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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 in Baghdad city were used. 70% of these results were used to train the prediction ANN models and the rest were equally divided to test and validate the ANN models. The performance of the developed models was examined using the correlation coefficient R. The final models have demonstrated that the ANN has capability for acceptable prediction of compression index and compression ratio. Two equations were proposed to estimate compression index using the connecting weights algorithm, and good agreements with test results were achieved.

 

 

 

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Publication Date
Fri Apr 12 2019
Journal Name
Journal Of Economics And Administrative Sciences
Accounting Mining Data Using Neural Networks (Case study)
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Business organizations have faced many challenges in recent times, most important of which is information technology, because it is widely spread and easy to use. Its use has led to an increase in the amount of data that business organizations deal with an unprecedented manner. The amount of data available through the internet is a problem that many parties seek to find solutions for. Why is it available there in this huge amount randomly? Many expectations have revealed that in 2017, there will be devices connected to the internet estimated at three times the population of the Earth, and in 2015 more than one and a half billion gigabytes of data was transferred every minute globally. Thus, the so-called data mining emerged as a

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Publication Date
Fri Mar 20 2020
Journal Name
Remote Sensing
Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics
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The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, rec

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Publication Date
Tue May 01 2018
Journal Name
Sci.int.(lahore)
The Effect of Wavelet Coefficient Reduction on Image Compression Using DWT and Daubechies Wavelet Transform
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FG Mohammed, HM Al-Dabbas, Science International, 2018 - Cited by 2

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Publication Date
Mon Jan 01 2024
Journal Name
Aims Energy
The impact of different compression ratios on emissions, and combustion characteristics of a biodiesel engine
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<p>The current work investigated the combustion efficiency of biodiesel engines under diverse ratios of compression (15.5, 16.5, 17.5, and 18.5) and different biodiesel fuels produced from apricot oil, papaya oil, sunflower oil, and tomato seed oil. The combustion process of the biodiesel fuel inside the engine was simulated utilizing ANSYS Fluent v16 (CFD). On AV1 diesel engines (Kirloskar), numerical simulations were conducted at 1500 rpm. The outcomes of the simulation demonstrated that increasing the compression ratio (CR) led to increased peak temperature and pressures in the combustion chamber, as well as elevated levels of CO<sub>2</sub> and NO mass fractions and decreased CO emission values un

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Publication Date
Mon Mar 23 2020
Journal Name
Journal Of Engineering
Experimental and Numerical Study on CFRP-Confined Square Concrete Compression Members Subjected to Compressive Loading
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     Strengthening of the existing structures is an important task that civil engineers continuously face. Compression members, especially columns, being the most important members of any structure, are the most important members to strengthen if the need ever arise. The method of strengthening compression members by direct wrapping by Carbon Fiber Reinforced Polymer (CFRP) was adopted in this research. Since the concrete material is a heterogeneous and complex in behavior, thus, the behavior of the confined compression members subjected to uniaxial stress is investigated by finite element (FE) models created using Abaqus CAE 2017 software. The aim of this research is to study experimentally and numerically, the beha

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection
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            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the

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Publication Date
Fri May 01 2020
Journal Name
International Journal Of Advanced Science And Technology
Improved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval
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Background/Objectives: The purpose of current research aims to a modified image representation framework for Content-Based Image Retrieval (CBIR) through gray scale input image, Zernike Moments (ZMs) properties, Local Binary Pattern (LBP), Y Color Space, Slantlet Transform (SLT), and Discrete Wavelet Transform (DWT). Methods/Statistical analysis: This study surveyed and analysed three standard datasets WANG V1.0, WANG V2.0, and Caltech 101. The features an image of objects in this sets that belong to 101 classes-with approximately 40-800 images for every category. The suggested infrastructure within the study seeks to present a description and operationalization of the CBIR system through automated attribute extraction system premised on CN

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Publication Date
Thu Jun 01 2017
Journal Name
International Journal Of Engineering Research And Advanced Technology
The Use of First Order Polynomial with Double Scalar Quantization for Image Compression
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Publication Date
Tue Sep 01 2020
Journal Name
Journal Of Engineering
Buckling Behavior of Aluminum Alloy Thin-Walled Beam with Holes under Compression Loading
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Thin-walled members are increasingly used in structural applications, especially in light structures like in constructions and aircraft structures because of their high strength-to-weight ratio. Perforations are often made on these structures for reducing weight and to facilitate the services and maintenance works like in aircraft wing ribs. This type of structures suffers from buckling phenomena due to its dimensions, and this suffering increases with the presence of holes in it. This study investigated experimentally and numerically the buckling behavior of aluminum alloy 6061-O thin-walled lipped channel beam with specific holes subjected to compression load. A nonlinear finite elements analysis was used to obtain the

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Publication Date
Sun Nov 19 2017
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
Image Compression based on Fixed Predictor Multiresolution Thresholding of Linear Polynomial Nearlossless Techniques
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Image compression is a serious issue in computer storage and transmission,  that simply makes efficient use of redundancy embedded within an image itself; in addition, it may exploit human vision or perception limitations to reduce the imperceivable information Polynomial coding is a modern image compression technique based on modelling concept to remove the spatial redundancy embedded within the image effectively that composed of two parts, the  mathematical model and the residual. In this paper, two stages proposed technqies adopted, that starts by utilizing the lossy predictor model along with multiresolution base and thresholding techniques corresponding to first stage. Latter by incorporating the near lossless com

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