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Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network
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Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data that was adopted by the ANN study was used here where it is comprised of 1922 measured points of SSW and the other nine parameters of Gamma Ray, Compressional Sonic, Caliper, Neutron Log, Density Log, Deep Resistivity, Azimuth Angle, Inclination Angle, and True Vertical Depth from one Iraqi directional well. Three existing empirical correlations are based only on Compressional Sonic Wave Time (CSW) for predicting SSW. In the same way of developing previous correlations, a fourth empirical correlation was developed by using all measured data points of SSW and CSW. A comparison demonstrated that utilizing ANN was better for SSW predicting with a higher R2 equal to 0.966 and lower other statistical coefficients than utilizing four empirical correlations, where correlations of Carroll, Freund, Brocher, and developed fourth had R2 equal to 0.7826, 0.7636, 0.6764, and 0.8016, respectively, with other statistical parameters that show the new developed correlation best than the other three existing. The use of ANN or new developed correlation in future SSW calculations is relevant to decision makers due to a number of limitations and target SSW accuracy.

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Publication Date
Sat Jan 01 2022
Journal Name
Computers, Materials & Continua
An Optimal Method for Supply Chain Logistics Management Based on Neural Network
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Publication Date
Sat Sep 28 2024
Journal Name
Journal Of The Pakistan Medical Association
Pulse transmission time and amplitude of digital pulse wave determined by fingertip plethysmography as a surrogate marker of brachial artery flowmediated dilatation
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Objectives: To assess the changes in blood vessel stiffness and digital pulse wave amplitude because of flowmediateddilatation, and to explore how these two variables change when endothelial dysfunction isexperimentally induced.Method: The experimental study was conducted at the departments of physiology at the College of Medicine,Mustansiriyah University, and the College of Medicine, Al-Iraqia University, Baghdad, Iraq, from October 14, 2021, toMay 31, 2022, and comprised healthy young males who were subjected to the flow-mediated dilatation techniqueon the left brachial artery. Pulse transit time and the amplitude of the digital pulse wave were measured duringreactive hyperaemia for 2.5 minutes from the left middle finger using a

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge
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Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

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Publication Date
Wed Sep 14 2016
Journal Name
Journal Of Baghdad College Of Dentistry
Comparison of Shear Bond Strength of Three Different Brackets Bonded on Zirconium Surfaces (In Vitro Study)
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Background: With the increased in the demands of adult orthodontics, the challenge of direct bonding to non-enamel surface (zirconium) had been increased. The present study was carried out to compare the shear bond strength of three different brackets (stainless steel, sapphire and composite) bonded to zirconium surface and study the mode of bond failure. Materials and methods: The sample was comprised of 30 models (8mm *6mm*1.5mm) of full contour zirconium veneers. They were divided into three groups according to the brackets type; all samples were treated first by sandblast with aluminum oxide particle 50 µm then coated by z-prime plus primer. A central incisor bracket of each group was bonded to the prepared zirconium surface with lig

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Publication Date
Wed Aug 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Some Estimation Methods Of GM(1,1) Model With Missing Data and Practical Application
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This paper presents a grey model GM(1,1) of the first rank and a variable one and is the basis of the grey system theory , This research dealt  properties of grey model and a set of methods to estimate parameters of the grey model GM(1,1)  is the least square Method (LS) , weighted least square method (WLS), total least square method (TLS) and gradient descent method  (DS). These methods were compared based on two types of standards: Mean square error (MSE), mean absolute percentage error (MAPE), and after comparison using simulation the best method was applied to real data represented by the rate of consumption of the two types of oils a Heavy fuel (HFO) and diesel fuel (D.O) and has been applied several tests to

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Publication Date
Thu Nov 03 2022
Journal Name
Sensors
A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal
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In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

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Publication Date
Wed Aug 28 2019
Journal Name
Journal Of Engineering
Treatment of Simulated Carwash Wastewater by Electrocoagulation with Sonic Energy
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Oily carwash wastewater is a high organic and chemical wastewater. This paper targeted to investigate a treatment to decrease the water consumption and contaminants in car-washing stations. Electrocoagulation combined with ultrasonic energy (Sono-Electrocoagulation) was suggested so that the carwash wastewater is treated to be reused. The effect of both the voltage and time of treatment on the removal of COD, turbidity, conductivity, and total dissolved solids (TDS) were studied at constant initial pH 7 and electrode distance 2 cm. The results showed the best results of removal COD, turbidity, TDS, and reduce electrical conductivity is when the voltage was 30 V and a treatment time of 90 minutes.

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Publication Date
Wed Oct 18 2023
Journal Name
Ieee Access
A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)
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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Geological Journal
Sonic Scanner Helps in Identifying Reservoir Potential and Isotropic Characteristics of Khasib Formation
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Natural fractures provide an important reservoir space and migration channels for oil and gas reservoirs and control the reservoir potential. Therefore, it is essential to understand the methods for identifying accurate reservoir permeability and characterizing reservoir fractures. In particular, using conventional measurements to identify permeability and characterize fractures is very expensive. While using conventional logging data is very challenging, and an efficient characterization correlation method is urgently needed. In this paper, we have evaluated reservoir potential based on the sensitivity of sonic scanner tools to fluid mobility, maximum stress direction, and fractures presence. This tool provides a continuous estimat

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Publication Date
Fri Dec 23 2011
Journal Name
International Journal Of The Physical Sciences
Fast prediction of power transfer stability index based on radial basis function neural network
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