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Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log
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Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is used to train the model, where the model prediction result is validated with core permeability. Seven oil well logs were used as input parameters, and the model was constructed with Techlog software. The predicted permeability with the model compared with Schlumberger-Doll-Research permeability as a cross plot, which results in the correlation coefficient of 94%, while the predicted permeability validated with the core permeability of the well, which obtains good agreement where R2 equals 80%. The model was utilized to forecast permeability in a well that did not have a nuclear magnetic resonance log, and the predicted permeability was cross-plotted against core permeability as a validation step, with a correlation coefficient of 77%. As a result, the low percentage of matching was due to data limitations, which demonstrated that as the amount of data used to train the model increased, so did the precision.

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Publication Date
Wed Jan 15 2003
Journal Name
كلية الترا ث الجامعة
Estimating an Exponentiated Expanded Power Function Distribution Using an Artificial Intelligence Algorithm
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The distribution of the expanded exponentiated power function EEPF with four parameters, was presented by the exponentiated expanded method using the expanded distribution of the power function, This method is characterized by obtaining a new distribution belonging to the exponential family, as we obtained the survival rate and failure rate function for this distribution, Some mathematical properties were found, then we used the developed least squares method to estimate the parameters using the genetic algorithm, and a Monte Carlo simulation study was conducted to evaluate the performance of estimations of possibility using the Genetic algorithm GA.

Publication Date
Fri Aug 30 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
Artificial Intelligence and Cybersecurity in Face Sale Contracts: Legal Issues and Frameworks
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The sale of facial features is a new modern contractual development that resulted from the fast transformations in technology, leading to legal, and ethical obligations. As the need rises for human faces to be used in robots, especially in relation to industries that necessitate direct human interaction, like hospitality and retail, the potential of Artificial Intelligence (AI) generated hyper realistic facial images poses legal and cybersecurity challenges. This paper examines the legal terrain that has developed in the sale of real and AI generated human facial features, and specifically the risks of identity fraud, data misuse and privacy violations. Deep learning (DL) algorithms are analyzed for their ability to detect AI genera

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Publication Date
Thu Jul 31 2025
Journal Name
مجلة واسط للعلوم الانسانية
Artificial Intelligence in English Language Education in Iraq: A Review study of Interventions and Perceptions
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AI in teaching English is reshaping language learning. While interest in AI-supported education is growing worldwide, research in this area is still emerging in Iraq. This review synthesizes empirical AI-based intervention studies to enhance English language learning in Iraqi higher education, and the perceptions of stakeholders regarding AI tools in language instruction. The reviewed intervention studies, comprising studies employed different AI platforms to support grammar instruction, speaking fluency, writing feedback, and pragmatic competence. These interventions yielded improvements in learners’ performance, motivation, and communicative confidence. In parallel, perception-focused studies revealed positive attitudes toward A

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Publication Date
Mon Oct 05 2020
Journal Name
International Journal Of Advanced Science And Technology
Improved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval
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Improved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval

Publication Date
Tue Jul 01 2025
Journal Name
Ain Shams Engineering Journal
Deep neural networks for speech enhancement and speech recognition: A systematic review
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Publication Date
Tue Jul 01 2025
Journal Name
Ain Shams Engineering Journal
Deep neural networks for speech enhancement and speech recognition: A systematic review
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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Effect of radial magnetic field on peristaltic transport of Jeffrey fluid in curved channel with heat / mass transfer
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Publication Date
Sat Mar 05 2022
Journal Name
Mathematical Methods In The Applied Sciences
Double‐diffusive convection on peristaltic flow of hyperbolic tangent nanofluid in non‐uniform channel with induced magnetic field
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Consequence of thermal and concentration convection on peristaltic pumping of hyperbolic tangent nanofluid in a non‐uniform channel and induced magnetic field is discussed in this article. The brief mathematical modeling, along with induced magnetic field, of hyperbolic tangent nanofluid is given. The governing equations are reduced to dimensionless form by using appropriate transformations. Exact solutions are calculated for temperature, nanoparticle volume fraction, and concentration. Numerical technique is manipulated to solve the highly non‐linear differential equations. The roll of different variables is graphically analyzed in terms of concentration, temperature, volume fraction of nanoparticles, axial induced magnetic fie

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Publication Date
Tue Dec 01 2020
Journal Name
Iraqi Journal Of Physics
Study The Influence of Doping Electric and Magnetic Nanoparticles on The Nonlinear Optical Properties of Nematic Liquid Crystals
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The nonlinear optical properties response of nematic liquid crystal (6CHBT) and the impact of doping with two kinds of nanoparticles; Fe3O4 magnetic nanoparticles and SbSI ferroelectric nanoparticles have been studied using the non-linear dynamic method through z-scan measurement technique. This was achieved utilizing CW He-Ne laser. The pure LC and magnetic LC nanoparticle composite samples had a maximum absorption while the ferroelectric LC nanoparticle composite had a minimum absorption of the incident light. The nonlinear refractive index was positive for the pure LC and the rod-like ferronematic LC composite samples, while it was negative for the ferroelectric LC composite. The studying of the nonlinear optical

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