A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited grids and used in the training and testing of the used network. A comparison between the calculated and observed cumulative oil production has been carried out through the testing steps of the constructed ANN, an absolute average percentage error of the used network was reached to 4.044%, and this is consider to be an acceptable limit within engineering applications, in addition to that, a good behavior was reached with (FFNNW) and suitable re-entry wells location were identified according to the reservoir configuration (pressure and saturation distribution) output from SRF simulation model at the end of 2005.
This current research deals with the "The role of organizational change in the achievement of strategic success" Who are getting increased attention to being one of the important topics and relatively new, And which have a significant impact on the future of the organization So there is a need for this research, which aims to identify the role of organizational change across dimensions (technology, organizational structure, human resources, organizational culture) in the strategic success through its components (a specific strategy, effective implementation, innovation, customer satisfaction)، And that by two main hypotheses, branched about eight hypothes
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Quality is the key to success in today's world, which is based mainly on competition in the provision of high quality services through the application of the modern management method which is called total quality management in organizations. This includes describing the provision of health services and satisfaction of patients . .  
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous rel
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تعتبر المعادلات التفاضلية الموجية من اهم المواضيع التي تمثل على سبيل المثال الحركة الموجية للاهتزازات الأرضية . ومن هنا فان ايجاد حلول تقريبيه لمثل هذه المعادلات بدقة وسرعه عالية وبشكل اسرع من الحلول التحليلية والمعقدة , اصبح ممكنا من خلال استخدام الذكاء الاصطناعي واساليب التعلم الالي. في هذا البحث هناك ثلاثة أهداف الأول هو تحويل مشكلة القيمة الأولية للمعادلة الموجية إلى شكلها القانوني وإيجاد حلها ا
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In this work, microbubble dispersed air flotation technique was applied for cadmium ions removal from wastewater aqueous solution. Experiments parameters such as pH (3, 4, 5, and 6), initial Cd(II) ions concentration (40, 80, and 120 mg/l) contact time( 2, 5, 10 , 15, and 20min), and surfactant (10, 20and 40mg/l) were studied in order to optimize the best conditions .The experimental results indicate that microbubbles were quite effective in removing cadmium ions and the anionic surfactant SDS was found to be more efficient than cationic CTAB in flotation process. 92.3% maximum removal efficiency achieved through 15min at pH 5, SDS surfactant concentration 20mg/l, flow rate250 cm3/min and at 40mg/l Cd(II) ions initial co
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Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and
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Image Fusion Using A Convolutional Neural Network