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Comparison of some artificial neural networks for graduate students
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Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, University of Baghdad for the period from 2014-2015 to The academic year 2017-2018. The variables are use in the research is (student’s success, age, gender, job, type of study (higher diploma, master’s, doctorate), specialization (statistics, economics, accounting, industry management, administrative management, and public administration) and channel acceptance). It became clear that the best variables that affect the success of graduate students are the type of study, age and job.                                                                                     

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Publication Date
Wed Aug 17 2022
Journal Name
Applied Sciences
Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange
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The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a co

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Publication Date
Mon Sep 30 2024
Journal Name
Iraqi Journal Of Science
Attention-Deficit Hyperactivity Disorder Prediction by Artificial Intelligence Techniques
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Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

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Publication Date
Wed Mar 30 2022
Journal Name
Journal Of Educational And Psychological Researches
Environmental Stress and their relationship to self-rebellion for Preparatory Stage Students
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The Present research aimed at identifying:   

1- The level of environmental stress among preparatory students

2- The level of self-rebellion  among preparatory students

3- The correlation between the two variables of research (environmental stress and self-rebellion) and the extent to which the independent variable contributes to the variable of the middle school students

The current research has determined the students of the fifth stage of the preparatory stage and all the branches in the departments of education in Baghdad province the morning study for the academic

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Agricultural And Statistical Sciences,
The comparison of several methods for calculating the degree of heritability and calculating the number of genes II. Yield components
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Publication Date
Fri Mar 15 2019
Journal Name
Journal Of The College Of Education For Women
Exploring the Effectiveness of two Scales for Measuring Cultural Intelligence of the Preparatory School Students
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The present study aims at exploring tow cultural intelligence scales of preparatory school students. It also aims at finding out the statistically significant differences according to gender and specification. Accordingly, the present study seeks to answer the following questions:

  1. Is there cultural intelligence of the preparatory school students?
  2. Is there any statistically significant differences according to gender and specification variables?
  3. Is there a scale more effective than cultural intelligence scales?

The stratified random sampling method is used to for selecting the sample of (216) students of scientific and humanistic specifications from

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Publication Date
Wed Mar 23 2022
Journal Name
Journal Of Educational And Psychological Researches
The Effectiveness of a Training Program for the Development of Moral Intelligence among Preparatory Students
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This current research aims to identify the effectiveness of a training program in developing moral intelligence and mutual social confidence among middle school students. The researcher made a number of hypotheses for this purpose to achieve the goal of the research.                             

The researcher relied on the (Al Zawaida 2011) scale prepared according to Coles (1997), including (60) items, and the mutual social trust scale for (Nazmi 2001) based on Roter's theory including (38) items.               &nbsp

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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach
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Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both so

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Publication Date
Sat May 01 2010
Journal Name
Journal Of Baghdad College Of Dentistry
Comparison of microleakage Restorative Dentistry Comparison of microleakage in three different retrograde cavity preparations with Mineral Trioxide Aggregate as filling material
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Background: The apical seal is the single most important factor in determining the success of surgical endodontics, the aim of this study was to compare the sealing ability of Mineral Trioxide Aggregate in three different cavity designs. Materials and Methods: Thirty extracted human single-rooted teeth were divided into three groups of ten teeth per group, a retrograde cavity preparation was carried out using a low speed handpiece and round bur with parallel walls in the first group, ultrasonic retrotip and unit in the second group and a low speed handpiece with a carbide inverted cone bur with undercuts in the third group, all the cavities were filled with MTA. microleakage was measured by dye penetration technique using methylene blue. Re

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Publication Date
Sat Jan 01 2022
Journal Name
Proceedings Of International Conference On Computing And Communication Networks
Speech Age Estimation Using a Ranking Convolutional Neural Network
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Publication Date
Tue Dec 31 2024
Journal Name
Journal Of Soft Computing And Computer Applications
Enhancing Image Classification Using a Convolutional Neural Network Model
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In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

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