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Attention Mechanism Based on a Pre-trained Model for Improving Arabic Fake News Predictions
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     Social media and news agencies are major sources for tracking news and events. With these sources' massive amounts of data, it is easy to spread false or misleading information. Given the great dangers of fake news to societies, previous studies have given great attention to detecting it and limiting its impact. As such, this work aims to use modern deep learning techniques to detect Arabic fake news. In the proposed system, the attention model is adapted with bidirectional long-short-term memory (Bi-LSTM) to identify the most informative words in the sentence. Then, a multi-layer perceptron (MLP) is applied to classify news articles as fake or real. The experiments are conducted on a newly launched Arabic dataset called the Arabic Fake News Dataset (AFND). The AFDN dataset contains exactly 606912 news articles collected from multiple sources, so it is suitable for deep learning requirements. Both simple recurrent neural networks (S-RNN), long short-term memory (LSTM), and gated recurrent units (GRU) are used for comparison. According to evaluation criteria, our proposed model achieved an accuracy of (0.8127), which is the best and highest accuracy among the deep learning methods used in this work. Moreover, the performance of our proposed model is better compared to previous studies, which used the AFND.

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Publication Date
Thu Dec 26 2024
Journal Name
Al–bahith Al–a'alami
News Coverage of Iraq’s International Relations in Iraqis News Channel
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The research aims to identify the new reality of Iraq’s international relations through the news
coverage carried out by Al-Iraqiya TV since the size and nature of the coverage indicate the extent
to which these relations have reached. The research problem is summarized by the main question
(what is the size and nature of news coverage of Iraq’s international relations in the Iraqi News
Channel?).
This research is considered a descriptive research, as the researchers used the survey method and the
content analysis method through a partial confinement of the research community consisting of the
main news broadcast for one programmatic period. The content analysis form that was subjected to
evaluation was design

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Heart Disease Classification–Based on the Best Machine Learning Model
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    In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne

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Publication Date
Thu Nov 21 2019
Journal Name
Journal Of Engineering
A Neural Networks based Predictive Voltage-Tracking Controller Design for Proton Exchange Membrane Fuel Cell Model
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In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

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Publication Date
Thu Oct 01 2015
Journal Name
Al–bahith Al–a'alami
Correspondents and Professional Standards in News Coverage (A Field Study)
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         The essence of the new work in the satellite TV channels is to provide news coverage of news that will inform the people of what is going on around them in order to increase their political, social, economic and cultural awareness and this drives them to take positions or certain behaviors on according to what the communicator in these channels wants. News and news reports are generally used as a psychological variable to influence public opinion and does not offer interestingness and information. Therefore, satellite TV channels have assumed special attention towards their correspondents desiring to achieve scoop in news coverage and to have the final word in reading events and install it

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Publication Date
Sat Aug 01 2015
Journal Name
International Journal Of Computer Science And Mobile Computing
Image Compression based on Non-Linear Polynomial Prediction Model
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Publication Date
Fri Feb 04 2022
Journal Name
Iraqi Journal Of Science
AHeuristic Strategy for Improving the Performance of Evolutionary Based Complex Detection in Protein-Protein Interaction Networks
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One of the most interested problems that recently attracts many research investigations in Protein-protein interactions (PPI) networks is complex detection problem. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem wherein, recently, the field of Evolutionary Algorithms (EAs) reveals positive results. The contribution of this work is to introduce a heuristic operator, called protein-complex attraction and repulsion, which is especially tailored for the complex detection problem and to enable the EA to improve its detection ability. The proposed heuristic operator is designed to fine-grain the structure of a complex by dividing it into two more complexes, each being distinguished with a core pr

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Publication Date
Thu Oct 18 2018
Journal Name
Al–bahith Al–a'alami
The Pressures Affecting the Performance of Iraqi Media before the Legislative Elections of 2018 (Iraqi Satellite News Channel- as a Model): A Survey Study  Dr.Safad Husam Hammody
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The modernity of election practices of the elections in Iraq, according to the democratic approach, has led to a struggle between political rival forces reflecting a deep pressure on the tools involved in the management, marketing or control of these elections across the general social level. Hence the problem of research resides in answering the following question: What is the nature and size of the pressures affecting the media performance of Al-Iraqia News channel before the legislative elections of 2018 in Iraq?
      The objectives of the research were the following:
1. to identify the nature of the pressures that limit the Al-Iraqia News channel’s perfo

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Publication Date
Thu Feb 28 2019
Journal Name
Iraqi Journal Of Science
Arabic Handwriting Word Recognition Based on Scale Invariant Feature Transform and Support Vector Machine
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Offline Arabic handwritten recognition lies in a major field of challenge due to the changing styles of writing from one individual to another. It is difficult to recognize the Arabic handwritten because of the same appearance of the different characters.  In this paper a proposed method for Offline Arabic handwritten recognition. The   proposed method for recognition hand-written Arabic word without segmentation to sub letters based on feature extraction scale invariant feature transform (SIFT) and   support vector machines (SVMs) to enhance the recognition accuracy. The proposed method  experimented using (AHDB) database. The experiment result  show  (99.08) recognition  rate.

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Publication Date
Tue Jan 04 2022
Journal Name
Iraqi Journal Of Science
Proposed Handwriting Arabic Words classification Based On Discrete Wavelet Transform and Support Vector Machine
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A proposed feature extraction algorithm for handwriting Arabic words. The proposed method uses a 4 levels discrete wavelet transform (DWT) on binary image. sliding window on wavelet space and computes the stander derivation for each window. The extracted features were classified with multiple Support Vector Machine (SVM) classifiers. The proposed method simulated with a proposed data set from different writers. The experimental results of the simulation show 94.44% recognition rate.

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Publication Date
Fri Aug 10 2018
Journal Name
In Book: Challenges In Mechanics Of Time-dependent Materials, Volume 2edition: 1stchapter: 11
A Case Study to Evaluate Live Load Distributions for Pre-stressed RC Bridge
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