.6 %) predominantly. Escherichia coli were the most prevalent isolates from urine 45 (71.4 %), Klebsiella spp. 11 (57.9 %) and Enterobacter spp. 11 (68.8 %) followed by Escherichia coli 10 (15.9 %) predominant from blood. 68 (48.6 %) of specimens were urine, 47 (33.

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Publication Date
Tue Jun 01 2021
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
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Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

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Publication Date
Mon Oct 01 2012
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
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This paper deals with prediction the effect of soil re-moulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil re-moulding due to actual pile driving. The re

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Publication Date
Sat Oct 06 2012
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
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Publication Date
Mon Dec 02 2024
Journal Name
Engineering, Technology & Applied Science Research
An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength
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This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

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Publication Date
Sat Jul 22 2023
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
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This paper deals with prediction the effect of soil remoulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity
according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil remoulding due to actual pile driving. T

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Applied Engineering Science
Rutting prediction of hot mix asphalt mixtures reinforced by ceramic fibers
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One of the most severe problems with flexible asphalt pavements is permanent deformation in the form of rutting. Accordingly, the practice of adding fiber elements to asphalt mix to improve performance under dynamic loading has grown significantly in order to prevent rutting distress and ensure a safe and long-lasting road surface. This paper explores the effects of a combination of ceramic fiber (CF), a low-cost, easily available mineral fiber, and thermal insulator fiber reinforced to enhance the Marshall properties and increase the rutting resistance of asphalt mixes at high temperatures. Asphalt mixtures with 0%, 0.75%, 1.5%, and 2.25% CF content were prepared, and Marshall stability and wheel tracking tests were employed to stu

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Publication Date
Sun Jun 12 2011
Journal Name
Baghdad Science Journal
Analysis of grape fruits and grape seed for their major , minor and trace elemental contact by XRF technique
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Grapes and grape seeds are important samples employed for environmental medical studies . The air of this work was to identify and concentration calculation of the elements in grapes fruit and thier seeds by using X-Ray fluoresces technique (XRF) . Samples were collected from Abo Ghraib of Baghdad city ,the grape seeds were obtained from those samples . Both samples were taken under experimental procedure to obtain the sample which were ready for analysis . The samples were then submitted to experimental conditions using a radiation source and then samples were applied for counting analysis shows the elements Na , Mg , Al , Si , P , S , Cl , K , Ca , and Sr as major components of the samples. Fe , Sr , I , Ba and V were

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