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jcoeduw-1361
Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Convolutional Neural Network (CNN) has been chosen as a better option for the training process because it produces a high accuracy. The final accuracy has reached 91.18% in five different classes. The results are discussed in terms of the probability of accuracy for each class in the image classification in percentage. Cats class got 99.6 %, while houses class got 100 %.Other types of classes were with an average score of 90 % and above.

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Publication Date
Thu Dec 01 2011
Journal Name
Journal Of Economics And Administrative Sciences
The effect of organizational learning dimensions on availability of learning organization dimensions in Iraqi planning ministry
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The aim of this research to study.

The dimensions of organizational learning have been defined(learning dynamics, individuals empowerment, knowledge management and technology application) as well as the dimensions of learning organization have been defined (culture values, knowledge transfer, communication and employee characteristics), Asset completion questionnaire was used to collect data of this research from a purposely sample represent forty employees who works in Iraqi Planning Ministry at different positions. The research divided to four parts :

The first to the research methodology, the second to the theoretical review o

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Publication Date
Mon Apr 04 2022
Journal Name
Journal Of Educational And Psychological Researches
Some Indicators of Learning Difficulties and their Relationship to the Self-Concept of Elementary School Students (Case Study)
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The aim of the research is to identify learning difficulties and their role in children's perception of self-concept. The researcher adopted the descriptive and analytical approach method in this study. A questionnaire was designed by the researcher to collect some related information such as biodata, family, health, diagnostic and behavioral patterns of the case. In addition, the researcher adopted the scale of learning difficulties for elementary school students prepared by Zaidan Ahmed Al-Sartawi (1995), the scale of student appreciation for the survey of learning difficulties for primary school students by Michael Best, which was translated to the Arabic language by (Saeed Abdullah Debis). The researcher adopted also the Self-Concept

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Publication Date
Wed Jan 02 2019
Journal Name
Journal Of The College Of Languages (jcl)
A Discourse Taxonomy of Language Cliched Units in English and Russian: Дискурсивная таксономия языковых клишированных единиц в английском и русском языках
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 The paper exposes a discourse taxonomy of language clichéd units in English and Russian, and presents some definitions of what is a cliché as being a semioticform of perceiving language and discourse. Also, it deals with language clichés as being units of structure reflecting a scheme of stereotypical situations of dialogue, whether among individuals of one culture, or in relation to cross-cultural communication. The paper proposes a taxonomy of units in language clichés in English and Russian by tackling clichés of daily use, such as professional, cultural, artistic, and scientific clichés.

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Publication Date
Tue Jul 31 2018
Journal Name
Journal Of Theoretical And Applied Information Technology
Classification and monitoring of autism using svm and vmcm
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Autism is a lifelong developmental deficit that affects how people perceive the world and interact with each others. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. The commonly used tools for analyzing the dataset of autism are FMRI, EEG, and more recently "eye tracking". A preliminary study on eye tracking trajectories of patients studied, showed a rudimentary statistical analysis (principal component analysis) provides interesting results on the statistical parameters that are studied such as the time spent in a region of interest. Another study, involving tools from Euclidean geometry and non-Euclidean, the trajectory of eye patients also showed interesting results. In this

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Publication Date
Tue Dec 03 2013
Journal Name
Ibn Al-haitham Journal For Pure And Applied Science
New adaptive satellite image classification technique for al Habbinya region west of Iraq
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Publication Date
Tue Jan 02 2018
Journal Name
Journal Of Educational And Psychological Researches
The impact of learning strategies in the collection of biology and their systemic thinking
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Research Summary The aim of the search for knowledge of the effect generative learning strategy in: 1 - Achievement of the second grade. 2 - Systemic thinking for the second grade students when studying the biology. The study sample increased (60) students distributed into two equal experimental and control groups. Prepare the test of 40 pieces of multiple choice type and prepare a test for systematic thinking according to three skills 1. Understand the relationships between the parts of the systemic form and complement the sentences given 2 - complement the relationships between parts of the systemic form 3. Building the systemic form. It was a search result 1- There is a difference of statistical significance (at level 0.05) between th

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Publication Date
Fri Apr 28 2023
Journal Name
Mathematical Modelling Of Engineering Problems
Design Optimal Neural Network for Solving Unsteady State Confined Aquifer Problem
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Publication Date
Sun Jun 06 2021
Journal Name
Materials
Strengthening of Continuous Reinforced Concrete Deep Beams with Large Openings Using CFRP Strips
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To accommodate utilities in buildings, different sizes of openings are provided in the web of reinforced concrete deep beams, which cause reductions in the beam strength and stiffness. This paper aims to investigate experimentally and numerically the effectiveness of using carbon fiber reinforced polymer (CFRP) strips, as a strengthening technique, to externally strengthen reinforced concrete continuous deep beams (RCCDBs) with large openings. The experimental work included testing three RCCDBs under five-point bending. A reference specimen was prepared without openings to explore the reductions in strength and stiffness after providing large openings. Openings were created symmetrically at the center of spans of the other specimens

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Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Physics
Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration
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         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmosp

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Publication Date
Sun May 01 2016
Journal Name
Journal Of Engineering
Prediction of Ryznar Stability Index for Treated Water of WTPs Located on Al-Karakh Side of Baghdad City using Artificial Neural Network (ANN) Technique
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In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respe

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