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Vertical Stress Prediction for Zubair Oil Field/ Case Study
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Predicting vertical stress was indeed useful for controlling geomechanical issues since it allowed for the computation of pore pressure for the formation and the classification of fault regimes. This study provides an in-depth observation of vertical stress prediction utilizing numerous approaches using the Techlog 2015 software. Gardner's method results in incorrect vertical stress values with a problem that this method doesn't start from the surface and instead relies only on sound log data. Whereas the Amoco, Wendt non-acoustic, Traugott, average technique simply needed density log as input and used a straight line as the observed density, this was incorrect for vertical computing stress. The results of these methods show that extrapolated density measurement used an average for the real density. The gradient of an extrapolated method is much better in shallow depth into the vertical stress calculations. The Miller density method had an excellent fit with the real density in deep depth. It has been crucial to calculate vertical stress for the past 40 years because calculating pore pressure and geomechanical building models have employed vertical stress as input. The strongest predictor of vertical stress may have been bulk density. According to these results, the miller and extrapolated techniques may be the best two methods for determining vertical stress. Still, the gradient of an extrapolated method is much more excellent in shallow depth than the miller method. Extrapolated density approach may produce satisfactory results for vertical stress, while miller values are lower than those obtained by extrapolating. This may be due to the poor gradient of this method at shallow depths. Gardner's approach incorrectly displays minimum values of about 4000 psi at great depths. While other methods provide numbers that are similar because these methods use constant bulk density values that start at the surface and continue to the desired depth, this is incorrect.

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Publication Date
Fri Mar 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Balancing the Multi Assembly line Using Ranked Positional Weight and COMSOAL Algorithms: Case Study at the Sewing line Factory (7) /the General Company for Leather Industries (GCLI/ Baghdad)
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      The problem of multi assembly line balancing appears as one of the most prominent and complex type of problem. The research problem of this dissertation is concerned with choosing the suitable method that includes the nature of the processes of the multi assembly type of the sewing line at factory no. (7). The State Company for Leather Manufacturing. The sewing line currently suffers from idle times at work stations which resulted in low production levels that do not meet the production plans. The authors have devised a flexible simulation model which uses the uniform distribution to generate task time for each shoe type produced by the factory. The simulation of the multi assembly line was based on assigni

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Publication Date
Sat Apr 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
The diagnosis and determine the level of balance between the time available for life and work: The case study of a sample of doctors hospitals City of Medicine
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Purpose: The diagnosis and determine the level of balance between the time available for life and work with the doctors in the hospitals of t the six hospitals in the City of Medicine.

Design / methodology / Approach: It has been relying on ready-scale, to make sure the diagnosis and determine the level of balance between the time available for life and work, where they were distributed on Form 42 doctors in the six hospitals in the City of Medicine, were analyzed by software (Nvivo and SPSS v.22).

Results: The results showed that there is a good level of balance between the time available for life and work with the doctors.

Research limitations: The diffi

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Publication Date
Thu Jun 01 2023
Journal Name
Ifip Advances In Information And Communication Technology
Rapid Thrombogenesis Prediction in Covid-19 Patients Using Machine Learning
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Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The Prediction of COVID 19 Disease Using Feature Selection Techniques
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Abstract<p>COVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in </p> ... Show More
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Publication Date
Tue Feb 28 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Development prediction algorithm of vehicle travel time based traffic data
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This work bases on encouraging a generous and conceivable estimation for modified an algorithm for vehicle travel times on a highway from the eliminated traffic information using set aside camera image groupings. The strategy for the assessment of vehicle travel times relies upon the distinctive verification of traffic state. The particular vehicle velocities are gotten from acknowledged vehicle positions in two persistent images by working out the distance covered all through elapsed past time doing mollification between the removed traffic flow data and cultivating a plan to unequivocally predict vehicle travel times. Erbil road data base is used to recognize road locales around road segments which are projected into the commended camera

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Publication Date
Sun Jan 01 2023
Journal Name
8th Engineering And 2nd International Conference For College Of Engineering – University Of Baghdad: Coec8-2021 Proceedings
Prediction of consolidation due to dewatering by using MATLAB software
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Publication Date
Thu Dec 23 2010
Journal Name
Arabian Journal Of Geosciences
Climatic prediction of the terrestrial and coastal areas of Iraq
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Publication Date
Fri Mar 01 2024
Journal Name
Iaes International Journal Of Artificial Intelligence (ij-ai)
Analyzing the behavior of different classification algorithms in diabetes prediction
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<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c

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Publication Date
Sun Nov 26 2017
Journal Name
Journal Of Engineering
Compression Index and Compression Ratio Prediction by Artificial Neural Networks
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Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

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Publication Date
Wed Mar 01 2017
Journal Name
Neural Computing And Applications
The potential of nonparametric model in foundation bearing capacity prediction
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