The Bangestan reservoir, which occurs in the Ahwaz oilfield, consists of the middle Cretaceous limestone Ilam and Sarvak Formations that were deposited in the Zagros Basin. The reservoir is divided into ten Zones (A to J) formed in the upper Albian-Santonian and contains considerable hydrocarbon accumulations. The limestones were deposited on an extensive shallow carbonate platform on a passive margin and are dominated by rudist biostrome and grainstone facies. Paleogeographical changes mean that identification of the facies is complex. Seismic stratigraphy and isotopic data are used to better understand the structural and geological setting and develop an understanding of the sedimentary environment. The results show that the rudist biostr

هدفت الدراسة الى التعرف على درجة اتخاذ القرار وعلاقتها ببعض المهارات  الأساسية الهجومية بكرة السلة للطالبات ، اذ يعد اتخاذ القرار قدرة الفرد على التوصل لحل مشكلة اعتراضية أو موقف محير، وذلك باختيار حل من البدائل الموجودة أو المبتكرة، وهذا الاختيار يعتمد على المعلومات التي جمعها الفرد حول المشكلة وعلى القيم والعادات والخبرة والتعليم والمهارات الفردية وتمتاز لعبة كرة السلة بوجود الكثير من المواقف المتغ

In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen

Wellbore instability is one of the major issues observed throughout the drilling operation. Various wellbore instability issues may occur during drilling operations, including tight holes, borehole collapse, stuck pipe, and shale caving. Rock failure criteria are important in geomechanical analysis since they predict shear and tensile failures. A suitable failure criterion must match the rock failure, which a caliper log can detect to estimate the optimal mud weight. Lack of data makes certain wells' caliper logs unavailable. This makes it difficult to validate the performance of each failure criterion. This paper proposes an approach for predicting the breakout zones in the Nasiriyah oil field using an artificial neural network. It

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature