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Improving TF-IDF with Singular Value Decomposition (SVD) for Feature Extraction on Twitter
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Publication Date
Sun Jul 07 2013
Journal Name
Journal Of Educational And Psychological Researches
Effect of Using Computer in Getting and Remaining Information at Students of First Stage in Biology Subject
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Effect of Using Computer in Getting and Remaining Information at Students of First Stage in Biology Subject MIAAD NATHIM RASHEED LECTURER Abstract The recent research goal is to know the influence of computer use to earn and fulfillment information for students of first class in biology material and to achieve that put many of the zeroing hypothesis by researcher as follow: There were no differences between statistical signs at level (0,05) between the average students' marks who they were study by using computer and between the average student ' marks who they were study in classical method of earning and fulfillment. The researcher chose the intentional of the medical technical institute that included of two branches the first class (A

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Publication Date
Tue Dec 15 2020
Journal Name
Aip Conference Proceedings
The water bodies in the Southern East of Iraq before and after 2018
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This study is concerned with the recent changes that occurred in the last three years (2017-2019) in the marshes region in southern Iraq as a result of the changes in the global climate, the study included all the water bodies in the five governorates that are located in the southern regions of Iraq (Wasit, Maysan, Dhi-Qar, Qadisiyah and Basrah), which represent the marshes lands in Iraq. Scenes of the Landsat 8 satellite are used to create a mosaic to cover the five governorates within a time window with the slightest difference between the date of the scene capture, not to exceed 8 days. The results of calculating the changes in water areas were obtained using the classifier support vector machine, where high accuracy ratios were recorded

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Publication Date
Tue Dec 27 2022
Journal Name
2022 3rd Information Technology To Enhance E-learning And Other Application (it-ela)
Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

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Publication Date
Mon Apr 01 2019
Journal Name
Arpn Journal Of Engineering And Applied Sciences
Assessment of vegetable cover in south Iraq by remote sensing methods
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The vegetable cover plays an important role in the environment and Earth resource sciences. In south Iraq, the region is classified as arid or semiarid area due to the low precipitations and high temperature among the year. In this paper, the Landat-8 satellite imagery will be used to study and estimate the vegetable area in south Iraq. For this purpose many vegetation indices will be examined to estimate and extract the area of vegetation contain in and image. Also, the weathering parameters must be investigated to find the relationship between these parameters and the arability of vegetation cover crowing in the specific area. The remote sensing packages and Matlab written subroutines may be use to evaluate the results.

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Scopus
Publication Date
Sun Jun 12 2011
Journal Name
Baghdad Science Journal
Satellite Images Unsupervised Classification Using Two Methods Fast Otsu and K-means
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Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Engineering
Face-based Gender Classification Using Deep Learning Model
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

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

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Publication Date
Sat Jun 06 2020
Journal Name
Journal Of The College Of Education For Women
Obese Women and Choosing Ready-made Clothes: Difficulties and Choices
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Thisstudy aims to determine the specifications of obese women accordingto the heightand type of obesity. It also aimstoidentify the significance of differences in choosing ready-made clothes for the research sample. Finally, the significance of differences in choosing ready-made clothes according to the variable of binaryclassification ofobesity is also identified.The study sample includes obese women: employees, non-employees and students with the age group (18-50) years.The weights and lengths of the sample have been taken to suit the group of obese women.Aquestionnaire in the form of an open question was distributed among (50) obese womenso as to extract the items of the questionnaire. After that, the questionnaire was distributed amo

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Publication Date
Wed Mar 30 2022
Journal Name
Iraqi Journal Of Science
Monitoring Vegetation Area in Baghdad Using Normalized Difference Vegetation Index
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       Vegetation monitoring is considered an important application in remote sensing task due to variation of vegetation types and their distribution. The vegetation concentration around the Earth is increase in 5% in 2000 according to NASA monitoring. This increase is due to the Indian vegetable programs. In this research, the vegetation monitoring in Baghdad city was done using Normalized Difference Vegetation Index (NDVI) for temporal Landsat satellite images (Landsat 5 TM& Landsat 8 OIL). These images had been used and utilize in different times during the period from 2000, 2010, 2015 & 2017. The outcomes of the study demonstrate that a change in the vegetation Cover (VC) in Baghdad city. (NDVI) generally shows a

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Publication Date
Sun Dec 01 2019
Journal Name
Regenerative Therapy
In vitro periodontal ligament cell expansion by co-culture method and formation of multi-layered periodontal ligament-derived cell sheets
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Objective: Per-implantitis is one of the implant treatment complications. Dentists have failed to restore damaged periodontium by using conventional therapies. Tissue engineering (stem cells, scaffold and growth factors) aims to reconstruct natural tissues. The paper aimed to isolate both periodontal ligament stem cells (PDLSCs) and bone marrow mesenchymal stem cells (BMMSCs) and use them in a co-culture method to create three-layered cell sheets for reconstructing natural periodontal ligament (PDL) tissue. Materials and methods: BMMSCs were isolated from rabbit tibia and femur, and PDLSC culture was established from the lower right incisor. The cells were co-cultured to induce BMMSC differentiation into PDL cells. Cell morphology, stem cel

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