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Distribution of New Horizontal Wells by the Use of Artificial Neural Network Algorithm
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Abstract<p>It is an established fact that substantial amounts of oil usually remain in a reservoir after primary and secondary processes. Therefore; there is an ongoing effort to sweep that remaining oil. Field optimization includes many techniques. Horizontal wells are one of the most motivating factors for field optimization. The selection of new horizontal wells must be accompanied with the right selection of the well locations. However, modeling horizontal well locations by a trial and error method is a time consuming method. Therefore; a method of Artificial Neural Network (ANN) has been employed which helps to predict the optimum performance via proposed new wells locations by incorporating reservoir properties and production data of previous wells.</p><p>This study used the Artificial Neural Network (ANN) that has been programmed in a manner to predict the cumulative oil produced for a certain grid by providing the corresponding properties of the grid. The network has been validated with real data collected from a number of drilled hypothetical wells. Furthermore; the validated network used to simulate the field parts that have not been drilled yet, to predict the corresponding cumulative oil for each grid. Field-scale simulation has been carried out and new horizontal wells have been allocated using the validated prepared data by the Artificial Neural Network Algorithm and an approved Iraqi reservoir model. Finally, different optimization scenarios have been investigated on the overall field recovery performance.</p>
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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Machine Learning And Computing
Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks
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Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

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Publication Date
Mon Jan 01 2024
Journal Name
Aims Mathematics
Solving quaternion nonsymmetric algebraic Riccati equations through zeroing neural networks
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<abstract><p>Many variations of the algebraic Riccati equation (ARE) have been used to study nonlinear system stability in the control domain in great detail. Taking the quaternion nonsymmetric ARE (QNARE) as a generalized version of ARE, the time-varying QNARE (TQNARE) is introduced. This brings us to the main objective of this work: finding the TQNARE solution. The zeroing neural network (ZNN) technique, which has demonstrated a high degree of effectiveness in handling time-varying problems, is used to do this. Specifically, the TQNARE can be solved using the high order ZNN (HZNN) design, which is a member of the family of ZNN models that correlate to hyperpower iterative techniques. As a result, a novel

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Publication Date
Sun Jun 07 2009
Journal Name
Baghdad Science Journal
New Formulas of Special Singular Matrices
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Many of the elementary transformations of determinants which are used in their evaluation and in the solution of linear equations may by expressed in the notation of matrices. In this paper, some new interesting formulas of special matrices are introduced and proved that the determinants of these special matrices have the values zero. All formulation has been coded in MATLAB 7.

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Publication Date
Sat Feb 02 2019
Journal Name
Journal Of The College Of Education For Women
Simplification of new fashion design methods
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Simplification of new fashion design methods

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Publication Date
Tue Jan 01 2019
Journal Name
Sci.int.(lahore)
SOME NEW TYPES OF PERFECT MAPPINGS
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In this work, we introduce a new kind of perfect mappings, namely j-perfect mappings and j-ω-perfect mappings. Furthermore we devoted to study the relationship between j-perfect mappings and j-ω-perfect mappings. Finally, certain theorems and characterization concerning these concepts are studied; j = , δ, α, pre, b, β

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Publication Date
Sun Jun 02 2013
Journal Name
Baghdad Science Journal
Synthesis of New Pyrazoline - Phenoxathiin Derivatives
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Phenoxathiin was prepared by the reaction of diphenyl ether with sulfur in the presence of anhydrous aluminum chloride. This work comprised the synthesis of new phenoxathiin derivatives containing heterocyclic moieties. These heterocyclic compounds were synthesized in three groups. The first group was made up of 2-(oxoalken-1-yl) phenoxathiin derivatives (3a-3j) obtained from the reaction of 2-acetylphenoxathiin with different aromatic aldehyde in the presence of sodium hydroxide. The other two groups involved compounds produced from the reaction of (3a-3j) with hydrazine hydrate in acetic acid to get 2-(1-acetyl pyrazolin-3-yl) phenoxathiin derivatives (4a-4j), and phenyl hydrazine in the presence of piperidine to afford 2-(1-phenyl pyrazo

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Publication Date
Thu Sep 18 2025
Journal Name
Al-fatih Journal
The impact of the use of mirrors in the level of performance of some skills epeeEmpirical researchStudents on the third stage in the Faculty of Physical Education for Girls
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يهدف البحث الى أعداد بعض تمرينات الاساسية لسلاح الشيش بأستخدام المرايا في تطوير قدرة مستوى تعلم الطالبات في المبارزة ومعرفة الفروق بين المجموعتين التجريبي والضابطة بتأثير استخدام المرايا في مستوى اداء بعض مهارات سلاح الشيش لطالبات المرحلة الثالثة , وقد أستخدمت الباحثتان المنهج التجريبي على عينة من طالبات المرحلة الثالثة , وقد بلغ عددهم (45) طالبة , وقد خرجت الباحثتين بعدة أستنتاجات وهي:- - أن المنهاج التعليمي

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Data And Network Science
The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: Evidence from Jordanian telecommunication firms
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This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big

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Publication Date
Mon May 21 2007
Journal Name
Journal Of Planner And Development
Determination of levels of interaction between urban land use and the adoption of spatial analysis techniques (study area, Najaf City)
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the student of the structure of the city and its constituent elements will clearly sense the invisible relationships that underlie the different forms of urban activity, which in turn are defined by the generality of the urban patterns in that city, which will vary clearly according to the location in the city. These relations will be embodied in their true form in the interactions between the different uses of the earth, and the change that will result from their regularity in the form of entities in independent groups, which may share with each other a component of it.
Therefore, the process of controlling the functional interactions between the uses of the urban land and the awareness of t

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Publication Date
Tue Apr 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
A Note on the Hierarchical Model and Power Prior Distribution in Bayesian Quantile Regression
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  In this paper, we investigate the connection between the hierarchical models and the power prior distribution in quantile regression (QReg). Under specific quantile, we develop an expression for the power parameter ( ) to calibrate the power prior distribution for quantile regression to a corresponding hierarchical model. In addition, we estimate the relation between the  and the quantile level via hierarchical model. Our proposed methodology is illustrated with real data example.

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