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. T

     The aim of this research is to identify the effectiveness of thinking skills in developing the life skills of the students of the first Academic year, particularly the differences in life skills according to the variable of study specialization (human-scientific). To achieve the research objectives, the life skills scale was constructed on the classification of (WHO). The psychometric properties of the scale were examined. The validity of the scale, which is the final form of (60) items, was valid for measuring the life skills. The scale was then applied to the research sample of (112) students of the first year of the University of Bisha.  The SPSS program processed statistical analysis.  Resul

There is an assumption implicit but fundamental theory behind the decline by the time series used in the estimate, namely that the time series has a sleep feature Stationary or the language of Engle Gernger chains are integrated level zero, which indicated by I (0). It is well known, for example, tables of t-statistic is designed primarily to deal with the results of the regression that uses static strings. This assumption has been previously treated as an axiom the mid-seventies, where researchers are conducting studies of applied without taking into account the properties of time series used prior to the assessment, was to accept the results of these tests Bmanueh and delivery capabilities based on the applicability of the theo

The research aims at measuring the extent of the relationship and influence of the indicators of the Core competencies of the audit firms and offices in the Earning Quality of the private banks listed in the Iraq Stock Exchange under audit. The research community represents 38 banks. The sample of the research has been approved only 10 banks continue to issue their financial statements for the period (2007 – 2017), in addition to the audit offices assigned to audit these banks, which amounted to 14 companies and auditing offices. John's (1999) model revised by Kothari et al., (2005) was adopted to measure the Earning Quality by finding discretionary accruals and non-discretionary accruals, to measure the Core competencies indicators ,

Abstract

The current research is attempt to test the reflection of the lean management on the human resources management practices of two of the most important communication companies operating in Iraq (`Zain & Asia cell), The research aims to Determine the extent of adoption of the lean management approach in the two researched companies, as it improving human resource management practices. The research problem represented in the existence of lack of in some aspects of the application the lean management approach in service sector and neglecting the impact of its tools on the human resource management practices. For this purpose three principle research hypotheses has been formulated, first there is a correlation rel

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class