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Improving Measurement of Effectiveness of Blended Learning in Iraqi Education Using SVM
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E-learning has recently become of great importance, especially after the emergence of the Corona pandemic, but e-learning has many disadvantages. In order to preserve education, some universities have resorted to using blended learning. Currently, the Ministry of Higher Education and Scientific Research in Iraq has adopted e-learning in universities and schools, especially in scientific disciplines that need laboratories and a spatial presence. In this work, we collected a dataset based on 27 features and presented a model utilizing a support vector machine with regression that was enhanced with the KNN method, which identifies factors that have a substantial influence on the model for the type of education, whether blended or traditional.
Furthermore, the dataset used was primarily focused on three key factors: personal information, the impact of e-Learning platforms, and the influence of the Corona virus. The attributes that were measured revealed that social status, computer skills, and the basic platform gave the user enough tools to continue the learning process. The size of the classrooms and laboratories that meet the health safety conditions is the most significant. The goal of our work is to discover a model that predicts how blended learning will be used during and after the coronavirus pandemic and to produce a model with minimal errors.

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Publication Date
Mon Jun 15 2020
Journal Name
Al-academy
The effectiveness of the constructivist learning model in acquiring the Institute of Fine Arts' students of artistic analysis skills: اخلاص عبد القادر طاهر
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The constructivist learning model is one of the models of constructivist theory in learning, as it generally emphasizes the active role of the learner during learning, in addition to that the intellectual and actual participation in the various activities to help students gain the skills of analyzing artistic works. The current research aims to know the effectiveness of the constructivist learning model in the acquisition of the skills of the Institute of Fine Arts for the skills of (technical work analysis). To achieve the goal, the researcher formulated the following hypothesis: There are no statistically significant differences between the average scores of the experimental group students in the skill test for analyzing artworks befor

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Publication Date
Tue Mar 01 2016
Journal Name
Al-academy
The effectiveness of visual intelligence strategy in the collection of students in the Department of Art Education in the perspective material: أسامة حسن عبد علي الصفار
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Select the researcher discussed problem of asking the following : Do you use visual intelligence strategy effective in the collection of students in the Department of Art Education in the foreseeable material ? The research aims to " measure the effectiveness of the strategy in the collection of visual intelligence students in the Department of Art Education in the foreseeable material ". To verify the objective of this research was identify hypotheses zero to measure the level of achievement in the foreseeable material second grade students in the Department of Art Education - Faculty of Fine Arts . The population of the research students in the Department of Art Education / Faculty of Fine Arts at the University of Baghdad who are stud

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers
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Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

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Publication Date
Fri Aug 26 2022
Journal Name
Journal Of Contemporary Medical Sciences
Measurement of the serum level of Leucine-rich alpha-2-glycoprotein-1 in hospitalized Iraqi COVID-19 Patients
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Objective: The study aimed to assess Leucine-rich alpha-2-glycoprotein-1 biomarker serum level in hospitalized COVID-19 patients. Methods: The case control study from multi-centers in Baghdad included 45 adult patients (19 females and 26 males) with COVID-19, diagnosed with a positive real-time reverse transcription polymerase chain reaction and excluded negative RT-PCR for COVID-19 and comorbidity conditions. Second group, was 43 control (20 females and 23 males). Results: This study found a decrease Leucine-rich alpha-2-glycoprotein-1 biomarker serum level in these patients and a significant difference in D. dimer, neutrophil count, lymphocyte count, and the neutrophil-lymphocyte ratio between the patients and controls at a P valu

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Publication Date
Wed Mar 28 2018
Journal Name
Iraqi Journal Of Science
Radon Concentration Measurement in Ainkawa Region Using Solid State Nuclear Track Detector
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Radon is the air contaminant radioactive gas which people exposed to, is a reason for lung damages and lung cancer. The areas that are subject to high radon levels are found by radon concentration measurement. The radon activity concentration, annual effective dose, and potential alpha energy concentration (PAEC), were measured in houses of Ainkawa region using CR-39 solid state nuclear track detectors SSNTDs with the sealed-can technique. In the present paper the estimated values for radon activity concentration are in the range 55.99-112.8 Bq/m3 with 84.30 Bq/m3 as a mean value, the range of annual effective dose are 1.411-2.872 mSv/y, with mean value 2.124 mSv/y, and the potential alpha energy c

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Publication Date
Mon May 06 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
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The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying

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Publication Date
Sat Mar 11 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Measurement of Ferritin and Transforming Growth Factor-β1 Levels in Iraqi Women with Polycystic Ovary Syndrome.
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      Background: Polycystic ovary syndrome (PCOS) is common heterogeneous disorder syndrome in females, characterized by chronic oligoovulation, polycystic ovary, and hyperandrogenism. This study aimed to the association of ferritin and transforming growth factor- β1 (TGF-β1) levels with insulin resistance, cardiovascular and type 2 diabetes risks. Patients and methods: (61) Iraqi women with PCOS patients diagnosed according to the Rotterdam criteria, were subdivided according to their Body Mass Index (BMI) to: (20) lean women with normal BMI: (18-24), (17) overweight women with BMI: (25-29) and (25) obese women with BMI >30. For the the purpose of comparison, (20) healthy Iraqi women were enrolled as controls ma

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Publication Date
Thu Mar 10 2022
Journal Name
International Journal Of Early Childhood Special Education
The effect of reciprocal style exercises in developing some physical abilities in learning the performance of female players for the effectiveness of the long jump
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The importance of the research lies in knowing the effect of the exercises of the reciprocal method in developing some physical abilities in learning the performance of the players for the effectiveness of the long jump in an economical manner in terms of time and effort and knowing their positive impact and the extent of their impact in creating the required learning for students, and the research aims to prepare reciprocal style exercises in developing some abilities The researchers used the experimental method in the pre and post test for the experimental and control groups to suit the nature of the research, and the research community was identified for the long jump players, the Specialized School for Talent Care in the 2022 sports sea

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Energy Consumption Prediction of Smart Buildings by Using Machine Learning Techniques
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     This paper presents an IoT smart building platform with fog and cloud computing capable of performing near real-time predictive analytics in fog nodes. The researchers explained thoroughly the internet of things in smart buildings, the big data analytics, and the fog and cloud computing technologies. They then presented the smart platform, its requirements, and its components. The datasets on which the analytics will be run will be displayed. The linear regression and the support vector regression data mining techniques are presented. Those two machine learning models are implemented with the appropriate techniques, starting by cleaning and preparing the data visualization and uncovering hidden information about the behavior of

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Publication Date
Mon Jan 27 2020
Journal Name
Iraqi Journal Of Science
Sentiment Analysis in Social Media using Machine Learning Techniques
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Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show

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