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Modeling Social Networks using Data Mining Approaches-Review
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     Getting knowledge from raw data has delivered beneficial information in several domains. The prevalent utilizing of social media produced extraordinary quantities of social information. Simply, social media delivers an available podium for employers for sharing information. Data Mining has ability to present applicable designs that can be useful for employers, commercial, and customers. Data of social media are strident, massive, formless, and dynamic in the natural case, so modern encounters grow. Investigation methods of data mining utilized via social networks is the purpose of the study, accepting investigation plans on the basis of criteria, and by selecting a number of papers to serve as the foundation for this article. Afterward a watchful evaluation of these papers, it has beeniscovered that numerous data extraction approaches were utilized with social media data to report a number of various research goals in several fields of industrial and service. Though, implementations of data mining are still raw and require more work via industry and academic world to prepare the work sufficiently. Bring this analysis to a close. Data mining is the most important rule for uncovering hidden data in large datasets, especially in social network analysis, and it demonstrates the most important social media technology.

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Publication Date
Sun Jun 12 2022
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Using Phone Calls to Promote Community Pharmacist Counselling during COVID-19 Pandemic in Baghdad, Iraq
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Drug consultation is an important part of pharmaceutical care. mobile phone call or text message can serve as an easy, effective, and implementable alternative to improving medication adherence and clinical outcomes by providing the information needed significantly for people with chronic illnesses like diabetes and hypertension particularly during pandemics like COVID-19 pandemic.

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Publication Date
Mon Dec 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
USING SENSITIVITY ANALYSIS IN DETERMINING THE OPTIMAL&EFFICIENT PRODUCTION PLANS IN GREENHOUSES IN ASSOCIATION OF AL-WATAN UNDER CONDITION OF RISK &UNCERTAINTY
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 The objectives of this research are to determine and find out the reality of crops structure of greenhouses in association of Al-Watan  in order to stand on the optimal use of economic resources available for the purpose of reaching a crop structure optimization of the farm that achieves maximize profit and gross and net farm incomes , using the method of linear programming to choose the farm optimal plan with the highest net income , as well as identifying production plans farm efficient with (income - deviation) optimal (E-A) of the Association and derived, which takes into account the margin risk wich derived from each plan using the model( MOTAD), as a model of models of linear programming alternative programming m

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Publication Date
Mon Aug 01 2016
Journal Name
Ieee Transactions On Neural Systems And Rehabilitation Engineering
Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering
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Publication Date
Mon Oct 01 2018
Journal Name
Iraqi Journal Of Physics
Classification of brain tumors using the multilayer perceptron artificial neural network
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Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect

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Publication Date
Thu Mar 01 2018
Journal Name
Journal Of Engineering
Permeability Estimation by Using the Modified and Conventional FZI Methods
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There many methods for estimation of permeability. In this Paper, permeability has been estimated by two methods. The conventional and modified methods are used to calculate flow zone indicator (FZI). The hydraulic flow unit (HU) was identified by FZI technique. This technique is effective in predicting the permeability in un-cored intervals/wells. HU is related with FZI and rock quality index (RQI). All available cores from 7 wells (Su -4, Su -5, Su -7, Su -8, Su -9, Su -12, and Su -14) were used to be database for HU classification. The plot of probability cumulative of FZI is used. The plot of core-derived probability FZI for both modified and conventional method which indicates 4 Hu (A, B, C and D) for Nahr Umr forma

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Publication Date
Wed Aug 16 2023
Journal Name
مجلة الاستاذ للعلوم الانسانية والاجتماعية- كلية التربية ابن رشد – جامعة بغداد،
Using the Theory of Conceptual Metaphor in Two Sonnets by Victorian Female Poets
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This study has applied the theoretical framework of conceptual metaphor theory to the analysis of the source and target domains of metaphors that are used in two English nineteenth century sonnets, both written by contemporaneous female poets. The quantitative and qualitative results of the textual analysis have clearly revealed that Elizabeth Barrett Browning’s sonnet 23 centres around the conceptual mapping of the journey of love and life with that of possession. In contrast, Christina Rossetti’s sonnet Remember tackles the central conceptual mapping of death as a journey in relation to its further experiential connections. In addition, the application of conceptual metaphor theory in identifying the frequencies and densities of metap

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Publication Date
Sun Dec 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Fractional Hold-Up in RDC Column Using Artificial Neural Network
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In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

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Publication Date
Sun Jan 30 2022
Journal Name
Iraqi Journal Of Science
A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms
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    Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accu

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Publication Date
Tue Sep 29 2020
Journal Name
Iraqi Journal Of Science
An Automated Classification of Mammals and Reptiles Animal Classes Using Deep Learning
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Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200

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Publication Date
Wed Feb 12 2025
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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