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Main Difficulties Faced by EFL Students in Language Learning
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Many undergraduate learners at English departments who study English as a foreign language are unable to speak and use language correctly in their post -graduate careers. This problem can be attributed to certain difficulties, which they faced throughout their education years that hinder their endeavors to learn. Therefore, this study aims to discover the main difficulties faced by EFL students in language learning and test the difficulty variable according to gender and college variables then find suitable solutions for enhancing learning. A questionnaire with 15 items and 5 scales were used to help in discovering the difficulties. The questionnaire was distributed to the selected sample of study which consists of 90 (male and female) students selected randomly from the 3rd and 4th year class levels at English departments from colleges of Languages and Education (Ibn-Rushd) at the University of Baghdad. The results of the study showed that EFL students face difficulties in language learning such as the role of society in discouraging English language learning, the learners’ shyness, which prevents them from speaking English in fear of making mistakes, lack of motivation, and the influence of class size and crowdedness. After analyzing the results, some recommendations and suggestions were presented to solve the problem and eliminate difficulties.

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Reinforcement Learning-Based Television White Space Database
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Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-ba

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Publication Date
Sun Mar 15 2020
Journal Name
Journal Of Baghdad College Of Dentistry
Canine malposition in Iraqi dental students
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Background: This study aims to assess the prevalence of malposed canines among students of College of Dentistry/ University of Baghdad and evaluate the relation between canine malposition and occlusal features.

Material and method: The prevalence of buccally malposed canines was estimated by intra-oral visual examination of 250 young adult subjects (106 males and 144 females), their ages were between 19-24 years.

Results: The prevalence of the mandibular malposed canine (12%) was higher than the maxillary buccally malposed canine (10%). Generally, malposed canines were found higher in f

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Publication Date
Mon Feb 14 2022
Journal Name
Journal Of Educational And Psychological Researches
An Investigation of the Relationship between Writing Achievement and Writing Strategy Use by Secondary School Students
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Basically, this study aims to identify the extent to which Iraqi secondary school students use writing strategies and how proficiency level and students gender could affect writing strategy use. The study also examines the relationship between writing achievement and writing strategy use among Iraqi secondary school students. For this purpose, 140 Iraqi secondary school students were selected randomly from six different schools. Petric and Czarl’s questionnaire (2003) was adopted in the study as an instrument to collect the needed data. A software of SPSS used to analyze the collected data. The findings revealed that secondary school students appeared as low users of writing strategies; low proficient students do not show a sta

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Publication Date
Tue Aug 15 2023
Journal Name
Journal Of Economics And Administrative Sciences
Machine Learning Techniques for Analyzing Survival Data of Breast Cancer Patients in Baghdad
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The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

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Publication Date
Sat Dec 31 2022
Journal Name
International Journal On “technical And Physical Problems Of Engineering”
Age Estimation Utilizing Deep Learning Convolutional Neural Network
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Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes

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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Generative Adversarial Network for Imitation Learning from Single Demonstration
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Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

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Publication Date
Sun Feb 03 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The Effect of Orgnizational Learning in gnizational Effectifness: An Applied Study
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The purpose of this study is testing the effect of orgnizational learning in orgnizational Effectivness an applied study in Al-hiqma Jordinan Medecine Company . study sosiety 88 manegers sleect 80 of them .study used SPSS to test the hypothesis.study reachs to significant conculctions

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Publication Date
Wed Jan 01 2025
Journal Name
Journal Of Engineering And Sustainable Development
Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning Techniques
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Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

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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
Tue Jan 01 2019
Journal Name
Journal Of The College Of Languages (jcl)
Exploitation of the Democratization of Language in the Mass Media: Тенденция к демократизации языка в сми
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      In the present paper, the features of democratization of vocabulary in the language of mass media are tackled. Particularly, the functions of this phenomenon in the context of language as an ontological reality are analysed. As well, the growing role  of the language of the media, and the language of politics as an integral part pertaining to it, is noted. Further, attention is paid to the excessive saturation of the language of the media along with the literary language, by means of evaluation which may include negative results.

     In this concern, scholars had different views, specifically in relation to the use of colloquial vocabulary along with some vernacular profess

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