This research aims to study the mechanism of application of international specification requirements (ISO 9001: 2015) at the Iraqi Center- Korean Vocational Training return to vocational training department at the Ministry of Labour and Social Affairs for the purpose of preparing and creating the center to get a certificate of conformity with the requirements of the standard (ISO 9001: 2015) that would elevate the level of performance and services provided in the respondent Center after it is identified and the study of the reality of the quality management system by identifying strengths and weaknesses in the system to diagnose the gap and find ways to address that gap, and adopted the researchers the case study method to conduc

Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide

A new simultaneous spectrophotometric-kinetic method was developed to determine phenylephrine (PHEN) and tetracycline (TETR) via H-point standard addition method (HPSAM). The proposed procedures rely on the measurements of the difference in the rate of charge-transfer (CT) reaction between each of PHEN and TETR as electron donors with p-Bromanil (p-Br) as an electron acceptor. Different experimental factors which affect the extent of the complex formation were investigated by monitoring the value of absorbance at 446 nm. Time pair of 50 -100 sec was selected and employed, among different examined pairs since it results in the highest accuracy for HPSAM-plot. Linear calibration graphs in the concentration ranges of 10.0-40.0 and 10.0–50.0

Objectives:
To evaluate mothers’ attitudes toward readiness for discharge care at home for a premature baby in Intensive Care Unit at teaching hospitals in Medical City Complex and to find out the relationship between mothers’ attitudes and their socio-demographic characteristics.
Methodology: A quasi-experimental study design was carried out through the period of 6th January 2020 to 2021 to 11th March 2021, to evaluate mother’s attitude toward discharge care plan for premature babies. The study carried out in Welfare Teaching Hospital, Nursing Home Hospital and Baghdad Teaching Hospital at Medical City Complex in Baghdad City on 30 mother of premature babies in neonatal intensive care units using the nonprobability sampling

The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co

OBJECTIVE: To evaluate the patient satisfaction to hospital services and identify factors that influences this satisfaction.

Background: Medical students often face substantial psychological stress, which can increase the risk of substance use, professional detriment, and insufficient patient care. However, substance use in medical students remains understudied in Iraq. This study highlights the prevalence, patterns, risk factors, and negative effects of substance use among medical students at the University of Baghdad. A cross-sectional study involving 414 medical students at the University of Baghdad was conducted. The questionnaire included sociodemographic variables and the Alcohol, Smoking, and Substance Involvement Screening Test to screen for psychoactive substance use. The lifetime prevalence of substance use was 38.9%. Among substance users, 42.8

Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentati

Background: Medical-surgical nurses are responsible of providing competent care to clients with a wide-array of acute and chronic health problems. This challenging task requires arming nurses with advanced competencies of health literacy to effectively educate their clients. However, evidence about medical-surgical nurse’s health literacy-related knowledge and experience is limited.  Purposes: This study aimed to determine the level of the health literacy-related knowledge and experience among medical-surgical nurses.Design: A descriptive-cross-sectional study was conducted among a total sample of 177 nurses who were practicing in medical-surgical wards in teaching hospitals in Iraq. A convenience sampling method was used to sele

Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r