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Post COVID-19 Effect on Medical Staff and Doctors' Productivity Analysed by Machine Learning
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The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. These results reflect the overall impact observed following the entire course of the COVID-19 pandemic and are not specific to a particular wave. The analysis revealed that older participants experienced a more pronounced decline in productivity, with a mean decrease of 35% compared to younger participants. Female participants, on average, had a 28% decrease in productivity compared to their male counterparts. Moreover, individuals with lower socioeconomic status exhibited a substantial decline in productivity, experiencing an average decrease of 40% compared to those with higher socioeconomic status. Similarly, participants who slept for fewer hours per night had a significant decline in productivity, with an average decrease of 33% compared to those who had sufficient sleep. The machine learning analysis identified age, specialty, COVID-19 complications, socioeconomic status, and sleeping time as crucial predictors of productivity score. The study highlights the significant impact of post-COVID-19 on the productivity of medical staff and doctors in Iraq. The findings can aid healthcare organizations in devising strategies to mitigate the negative consequences of COVID-19 on medical staff and doctors' productivity.

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Publication Date
Fri Feb 04 2022
Journal Name
Neuroquantology
Detecting Damaged Buildings on Post-Hurricane Satellite Imagery based on Transfer Learning
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In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa

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Publication Date
Sat Sep 30 2023
Journal Name
Al–bahith Al–a'alami
The Iraqi Journalistic Treatment of COVID-19 pandemic
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This research aims to investigate the approaches adopted by Iraqi newspapers in addressing the COVID-19 pandemic crisis. Employing a descriptive methodology and survey technique, the study conducts content analysis on articles published in three prominent newspapers: Al-Sabah, Al-Mada, and Tareeq Al-Shaab. A multi-stage sampling method was employed, encompassing 260 issues of the aforementioned newspapers. Data collection involved the use of a content analysis questionnaire, with the "How it was said?" method utilized to determine analysis categories.
The results showed that Al-Sabah newspaper adopted a positive approach in addressing COVID-19-related topics, while Al-Mada newspaper remained neutral, and Tare

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Publication Date
Thu Feb 16 2023
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Self-Medication Towards Antibiotic Use Among Non- Medical University Staff (Conference Paper )#
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The use of antibiotics without prescription (self-medication) is growing globally and is associated with increased bacterial resistance, ineffective treatment and adverse reactions. This study aimed at assessing the practice of antibiotic self-medication in the Iraqi population. A cross-sectional study design was adopted in this work. The sample was comprised of 303 staff members from the non-medical colleges in Iraq. An online questionnaire was distributed between the 29th of June to the 14th of September 2021 to collect data including socio-demographic characteristics and questions about antibiotic self-medication. Most of the participants had a university degree and a moderate monthly income. The majority (88%) h

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Publication Date
Mon Feb 18 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Control Over The Medical Evacuation Program in light of the Government's Accounting System
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The accounting system of government is considered an important tool to follow up the financial transactions that reflect the activities of governmental units and by which the useful information for estimating governmental annual revenues and expenditures are provided through the state public budget because it is an information system that provides detailed past performance, as well as measures the efficiency of the governmental agencies performance in implementing the budget, and the of success governmental units is measured through the type of services and programs offered, their size and the possibility of achieving the objectives assigned to them. The medical evacuation program is one of the medical and curative health services provid

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Publication Date
Sun Dec 31 2023
Journal Name
Lark
Digital and pedagogical practices of French as a foreign language in a post-COVID-19 context (The case of the University of Baghdad)
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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
Wed Feb 01 2023
Journal Name
Journal Of Engineering
An Empirical Investigation on Snort NIDS versus Supervised Machine Learning Classifiers
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With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

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Publication Date
Mon Mar 21 2022
Journal Name
International Journal For Research In Applied Sciences And Biotechnology
Article Review: Toll-like Receptors and COVID-19
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By March 2020, a pandemic had been emerged Corona Virus Infection in 2019 (COVID-19), which was triggered through the sensitive pulmonary syndrome (SARS disease corona virus- 2 (SARS COV-2). Overall precise path physiology of SARS COV-2 still unknown, as does the involvement of every element of the acute or adaptable immunity systems. Additionally, evidence from additional corona virus groups, including SARS COV as well as the Middle East pulmonary disease, besides that, fresh discoveries might help researchers fully comprehend SARS CoV-2. Toll-like receptors (TLRs) serve a critical part in both detection of viral particles as well as the stimulation of the body's immune response. When TLR systems are activated, pro-inflammatory cy

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Publication Date
Wed Apr 01 2020
Journal Name
Al-rafidain Journal For Sport Sciences
The Effectiveness of UsingflippedclassroombyQuick Response Codes In Learning Someofskills In Artistic Gymnastics for men
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The study aimed to prepare quick response codes to learn some of the technical skills of the second graders in the Faculty of Physical Education and Sports Sciences. The experimental method was used in the design of the experimental and control experimental and control groups. The research sample was represented by second-graders in the College of Physical Education and Sports Sciences / University of Baghdad, and by lot, the second division (a) was chosen to represent the experimental group that applied the inverse method using the QR code, and the second division (g) to represent the control group and applied the traditional method. (10) Students per group. After the tribal tests, his main experiment was carried out for 10 weeks with one

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Publication Date
Mon Apr 04 2022
Journal Name
Journal Of Educational And Psychological Researches
The Degree of Implementation of the Coronavirus Prevention Standards (Covid-19) In the Kingdom Of Saudi Arabia (A COMPARATIVE STUDY BETWEEN FAMILIES OF PEOPLE WITH INTELLECTUAL DISABILITIES AND FAMILIES OF ORDINARY PEOPLE)
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The study aims to identify the degree of implementation of the coronavirus prevention standards (covid-19) in the kingdom of Saudi Arabia and compare it with the families of intellectual disabilities. The study population consisted of all families residing in the Kingdom of Saudi Arabia. To achieve the objectives of the research, the analytical descriptive approach was employed. The study sample consisted of (372) families, among them (84) families with intellectual disabilities, and (288) families without intellectual disabilities. They were chosen from the Saudi community according to what is available for collection in a simple random way, using the standard criteria for the prevention of coronavirus (Covid- 19) Prepared by the resear

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