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Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset
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The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA molecule. A sequence DL model based on a bidirectional gated recurrent unit (GRU) is implemented. The model is applied to the Stanford COVID-19 mRNA vaccine dataset to predict the mRNA sequences deterioration by predicting five reactivity values for every base in the sequence, namely reactivity values, deterioration rates at high pH, at high temperature, at high pH with Magnesium, and at high temperature with Magnesium. The Stanford COVID-19 mRNA vaccine dataset is split into the training set, validation set, and test set. The bidirectional GRU model minimizes the mean column wise root mean squared error (MCRMSE) of deterioration rates at each base of the mRNA sequence molecule with a value of 0.32086 for the test set which outperformed the winning models with a margin of (0.02112). This study would help other researchers better understand how to forecast mRNA sequence molecule properties to develop a stable COVID-19 vaccine.

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Publication Date
Fri Dec 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
Analysis the Impact of Corruption on Investment in Iraq
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There are different types of corruptions such as administrative, political, economic and financial corruption. The corruption forms also varied such as bribery, nepotism and extortion. All types and forms of corruption play significant role in the all economic variables generally and on investments in particular, and the corruption used to be an intermediate means in reducing the rate of economic growth.  The corruption contributes in reducing the domestic investments via pay bribery by investors to officials’ persons for supplemental contracts and tenders which finally leads to reduction in the investment efficiency. The corruption also contributes in rise of operational costs for the investment projects.  In additio

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Publication Date
Mon Jan 02 2017
Journal Name
Journal Of Educational And Psychological Researches
Toward a theoretical model of the terrorist personality and differentiation from psychopathic personality
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The current theoretical research targeted to construct a model of terrorist personality and its differentiation from psychopathic personality . Several assumptions or theories of perspectives of psychopathic personality have been compared with the terrorist personality studies that concerned . The suggested theoretical  model is interrupting the terrorist personality . The conclusions , discussions are mentioned. Finally, recommendation is suggested .

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Publication Date
Sun Mar 19 2023
Journal Name
Journal Of Educational And Psychological Researches
The Effect of Using the Generative Learning Model on the Achievement of First-Grade Intermediate Students of Chemical Concepts in Science
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Abstract

The current research aims to identify the effect of using a model of generative learning in the achievement of first-middle students of chemical concepts in science. The researcher adopted the null hypothesis, which is there is no statistically significant difference at the level (0.05) between the mean scores of the experimental group who study using the generative learning model and the average scores of the control group who study using the traditional method in the chemical concepts achievement test. The research consisted of (200) students of the first intermediate at Al-Farqadin Intermediate School for Boys affiliated with the Directorate of General Education in Baghdad Governorate / Al-Karkh 3 wit

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Publication Date
Mon Mar 01 2010
Journal Name
Journal Of Economics And Administrative Sciences
The study of the effect of the use of programming in the linear programming model (applied study)
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The parametric programming considered as type of sensitivity analysis. In this research concerning to study the effect of the variations on linear programming model (objective function coefficients and right hand side) on the optimal solution. To determine the parameter (θ) value (-5≤ θ ≤5).Whereas the result، the objective function equal  zero and the decision variables are non basic، when the parameter (θ = -5).The objective function value increases when the parameter (θ= 5) and the decision variables are basic، with the except of X24, X34.Whenever the parameter value increase, the objectiv

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Publication Date
Mon Jan 02 2017
Journal Name
Journal Of Educational And Psychological Researches
The effectiveness of the structural model of learning in the acquisition of geographical concepts among students of the first grade average)
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The current research aims to find out ( the effectiveness of the structural model of learning in the acquisition of geographical concepts at the first grade average students ) , and achieving the goals of research has been formulating the null hypothesis of the following :

    " There is no difference statistically significant when Mistoi (0.5 ) between the mean scores of the collection of students in the experimental group that is studying the general geographical principles " Bonmozj constructivist learning " and the mean scores of the control group , which is considering the same article ," the traditional way " to acquire concepts.

The researcher adopted th

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
An Effective Hybrid Deep Neural Network for Arabic Fake News Detection
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Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Modified Mathematical Model of Tumor Treatment by Radiotherapy
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In this research, a mathematical model of tumor treatment by radiotherapy is studied and a new modification for the model is proposed as well as introducing the check for the suggested modification. Also the stability of the modified model is analyzed in the last section.

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Publication Date
Thu Dec 15 2022
Journal Name
Bionatura
Wheat seed deterioration stimulated by plant extracts
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During 2019-2020, the experiment was conducted in the laboratory of the Department of Field Crop Sciences, Faculty of Agricultural Engineering Sciences - Baghdad University, to investigate the impact of soaking wheat seeds produced during the 2016 agricultural season with three plant extracts (licorice root extract 2%, 4% and 6%, Acadian and Humic(500, 1000, & 1500 mg L-1). Aside from the two control treatments (soaking in distilled water with dried seeds). The results show that the soaking treatment with licorice root extract outperformed the other therapies in conventional laboratory germination, root length, and seedling vigor index (95 percent and 3.42 cm 1207) compared to the two control treatments (soaking with distilled w

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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
An overview of machine learning classification techniques
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Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Face Recognition and Emotion Recognition from Facial Expression Using Deep Learning Neural Network
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Abstract<p>Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.</p>
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