Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

Background: We compared the pattern of cutaneous dermatoses among Iraqi females of all ages between 4 months preceding the coronavirus disease 2019 (COVID-19) pandemic, and the same months 1 year later within the COVID-19 pandemic.

Methods: This was a cross-sectional study, that targeted all female patients attending an outpatient clinic for dermatology and venereology in Al-Kindy teaching hospital, Baghdad between October 2019 to the end of January 2020, and the same 4-month duration 1 year later (October 2020 to the end of January 2021) after the COVID-19 peak period had passed and there was no or partial curfew to exclude seasonal impact.

Results

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

In this paper, a compartmental differential epidemic model of COVID-19 pandemic transmission is constructed and analyzed that accounts for the effects of media coverage. The model can be categorized into eight distinct divisions: susceptible individuals, exposed individuals, quarantine class, infected individuals, isolated class, infectious material in the environment, media coverage, and recovered individuals. The qualitative analysis of the model indicates that the disease-free equilibrium point is asymptotically stable when the basic reproduction number R0 is less than one. Conversely, the endemic equilibrium is globally asymptotically stable when R0 is bigger than one. In addition, a sensitivity analysis is conducted to determine which

ABSTRACT Objective: Cardiovascular diseases are the first ranked cause of death worldwide. Adhering to health promoting lifestyle behaviors will maintain an individual’s cardiovascular health and decrease the risk of cardiovascular diseases. Methods: In this descriptive study, 150 nursing faculty were surveyed via a non-probability (purposive) sampling method to assess their adherence to health promoting lifestyle in order to know the risk of cardiovascular diseases. The Arabic version of Health-Promoting Lifestyle Profile II (HPLP-II) was used to achieve this goal. Results: Seventy-two nursing faculty completed the survey. The results indicated that the study sample had moderate level of health promotion based on Health-Promot

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