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jeasiq-1345
Use some statistical algorithms in mock hacking satellite image
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In the recent years, remote sensing applications have a great interest because it's offers many advantages, benefits and possibilities for the applications that using this concept, satellite it's one must important applications for remote sensing, it's provide us with multispectral images allow as study many problems like changing in ecological cover or biodiversity for earth surfers, and illustrated biological diversity of the studied areas by the presentation of the different areas of the scene taken depending on the length of the characteristic wave, Thresholding it's a common used operation for image segmentation, it's seek to extract a monochrome image from gray image by segment this image to two region (foreground & background) depending on pixels intensity to reducing image distortion, and also separated the target area from the rest of scene features under study, so we seek to used number of  thresholding techniques in this paper for clarify the importance of this concept in image processing and we proposed a new statistical thresholding techniques which compared with techniques used, and the result showed the advantage of proposed techniques that achieved from applying the techniques on multispectral satellite image takin for an area west of Iraq that characterized their environmental diversity so it's a good case to study.

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Publication Date
Sun Jul 29 2018
Journal Name
Iraqi Journal Of Science
Cuneiform Tablets Image Preprocessing Proposed Algorithms Techniques for Pattern Recognition
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Cuneiform symbols recognition represents a complicated task in pattern recognition  and image analysis as a result of problems that related to cuneiform symbols like distortion and unwanted objects that associated with applying Binrizetion process like spots and writing lines. This paper aims to present new proposed algorithms to solve these problems for reaching uniform results about cuneiform symbols recognition that related to (select appropriate Binerized method, erased writing lines and spots) based on statistical Skewness measure, image morphology and distance transform concepts. The experiment results show that our proposed algorithms have excellent result and can be adopted

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Publication Date
Mon Oct 10 2016
Journal Name
Iraqi Journal Of Science
Satellite image classification using KL-transformation and modified vector quantization
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In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water

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Publication Date
Sun Sep 04 2011
Journal Name
Baghdad Science Journal
Study of contrast between satellite image data and ground data
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Spot panchromatic satellite image had been employed to study and know the difference Between ground and satellite data( DN ,its values varies from 0-255) where it is necessary to convert these DN values to absolute radiance values through special equations ,later it converted to spectral reflectance values .In this study a monitoring of the environmental effect resulted from throwing the sewage drainages pollutants (industrial and home) into the Tigris river water in Mosul, was achieved, which have an effect mostly on physical characters specially color and turbidity which lead to the variation in Spectral Reflectance of the river water ,and it could be detected by using many remote sensing techniques. The contaminated areas within th

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Publication Date
Sun Sep 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Use some probability amputated models to study the characteristics of health payments in the Iraqi Insurance Company
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Abstract

Due to the lack of previous statistical study of the behavior of payments, specifically health insurance, which represents the largest proportion of payments in the general insurance companies in Iraq, this study was selected and applied in the Iraqi insurance company.

In order to find the convenient model representing the health insurance payments, we initially detected two probability models by using (Easy Fit) software:

First, a single Lognormal for the whole sample and the other is a Compound Weibull  for the two Sub samples (small payments and large payments), and we focused on the compoun

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Publication Date
Thu Dec 19 2024
Journal Name
Iraqi Journal For Administrative Sciences
Using the Statistical Analysis to study the important reasons of the pollution in the Iraqi Marshlands Areas
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The need for detection and investigation of the causes of pollution of the marshes and submit a statistical study evaluated accurately and submitted to the competent authorities and to achieve this goal was used to analyze the factorial analysis and then obtained the results from this analysis from a sample selected from marsh water pollutants which they were: (Electrical Conductivity: EC, Power of Hydrogen: PH, Temperature: T, Turbidity: TU, Total Dissolved Solids: TDS, Dissolved Oxygen: DO). The size of sample (44) sites has been withdrawn and examined in the laboratories of the Iraqi Ministry of Environment. By illustrating SPSS program) the results had been obtained. The most important recommendation was to increase the pumping of addit

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Publication Date
Tue Dec 31 2019
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
USE OF IMMOBILIZED L-ARABINOSE ISOMERASE FOR PRODDUCTION OF TAGATOSE: USE OF IMMOBILIZED L-ARABINOSE ISOMERASE FOR PRODDUCTION OF TAGATOSE
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L-arabinose isomerase from Escherichia coli O157:H7 Was immobilized with activated Bentonite from local markets of Baghdad, Iraq by 10% 3-APTES and treated with 10% aqueous glutaraldehyde, the results refer that the yield of immobilization was 89%, and pH profile of free and immobilized L-arabinose isomerase was 7 and 7.5 and it is stable at 6-8 for 60 min respectively, while, the optimum temperature was 30 and 35°C and it was stable at 35 and 40°C for 60 min but it loses more than 60 and 30% from its original activity at 50°C for free and immobilized L-arabinose isomerase respectively. Immobilized enzyme retained its full activity for 32 day, but it retained 73.58% of its original activity after storage for 60 d

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Publication Date
Wed Apr 10 2019
Journal Name
Engineering, Technology & Applied Science Research
Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm
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Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Oil spill classification based on satellite image using deep learning techniques
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 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

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Publication Date
Sat Oct 01 2022
Journal Name
Al–bahith Al–a'alami
The evolution of the press image in the Iraqi press (An analytical study of the pictures of the first page of Al-Sabah newspaper for the year)
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This research sheds light on the development that has occurred on the use of press pictures in the Iraqi press by analyzing the form and content of the images used in (Al-Sabaah) newspaper for the 2012 model. The researcher's interest in this topic for what represented by the press picture of a way to highlight and attract the reader's attention to the substance journalist who represents the goals behind the newspaper publishing.

This research is divided into three sections. The first one dealt with the methodological framework of the research, and the second part addressed the aspect of press pictures and its history and technological development, while the third section devoted to the study of the development of the form and co

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Publication Date
Thu Dec 30 2021
Journal Name
Iraqi Journal Of Science
Image Segmentation Using PSO-Enhanced K-Means Clustering and Region Growing Algorithms
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    Image segmentation is a basic image processing technique that is primarily used for finding segments that form the entire image. These segments can be then utilized in discriminative feature extraction, image retrieval, and pattern recognition. Clustering and region growing techniques are the commonly used image segmentation methods. K-Means is a heavily used clustering technique due to its simplicity and low computational cost. However, K-Means results depend on the initial centres’ values which are selected randomly, which leads to inconsistency in the image segmentation results. In addition, the quality of the isolated regions depends on the homogeneity of the resulted segments. In this paper, an improved K-Means

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