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Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eyes' observation of the different colors and features of images. We propose a multi-layer hybrid system for deep learning using the unsupervised CAE architecture and using the color clustering of the K-mean algorithm to compress images and determine their size and color intensity. The system is implemented using Kodak and Challenge on Learned Image Compression (CLIC) dataset for deep learning. Experimental results show that our proposed method is superior to the traditional compression methods of the autoencoder, and the proposed work has better performance in terms of performance speed and quality measures Peak Signal To Noise Ratio (PSNR) and Structural Similarity Index (SSIM) where the results achieved better performance and high efficiency With high compression bit rates and low Mean Squared Error (MSE) rate the results recorded the highest compression ratios that ranged between (0.7117 to 0.8707) for the Kodak dataset and (0.7191 to 0.9930) for CLIC dataset. The system achieved high accuracy and quality in comparison to the error coefficient, which was recorded (0.0126 to reach 0.0003) below, and this system is onsidered the most quality and accurate compared to the methods of deep learning compared to the deep learning methods of the autoencoder

Publication Date
Wed Sep 15 2021
Journal Name
Geomechanics And Geoengineering
Effect of Deep Remediation and Improvement on Bearing Capacity and Settlement of Piled Raft Foundation Subjected to Static and Cyclic Vertical Loading
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Publication Date
Mon Mar 18 2019
Journal Name
Civil Engineering Journal
Circularization Technique for Strengthening of Plain Concrete Short Square Columns Subjected to a Uniaxial Compression Compressive Pressure
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This paper presents an experimental study for strengthening existing columns against axial compressive loads. The objective of this work is to study the behavior of concrete square columns strengthening with circulation technique. In Iraq, there are significantly more reinforced rectangular and square columns than reinforced circular columns in reinforced concrete buildings. Moreover, early research studies indicated that strengthening of rectangular or square columns using wraps of CFRP (Carbon Fiber Reinforced Polymer) provided rather little enhancement to their load-carrying capacity. In this paper, shape modification technique was performed to modify the shape (cross section) of the columns from square columns into circular colu

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Publication Date
Fri Sep 01 2023
Journal Name
Journal Of Engineering
The Efficiency of Belled Piles in Multi-Layers Soils Subjected to Axial Compression and Pullout Loads: Review
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Multi-belled piles are piles with enlarged ends; these piles have one or further bells at the lower third part of the pile. These piles are suitable for many soils with problems such as softening clay, the variation of groundwater table, expansive soils, black cotton soil, and loose sand. The current study reviewed the behavior of belled piles in multi-layer soils subjected to axial compression and pullout loading. The review covered the experimental and theoretical works on belled piles in multi-layered soils. These piles were subjected to static and dynamic loadings in compression and pullout cases. Most theoretical results focused on software such as PLAXIS 3D. The axial load applied on the piles comes from the upper

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Publication Date
Sun Jun 07 2015
Journal Name
Baghdad Science Journal
On The Nearby-Tip Strain Investigation and Failure-Propability Evaluation for Impacted Thin Plates Using the 2-Random-Variables Multi-Canonical-Based Joint Propability Distributions
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The study of the validity and probability of failure in solids and structures is highly considered as one of the most incredibly-highlighted study fields in many science and engineering applications, the design analysts must therefore seek to investigate the points where the failing strains may be occurred, the probabilities of which these strains can cause the existing cracks to propagate through the fractured medium considered, and thereafter the solutions by which the analysts can adopt the approachable techniques to reduce/arrest these propagating cracks.In the present study a theoretical investigation upon simply-supported thin plates having surface cracks within their structure is to be accomplished, and the applied impact load to the

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Publication Date
Wed Jun 07 2023
Journal Name
Journal Of Educational And Psychological Researches
Multiple Intelligence Test Item Selection-Based on Howard Gardner's MI Model Using a Generalized Partial Estimation Model: Ministry of Education \ Karkh First Directorate of Education
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The aim of the research is to examine the multiple intelligence test item selection based on Howard Gardner's MI model using the Generalized Partial Estimation Form, generalized intelligence. The researcher adopted the scale of multiple intelligences by Kardner, it consists of (102) items with eight sub-scales. The sample consisted of (550) students from Baghdad universities, Technology University, al-Mustansiriyah university, and Iraqi University for the academic year (2019/2020). It was verified assumptions theory response to a single (one-dimensional, local autonomy, the curve of individual characteristics, speed factor and application), and analysis of the data according to specimen partial appreciation of the generalized, and limits

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Publication Date
Sun Oct 04 2026
Journal Name
Al–bahith Al–a'alami
The Media and Shaping the Public's Mental Image towards the Transitional Justice
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Media plays an important role in shaping the mental image of their audiences for individuals, groups and organizations, States and peoples. It is the window through which overlooks the masses on events and issues, and in the light of their exposure to these means are their opinions and impressions.

Despite the importance of direct experiences in shaping opinions, drawing pictures and impressions, it is inevitable to rely on these means as individuals can not engage in direct experiences with thousands of events, issues and topics that concern their community and other societies.

There is no doubt that media is of great importance at the present time, because of its significant impact in the management of the course of pol

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Publication Date
Fri Sep 01 2017
Journal Name
International Research Journal Of Engineering And Technology
A POWERFUL AUTOMATED IMAGE INDEXING AND RETRIEVAL TOOL FOR SOCIAL MEDIA Sample
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The Internet image retrieval is an interesting task that needs efforts from image processing and relationship structure analysis. In this paper, has been proposed compressed method when you need to send more than a photo via the internet based on image retrieval. First, face detection is implemented based on local binary patterns. The background is notice based on matching global self-similarities and compared it with the rest of the image backgrounds. The propose algorithm are link the gap between the present image indexing technology, developed in the pixel domain, and the fact that an increasing number of images stored on the computer are previously compressed by JPEG at the source. The similar images are found and send a few images inst

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Publication Date
Tue Jul 01 2025
Journal Name
المجلة العراقية للعلوم الاقتصادية
Using the Hybrid ARDL–GRU Model in Investigating the Dynamic Relationship between Dinar Deposits and US Dollar Payments at the Central Bank of Iraq
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This research examines the relationship between dinar deposits and U.S. dollar payments at the Central Bank of Iraq using monthly data for the period 2016–2025. The ARDL model, the GRU neural network, and a hybrid ARDL–GRU model are applied. The results show that dollar payments are stationary at level, while dinar deposits become stationary after first differencing, with a significant positive long-run cointegrating relationship. The linear ARDL model has limited ability to capture sudden shocks, whereas the hybrid ARDL–GRU model achieves superior forecasting performance both in-sample and out-of-sample. The findings confirm the Central Bank of Iraq’s efficiency in managing domestic and foreign liquidity and maintaining market stab

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Publication Date
Wed Sep 11 2024
Journal Name
Lecture Notes In Civil Engineering
An Image Processing Algorithm to Address the Problem of Stains Merge on Water Sensitive Papers and Its Impact on the Evaluation of Spray Quality Indicators
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There are many techniques that can be used to estimate the spray quality traits such as the spray coverage, droplet density, droplet count, and droplet diameter. One of the most common techniques is to use water sensitive papers (WSP) as a spray collector on field conditions and analyzing them using several software. However, possible merger of some droplets could occur after they deposit on WSP, and this could affect the accuracy of the results. In this research, image processing technique was used for better estimation of the spray traits, and to overcome the problem of droplet merger. The droplets were classified as non-merged and merged droplets based on their roundness, then the merged droplets were separated based on the average non-m

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Publication Date
Thu May 01 2025
Journal Name
2025 3rd International Conference On Business Analytics For Technology And Security (icbats)
Comparison of Deep Neural Network Models (LSTM, Bi-LSTM, GRU and Bi-GRU) for Gold Price Prediction
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This research studies the comparison of deep neural network models and performance evaluation to predict the gold prices of time series, where the gold prices contain high fluctuations and non-linear patterns that are difficult to capture using traditional models, which makes predicting them a significant challenge. Therefore, the focus was on using deep learning models represented by (LSTM), (Bi-LSTM), (GRU) and (Bi-GRU). The results showed the superiority of the (Bi-GRU) model according to comparison criteria (MSE), (RMSE), (MAE), and (R∧2) compared to other models because it was able to understand the time patterns better by processing the data in both directions and provided superior performance, which indicates its effectiveness, eff

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