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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 Jan 01 2020
Journal Name
Ieee Access
Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey
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Publication Date
Thu May 01 2025
Journal Name
International Journal Of Engineering
Impact of Using Polyethylene Polymer on Properties of Hot Asphalt Mixture by Conducting Semi-Wet and Dry Mixing Process
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In recent years, various methods have been developed to enhance the characteristics of asphalt pavement in order to face the continuous challenges of increasing traffic loads and changing climate conditions. One of the most popular and successful methods is modifying the asphalt mixtures or asphalt binder with the addition of polymers. Therefore, two types of Polyethylene (PE) polymer, High-Density PE (HDPE) and Low-Density PE (LDPE), are used in this research. Two methods were applied to prepare PE-modified asphalt mixtures: Semi-Wet Method (S-WM) and Dry Method (DM). The findings of the investigation indicated that the addition of PE polymer can reduce the wear loss of aggregate. In general, the experimental results revealed that asphalt

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Publication Date
Mon Mar 12 2018
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
The Effect of Crystallization Time and Acid Type on the Synthesis of Nano-Gamma Alumina Using Double Hydrothermal Method
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Double hydrothermal method was used to prepare nano gamma alumina   using aluminum nitrate nano hydrate and sodium aluminate as an aluminum source, CTAB (cetyltrimethylammonium bromide) as surfactant, and variable acids: weak acids like; citric, and acitic acids, and strong acids like; hydrochloric and nitric acids as a bridge between aluminum salts and surfactant. Different crystallization times  12, 24, 48, and 72 hrs were applied. All the batches were prepared at pH equals to 9. XRD diffraction technique was used to investigate the crystalline nano gamma alumina pure from surfactant. N2 adsorption-desorption (BET) was used to measure the surface area and pore volume of the prepared nano alumina, the average p

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Publication Date
Fri Jul 12 2024
Journal Name
World Water Policy
The effect of natural factors on changing soil uses in the marshes: An experimental study using Landsat satellite data
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The study aimed to analyze the effect of meteorological factors (rainfall rate and temperature) on the change in land use in the marshes of the Al‐Majar Al‐Kabir region in southern Iraq. Satellite images from Landsat 7 for 2012 and Landsat 8 for 2022 were used to monitor changes in the land coverings, the images taken from the Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors of the Landsat satellite. Geometric correction was used to convert images into a format with precise geographic coordinates using ArcMap 10.5. The maximum likelihood classification method was used to examine satellite image data using a supervised approach, and the data were analyzed statistically. We obtained clear images of the area,

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Publication Date
Sun Oct 19 2025
Journal Name
Lecture Notes In Networks And Systems
Analyzing the Pragmatics of Argumentation in the Arabic Novel Using Artificial Intelligence: An Applied Study on the Granada Trilogy
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Publication Date
Tue Apr 10 2018
Journal Name
Ibn Al-haitham Jour. For Pure & Appl. Sci.
Effect of Scan Rate and pH on Determination Amoxilline Using Screen Printed Carbon Electrode Modified with Functionalized Graphene Oxide
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Graphene oxide GO was functionalized with 4-amino, 3-substituted 1H, 1, 2, 4 Triazole 5(4H) thion (ASTT) to obtain GOT. GOT characterized by FT-IR, XRD.via modification of the working electrode of the SPCE with the prepared nanomaterial (GOT) the effect of scan rate and pH on the determination of Amoxilline (AMOX) was studied using cyclic voltammetry. AMOX show various responses at pH ranging from 2 to 7 and also was observed sharp increase in the oxidation peaks in the pH 3. The formal potential (midpoint) for AMOX was highly pH-dependent. From the effect of scan rate, surface coverage concentration Γ of electroactive species the values of the electron transfer coefficient and the electron transfer constant rate ket was obtained as 5.39×

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Publication Date
Sun Jan 01 2012
Journal Name
Iraqi Journal Of Science
EFFECT OF LAND DEGRADATION DEGREE ON THE LAND COVER TYPE USING GIS TECHNOLOGY IN THE WEST OF BAGHDAD/IRAQ
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In this study is the phenomenon of desertification risk assessment in the Abu Ghraib area west of Baghdad/Iraq, which has an area of about (384.168 km 2), that the annual mean temperature is more than (22 C). Rainfall was low, ranging from the (200 mm) per year for Iraq and (2.82) mm per year of the study area* temperature is high and evaporation is also high (mm 7.73) per year*, so the climate in general of the dry type and the system of soil moisture is the kind of Aridic (Torric). To this study was to identify three indicators to monitor for the period from 2001-2005 using GIS and these indicators are (soil, groundwater and the nature of land use), using ArcGIS 9.1. The results showed that the risk of desertification was part of the leve

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Publication Date
Tue Jul 22 2025
Journal Name
Retos
Evaluating the impact of neurophysiological fatigue patterns on tactical decision-making in elite football players using wearable EEG technology
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Introduction: Elite football performance hinges on rapid tactical decision-making under physical and cognitive strain. While peripheral fatigue’s effects on motor output are well documented, the neurophysiological markers of mental fatigue and their impact on in-game decision making remain underexplored. Objective: To determine how EEG-derived central fatigue indices—frontal theta power and the theta/alpha ratio—relate to tactical decision accuracy and speed in elite football players. Methodology: Twenty male national-level footballers (age 22.4 ± 2.1 years; ≥ 5 years’ experience) completed the Yo-Yo Intermittent Recovery Test Level 1 while wearing an 8-channel dry-electrode frontal EEG headset. Frontal theta

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Publication Date
Thu Jul 20 2023
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Study the Effect of Manganese Ion Doping on the Size- Strain of SnO2 nanoparticles Using X-Ray Diffraction Data
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In this study, SnO2 nanoparticles were prepared from cost-low tin chloride (SnCl2.2H2O) and ethanol by adding ammonia solution by the sol-gel method, which is one of the lowest-cost and simplest techniques. The SnO2 nanoparticles were dried in a drying oven at a temperature of 70°C for 7 hours. After that, it burned in an oven at a temperature of 200°C for 24 hours. The structure, material, morphological, and optical properties of the synthesized SnO2 in nanoparticle sizes are studied utilizing X-ray diffraction. The Scherrer expression was used to compute nanoparticle sizes according to X-ray diffraction, and the results needed to be scrutinized more closely. The micro-strain indicates the broadening of diffraction peaks for nano

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Publication Date
Fri Jan 20 2023
Journal Name
Journal Of Electronic Materials
Influence of Dry and Wet Etching on AlInSb Contact Resistivity, Transfer Length, and Sheet Resistance Using Circular Transmission Model
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