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Reservoir permeability prediction based artificial intelligence techniques
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   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes beyond simply predicting lithology to provide a detailed quantification of primary minerals (e.g., calcite and dolomite) as well as secondary ones (e.g., shale and anhydrite). The results show important lithological contrast with the high-porosity layers correlating to possible reservoir areas. The richness of Quanti-Elan's interpretations goes beyond what log analysis alone can reveal. The methodology is described in-depth, discussing the approaches used to train neural networks (e.g., data processing, network architecture). A case study where output of neural network predictions of permeability in a particular oil well are compared with core measurements. The results indicate an exceptional closeness between predicted and actual values, further emphasizing the power of this approach. An extrapolated neural network model using lithology (dolomite and limestone) and porosity as input emphasizes the close match between predicted vs. observed carbonate reservoir permeability. This case study demonstrated the ability of neural networks to accurately characterize and predict permeability in complex carbonate systems. Therefore, the results confirmed that neural networks are a reliable and transformative technology tool for oil reservoirs management, which can help to make future predictive methodologies more efficient hydrocarbon recovery operations.

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Publication Date
Tue Sep 08 2020
Journal Name
Baghdad Science Journal
CTJ: Input-Output Based Relation Combinatorial Testing Strategy Using Jaya Algorithm
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Software testing is a vital part of the software development life cycle. In many cases, the system under test has more than one input making the testing efforts for every exhaustive combination impossible (i.e. the time of execution of the test case can be outrageously long). Combinatorial testing offers an alternative to exhaustive testing via considering the interaction of input values for every t-way combination between parameters. Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). IOR combinatorial testing only tests for the important combinations selected by the tester. Most of the researches in combinatorial testing

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach
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Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

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Publication Date
Mon Oct 09 2023
Journal Name
2023 Ieee 34th International Symposium On Software Reliability Engineering Workshops (issrew)
Semantics-Based, Automated Preparation of Exploratory Data Analysis for Complex Systems
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Publication Date
Sat Oct 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Improving Nursing process Performance based on service scheduling dimensions. ( Field research)
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Abstract :

This present paper sheds the light on dimensions of scheduling the service that includes( the easiness of performing the service, willingness , health factors, psychological sides, family matters ,diminishing the time of waiting that improve  performance of nursing process including ( the willingness of performance, the ability to perform  the performance , opportunity of performance) . There is genuine problem in the Iraqi hospitals  lying into the weakness of nursing staffs , no central decision to define and organize schedules. Thus the researcher has chosen  this problem as to be his  title . The research come a to develop  the nursing service

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Publication Date
Sat Nov 01 2008
Journal Name
Digital Signal Processing
A high performance parallel Radon based OFDM transceiver design and simulation
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major goal of the next-generation wireless communication systems is the development of a reliable high-speed wireless communication system that supports high user mobility. They must focus on increasing the link throughput and the network capacity. In this paper a novel, spectral efficient system is proposed for generating and transmitting twodimensional (2-D) orthogonal frequency division multiplexing (OFDM) symbols through 2- D inter-symbol interference (ISI) channel. Instead of conventional data mapping techniques, discrete finite Radon transform (FRAT) is used as a data mapping technique due to the increased orthogonality offered. As a result, the proposed structure gives a significant improvement in bit error rate (BER) performance. Th

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Publication Date
Sun May 01 2016
Journal Name
Iraqi Journal Of Science
Efficient text in image hiding method based on LSB method principle
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The steganography (text in image hiding) methods still considered important issues to the researchers at the present time. The steganography methods were varied in its hiding styles from a simple to complex techniques that are resistant to potential attacks. In current research the attack on the host's secret text problem didn’t considered, but an improved text hiding within the image have highly confidential was proposed and implemented companied with a strong password method, so as to ensure no change will be made in the pixel values of the host image after text hiding. The phrase “highly confidential” denoted to the low suspicious it has been performed may be found in the covered image. The Experimental results show that the covere

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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Multiwavelet and Estimation by Interpolation Analysis Based Hybrid Color Image Compression
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Nowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. The major topic of this paper, is improve the compresses of still images by Multiwavelet based on estimation the high Multiwavelet coefficients in high frequencies sub band  by interpolation instead of sending all Multiwavelet coefficients. When comparing the proposed approach with other compression methods Good result obtained

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Publication Date
Wed Jan 01 2020
Journal Name
Journal Of Information Hiding And Multimedia Signal Processing
Enhancement of lsb audio steganography based on carrier and message characteristics
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Data steganography is a technique used to hide data, secret message, within another data, cover carrier. It is considered as a part of information security. Audio steganography is a type of data steganography, where the secret message is hidden in audio carrier. This paper proposes an efficient audio steganography method that uses LSB technique. The proposed method enhances steganography performance by exploiting all carrier samples and balancing between hiding capacity and distortion ratio. It suggests an adaptive number of hiding bits for each audio sample depending on the secret message size, the cover carrier size, and the signal to noise ratio (SNR). Comparison results show that the proposed method outperforms state of the art methods

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Publication Date
Mon Jul 15 2019
Journal Name
Iet Microwaves, Antennas & Propagation
Hilbert metamaterial printed antenna based on organic substrates for energy harvesting
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Abstract In this study, an investigation is conducted to realise the possibility of organic materials use in radio frequency (RF) electronics for RF-energy harvesting. Iraqi palm tree remnants mixed with nickel oxide nanoparticles hosted in polyethylene, INP substrates, is proposed for this study. Moreover, a metamaterial (MTM) antenna is printed on the created INP substrate of 0.8 mm thickness using silver nanoparticles conductive ink. The fabricated antenna performances are instigated numerically than validated experimentally in terms of S11 spectra and radiation patterns. It is found that the proposed antenna shows an ultra-wide band matching bandwidth to cover the frequencies from 2.4 to 10 GHz with bore-sight gain variation from 2.2 to

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Publication Date
Wed Jul 12 2023
Journal Name
Energies
Finite Time Disturbance Observer Based on Air Conditioning System Control Scheme
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A novel robust finite time disturbance observer (RFTDO) based on an independent output-finite time composite control (FTCC) scheme is proposed for an air conditioning-system temperature and humidity regulation. The variable air volume (VAV) of the system is represented by two first-order mathematical models for the temperature and humidity dynamics. In the temperature loop dynamics, a RFTDO temperature (RFTDO-T) and an FTCC temperature (FTCC-T) are designed to estimate and reject the lumped disturbances of the temperature subsystem. In the humidity loop, a robust output of the FTCC humidity (FTCC-H) and RFTDO humidity (RFTDO-H) are also designed to estimate and reject the lumped disturbances of the humidity subsystem. Based on Lyapunov theo

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