Preferred Language
Articles
/
iRe5Po8BVTCNdQwCz2Wy
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
...Show More Authors

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
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
...Show More Authors

     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

... Show More
Preview PDF
Crossref (1)
Scopus Crossref
Publication Date
Sun Jul 01 2012
Journal Name
Journal Of Computer Science
Peer-to-Peer Video Conferencing Using Hybrid Content Distribution Model
...Show More Authors

View Publication
Scopus (4)
Crossref (1)
Scopus Crossref
Publication Date
Mon Mar 28 2022
Journal Name
Journal Of Physical Education
The Effect of Using a Teaching Aid on Learning Backswing to Handstand on Rings in Youth Artistic Gymnastics
...Show More Authors

               The research aimed at designing a teaching aid for learning backswing into handstand as well as identifying its effect on learning skill performance. The researchers hypothesized statistical differences between pre and post-tests in favor of the research group. They used the experimental method on six (13 – 16) year–old Baghdad club gymnasts. The researchers used the one group design in which all players perform pretests followed by special tests on the teaching aid than are tested posttests. The researchers conclude that the teaching aid positively affected learning the skill as well as the teaching aid was very good and endured the performance of all gymnasts. The researcher recommended making simi

... Show More
Crossref
Publication Date
Wed Jan 01 2025
Journal Name
Fusion: Practice And Applications
Enhanced EEG Signal Classification Using Machine Learning and Optimization Algorithm
...Show More Authors

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

... Show More
View Publication
Scopus (4)
Crossref (2)
Scopus Crossref
Publication Date
Sun May 11 2025
Journal Name
Journal Of Information Systems Engineering And Management
The Effect of the Learning Mastery Strategy using Interactive Learning Techniques as a Therapeutic Method on the Achievement of Secondary School Students in Mathematics
...Show More Authors

  The current research aims to identify the effect of the learning mastery strategy using interactive learning as a therapeutic method on the achievement of secondary school students in mathematics. To achieve the research objective, the researcher selected second-grade middle school students at Al-Haybah Intermediate School for Boys and determined his research sample, which consisted of (77) students distributed into two sections: Section (A) the experimental group, with (38) students, and Section (B) the control group, with (39) students. The statistical equivalence of the two research sample groups was confirmed in the variables (intelligence test, previous achievement, and previous knowledge test). The researchers chose the par

... Show More
View Publication
Crossref
Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Multichannel Optimization With Hybrid Spectral- Entropy Markers for Gender Identification Enhancement of Emotional-Based EEGs
...Show More Authors

View Publication
Scopus (28)
Crossref (24)
Scopus Clarivate Crossref
Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
COMPARATIVE STUDY FOR EDGE DETECTION OF NOISY IMAGE USING SOBEL AND LAPLACE OPERATORS
...Show More Authors

Many approaches of different complexity already exist to edge detection in
color images. Nevertheless, the question remains of how different are the results
when employing computational costly techniques instead of simple ones. This
paper presents a comparative study on two approaches to color edge detection to
reduce noise in image. The approaches are based on the Sobel operator and the
Laplace operator. Furthermore, an efficient algorithm for implementing the two
operators is presented. The operators have been applied to real images. The results
are presented in this paper. It is shown that the quality of the results increases by
using second derivative operator (Laplace operator). And noise reduced in a good

... Show More
View Publication Preview PDF
Publication Date
Sun Feb 03 2019
Journal Name
Iraqi Journal Of Physics
Change detection of remotely sensed image using NDVI subtractive and classification methods.
...Show More Authors

Change detection is a technology ascertaining the changes of
specific features within a certain time Interval. The use of remotely
sensed image to detect changes in land use and land cover is widely
preferred over other conventional survey techniques because this
method is very efficient for assessing the change or degrading trends
of a region. In this research two remotely sensed image of Baghdad
city gathered by landsat -7and landsat -8 ETM+ for two time period
2000 and 2014 have been used to detect the most important changes.
Registration and rectification the two original images are the first
preprocessing steps was applied in this paper. Change detection using
NDVI subtractive has been computed, subtrac

... Show More
View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Wed Oct 06 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Image segmentation by using thresholding technique in two stages
...Show More Authors

View Publication
Scopus (2)
Scopus Crossref
Publication Date
Sun Oct 31 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Image segmentation by using thresholding technique in two stages
...Show More Authors

Image segmentation can be defined as a cutting or segmenting process of the digital image into many useful points which are called segmentation, that includes image elements contribute with certain attributes different form Pixel that constitute other parts. Two phases were followed in image processing by the researcher in this paper. At the beginning, pre-processing image on images was made before the segmentation process through statistical confidence intervals that can be used for estimate of unknown remarks suggested by Acho & Buenestado in 2018. Then, the second phase includes image segmentation process by using "Bernsen's Thresholding Technique" in the first phase. The researcher drew a conclusion that in case of utilizing

... Show More
View Publication
Crossref