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bsj-5597
Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System
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              Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree.

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Publication Date
Thu Nov 30 2023
Journal Name
Iraqi Journal Of Science
COVID-19 Detection via Blood Tests using an Automated Machine Learning Tool (Auto-Sklearn)
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     Widespread COVID-19 infections have sparked global attempts to contain the virus and eradicate it. Most researchers utilize machine learning (ML) algorithms to predict this virus. However, researchers face challenges, such as selecting the appropriate parameters and the best algorithm to achieve an accurate prediction. Therefore, an expert data scientist is needed. To overcome the need for data scientists and because some researchers have limited professionalism in data analysis, this study concerns developing a COVID-19 detection system using automated ML (AutoML) tools to detect infected patients. A blood test dataset that has 111 variables and 5644 cases was used. The model is built with three experiments using Python's Auto-

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Publication Date
Sat Jan 30 2021
Journal Name
Iraqi Journal Of Science
A mathematical model for the dynamics of COVID-19 pandemic involving the infective immigrants
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‎  Since the first outbreak in Wuhan, China, in December 31, 2019, COVID-19    pandemic  ‎has been spreading to many countries in the world. The ongoing COVID-19 pandemic has caused a ‎major global crisis, with 554,767 total confirmed cases, 484,570 total recovered cases, and ‎‎12,306 deaths in Iraq as of February 2, 2020. In the absence of any effective therapeutics or drugs ‎and with an unknown epidemiological life cycle, predictive mathematical models can aid in ‎the understanding of both control and management of coronavirus disease. Among the important ‎factors that helped the rapid spread of the ep

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Publication Date
Tue May 30 2023
Journal Name
Iraqi Journal Of Science
Deep Convolutional Neural Network Architecture to Detect COVID-19 from Chest X-Ray Images
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      Today, the world is living in a time of epidemic diseases that spread unnaturally and infect and kill millions of people worldwide. The COVID-19 virus, which is one of the most well-known epidemic diseases currently spreading, has killed more than six million people as of May 2022. The World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) after an outbreak of SARS-CoV-2 infection. COVID-19 is a severe and potentially fatal respiratory disease caused by the SARS-CoV-2 virus, which was first noticed at the end of 2019 in Wuhan city. Artificial intelligence plays a meaningful role in analyzing medical images and giving accurate results that serve healthcare workers, especially X-ray images, which are co

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Publication Date
Sun Aug 01 2021
Journal Name
Bulletin Of Electrical Engineering And Informatics
A critical success factors for software project manager in GVTs within covid-19 pandemic
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The covid-19 pandemic sweeping the world and has rendered a large proportion of the workforce as they are unable to commute to work. This has resulted in employees and employers seeking alternative work arrangements, including the software industry. Then comes the need for the global market and international presence of many companies to implement the global virtual teams (GVTs). GVTs members are gradually engaged in globalized business environments across space, time and organizational boundaries via information and communication technologies. Despite the advancement of technology, the project managers are still facing many challenges in communication. Hense, to become a successful project manager still a big challenge for them. This study

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Publication Date
Fri Jun 03 2022
Journal Name
Military Medical Science Letters
COVID-19 AMONG A SAMPLE OF IRAQI PATIENTS WITH RHEUMATIC DISEASES: A MULTICENTER STUDY
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Publication Date
Thu Nov 30 2023
Journal Name
Iraqi Journal Of Science
Machine Learning Approach for New COVID-19 Cases Using Recurrent Neural Networks and Long-Short Term Memory
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     This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being  0.66975075, 0.470

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Publication Date
Sun Sep 03 2023
Journal Name
Wireless Personal Communications
Application of Healthcare Management Technologies for COVID-19 Pandemic Using Internet of Things and Machine Learning Algorithms
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Publication Date
Thu Mar 09 2023
Journal Name
Plos One
A cross-sectional analysis of the predictors of COVID-19 vaccine uptake and vaccine hesitancy in Iraq
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Background

Vaccine hesitancy poses a significant risk to global recovery from COVID-19. To date however, there is little research exploring the psychological factors associated with vaccine acceptability and hesitancy in Iraq.

Aim

To explore attitudes towards COVID-19 vaccination in Iraq. To establish the predictors of vaccine uptake and vaccine hesitancy in an Iraqi population.

Methods

Using a cross-sectional design, 7,778 participants completed an online questionnaire exploring their vaccination status, likelihood of infection, perc

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Publication Date
Sun Apr 09 2023
Journal Name
Bmj
COVID-19 Vaccine Uptake And Its’ Associated Factors among general population In Basmaia City in Baghdad 2022
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Abstract<sec><title>Objective

Vaccination is a vital cornerstone of public health, which has saved countless lives throughout history. Therefore, achieving high vaccination uptake rates is essential for successful vaccination programs. Unfortunately, vaccine uptake has been hindered by deferent factors and challenges. The objective of this study is to assess COVID-19 vaccine uptake and associated factors among the general population.

Methods

This study is a descriptive cross-sectional study conducted in Basmaia city, Baghdad from June to October 2022. Data were collected through a semi-structured questionnaire using multi-stag

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Publication Date
Wed Apr 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
A specimen of the internal control system on the stock in light of the application JIT system In construction companies
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Suffer most of the facilities of the high cost of inventory , which affects the high cost of the product and thus affects many administrative decisions , as well as suffer the facilities of the systems developed by the provisions of inventory control , and this problem is exacerbated in the construction sector where the inventory in the form of Construction spin of the Year for another it becomes difficult to control the cost effectively , and is the research problem in question follows: What are the implications of the use of the system in time inventory accounting system for the contracting company does kills Alrkaah to the provisions of the cost of inventory and what is the optimal approach to inventory control ? Find assumed

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