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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
Thu Oct 01 2020
Journal Name
Journal Of Taibah University Medical Sciences
Preparing polycaprolactone scaffolds using electrospinning technique for construction of artificial periodontal ligament tissue
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Objectives The strategies of tissue-engineering led to the development of living cell-based therapies to repair lost or damaged tissues, including periodontal ligament and to construct biohybrid implant. This work aimed to isolate human periodontal ligament stem cells (hPDLSCs) and implant them on fabricated polycaprolactone (PCL) for the regeneration of natural periodontal ligament (PDL) tissues. Methods hPDLSCs were harvested from extracted human premolars, cultured, and expanded to obtain PDL cells. A PDL-specific marker (periostin) was detected using an immunofluorescent assay. Electrospinning was applied to fabricate PCL at three concentrations (13%, 16%, and 20% weight/volume) in two forms, which were examined through field emission

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks
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Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforwar

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Finding the best estimation of generalized for failure rates by using Simulation
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The statistical distributions study aimed to obtain on best descriptions  of variable sets phenomena, which each of them got one behavior of that distributions .  The estimation operations study for that distributions considered of important things which could n't canceled in variable behavior study, as result  this research came as trial for reaching to best method for information distribution estimation which is generalized linear failure rate distribution, throughout studying the theoretical sides by depending on statistical posteriori methods  like greatest ability, minimum squares method and Mixing method (suggested method).        

The research

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Publication Date
Thu Apr 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Between Tree regression (TR), and Negative binomial regression (NBR) by Using Simulation.
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            In this paper, the process of comparison between the tree regression model and the negative binomial regression. As these models included two types of statistical methods represented by the first type "non parameter statistic" which is the tree regression that aims to divide the data set into subgroups, and the second type is the "parameter statistic" of negative binomial regression, which is usually used when dealing with medical data, especially when dealing with large sample sizes. Comparison of these methods according to the average mean squares error (MSE) and using the simulation of the experiment and taking different sample

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Publication Date
Wed Jan 04 2017
Journal Name
Applied Research Journal
ASSESSMENT OF SHEAR AND COMPRESSIBILITY PROPERTIES OF ASPHALT STABILIZED COLLAPSIBLE SOIL
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One of the major problems facing the road construction engineer is the collapsible granular soil which may be used for embankment construction. Problems appears when such compacted soil come in touch with water, it exhibits cracking and uncontrolled settlement. Collapsible soils are defined as any unsaturated soil that goes through a radical rearrangement of practice and great loss of volume upon wetting, with or without additional loading. An attempt has been made in this investigation to stabilize the collapsible soil of Nasiriya with asphalt emulsion. Specimens of pure and asphalt emulsion stabilized soil have been prepared using optimum fluid content and tested. The first group of specimens of (60x60x20) cm have been tested for direct s

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Publication Date
Sun Jan 01 2023
Journal Name
Ieee Access
Fuzzy-Based Ensemble Feature Selection for Automated Estimation of Speaker Height and Age Using Vocal Characteristics
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Publication Date
Fri May 01 2020
Journal Name
Journal Of Electrical And Electronics Engineering
HF Wave Propagation Prediction Based On Passive Oblique Ionosonde
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High frequency (HF) communications have an important role in long distances wireless communications. This frequency band is more important than VHF and UHF, as HF frequencies can cut longer distance with a single hopping. It has a low operation cost because it offers over-the-horizon communications without repeaters, therefore it can be used as a backup for satellite communications in emergency conditions. One of the main problems in HF communications is the prediction of the propagation direction and the frequency of optimum transmission (FOT) that must be used at a certain time. This paper introduces a new technique based on Oblique Ionosonde Station (OIS) to overcome this problem with a low cost and an easier way. This technique uses the

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Publication Date
Thu Feb 29 2024
Journal Name
Iraqi Journal Of Science
Examining the Seasonal Correlations Among Different Ionospheric Indices During Solar Cycle Twenty-Four
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     The present work aimed to examine the nature and degree of the cross-correlations among three different ionospheric indices: these are Optimum Working Frequency (OWF), Highest Probable Frequency (HPF), and Best Usable Frequency (BUF). VOCAP and ASASPS models were adopted to determine the datasets of the selected ionospheric indices. The determination was made for different transceiver stations that provide certain HF connection links during the minimum and maximum years of solar cycle 24, 2009 and 2014, respectively. Matlab program was implemented to produce the geodesic parameters for the selected transceiver stations. The determination was made for different path lengths (500, 1000, 1500, and 2000) Km and bearings (0o, 45

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Publication Date
Sun Dec 31 2023
Journal Name
Sumer Journal For Pure Science
COVID-19Disease Diagnosis using Artificial Intelligence based on Gene Expression: A Review
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