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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
Fri Jan 27 2012
Journal Name
Journal Of Huazhong University Of Science And Technology [medical Sciences]
An in vitro investigation into retention strength and fatigue resistance of various designs of tooth/implant supported overdentures
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Publication Date
Mon Nov 11 2019
Journal Name
Day 3 Wed, November 13, 2019
Drill Bit Selection Optimization Based on Rate of Penetration: Application of Artificial Neural Networks and Genetic Algorithms
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Abstract<p>The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the</p> ... Show More
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Publication Date
Wed Aug 15 2018
Journal Name
Al-khwarizmi Engineering Journal
Experimental and Simulation investigations of Micro Flexible Deep Drawing Using Floating Ring Technique
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Micro metal forming has an application potential in different industrial fields. Flexible tool-assisted sheet metal forming at micro scale is among the forming techniques that have increasingly attracted wide attention of researchers. This forming process is a suitable technique for producing micro components because of its inexpensive process, high quality products and relatively high production rate. This study presents a novel micro deep drawing technique through using floating ring as an assistant die with flexible pad as a main die. The floating ring designed with specified geometry is located between the process workpiece and the rubber pad. The function of the floating ring in this work is to produce SS304 micro cups with profile

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Publication Date
Sun Jan 01 2017
Journal Name
Green Chemistry
Dissolution of pyrite and other Fe–S–As minerals using deep eutectic solvents
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Processing sulfur containing minerals is one of the biggest sources of acute anthropogenic pollution particularly in the form of acid mine drainage.

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
DYNAMIC MODELING OF TIME-VARYING ESTIMATION FOR DISCRETE SURVIVAL ANALYSIS FOR DIALYSIS PATIENTS IN BASRAH, IRAQ
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Survival analysis is widely applied to data that described by the length of time until the occurrence of an event under interest such as death or other important events. The purpose of this paper is to use the dynamic methodology which provides a flexible method, especially in the analysis of discrete survival time, to estimate the effect of covariate variables through time in the survival analysis on dialysis patients with kidney failure until death occurs. Where the estimations process is completely based on the Bayes approach by using two estimation methods: the maximum A Posterior (MAP) involved with Iteratively Weighted Kalman Filter Smoothing (IWKFS) and in combination with the Expectation Maximization (EM) algorithm. While the other

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Publication Date
Thu Dec 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
The relationship between oil revenues and financial sustainability in the Iraq analysis for the period (1990-2013)
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This study aimed to analyze and measure the relationship between oil revenues and financial sustainability in Iraq, the study used the stylistic approach inductive and deductive approach. Accompanied by the use of quantitative and analytical style, which was based on two variables oil revenues and net general budget on annual data covered the period (1990-2013). Among the most important findings of the study contain the time-series variables study on the root of the unit and is not stable in the general level, and become stable after the use of mathematical processors to gain access to a stable by taking the first difference of natural Ogartm of the series. The way (Johnson) to a long-term relationship between oil revenues and ne

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Publication Date
Mon Sep 01 2014
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Analysis of the relationship between inflation and the tax revenue in Iraq for the period 1990-2010
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The Iraqi economy has suffered for a long period of inflation because of the Iraq war and the resolutions and the sanctions that were imposed on Iraq, this phenomenon overshadowed at various aspects of the economy including the tax revenue that the State seeks to optimize the total income for the budget, the research covers the years 1990-2010, these years have been divided according to the country's economic variables.

The research  adopted on econometrics analysis that is based on the information and data available on topics and has been using statistical methods to test functions are formulated.

Research concluded that rates of inflation and GDP impact is limited to direct taxation and indirect in current prices a

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Publication Date
Tue Sep 07 2021
Journal Name
Journal Of Petroleum Exploration And Production Technology
Wellbore instability management using geomechanical modeling and wellbore stability analysis for Zubair shale formation in Southern Iraq
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Abstract<p>Wellbore instability problems cause nonproductive time, especially during drilling operations in the shale formations. These problems include stuck pipe, caving, lost circulation, and the tight hole, requiring more time to treat and therefore additional costs. The extensive hole collapse problem is considered one of the main challenges experienced when drilling in the Zubair shale formation. In turn, it is caused by nonproductive time and increasing well drilling expenditure. In this study, geomechanical modeling was used to determine a suitable mud weight window to overpass these problems and improve drilling performance for well development. Three failure criteria, including Mohr–Coulomb, modifie</p> ... Show More
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Publication Date
Fri Dec 01 2017
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Integration between value chain analysis and balanced scorecard to for performance evaluation
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The performance evaluation process requires a set of criteria and for the purpose of measuring the level of performance achieved by the Unit and the actual level of development of its activities, and in view of the changes and of rapid and continuous variables surrounding the Performance is a reflection of the unit's ability to achieve its objectives, as these units are designed to achieve the objectives of exploiting a range of economic resources available to it, and the performance evaluation process is a form of censorship, focusing on the analysis of the results obtained from the achievement All its activities with a view to determining the extent to which the Unit has achieved its objectives using the resources available to it and h

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