Objectives: To determine the effectiveness of the educational program on nursing staff knowledge about infection control measures at the Intensive Care Unit in Al-Diwaniya Teaching Hospital.

Methodology: A pre-experimental design (one group design: pre-test and post-test) was used. This study was conducted in Al-Diwaniya Teaching Hospital for the period from ( 20^th February to 5^th March, 2020) on a non-probability (purposive) sample consisting of (25 nurses) working in ICU. A questionnaire was built as a data collection tool and consisted of two parts:

First part: The demographic characteristics of the nursing staff (age, gender, level of education, years of experien

Objective: To determine the effectiveness of the educational program on nursing staffs' knowledge about uses of steroids and their side effects.
Methodology: A pre-experimental study design (one group design: pre-test and post-test) was used. This study was conducted in Al-Diwaniya Teaching Hospital for the period from ( 28th May to 10 th June, 2020) on a non-probability (purposive) sample consisting of (30 nurses) working in Oncology unit. A questionnaire was built as a data collection tool and consisted of two parts:
First part: The demographic characteristics of the nursing staff (gender, age, level of education, years of experience in hospital, participation in training courses related to nursing care for a patients undergoing

             Hospitals are part of the service organizations and most importantly at the level of individuals because they are tied to the people health and their daily lives , the nursing service is one of the important services provided by hospitals, and nurses are the human resource that offers this service, from this standpoint the idea of  ​​research came to prepare work Scheduling for nurses in a scientific way to improve performance operational for their services and provide efficient service available 24 hours a day, the research use one of the modern and scientific rules of scheduling its “schedule of

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an

Background:  Imaging has a critical role in the diagnosis and evaluation of cardiac diseases, beginning with chest radiography and fluoro-scopy and progressing to coronary angio-graphy, echocardiography, nuclear medicine and recently  multidetector computed tomo-graphy (MDCT) as well as magnetic resonance (MR) imaging

Objective: To highlight the role of Multi-detector CT in the evaluation of coronary artery disease and its importance of being noninvasive diagnostic technique.

Methods: A cross sectional study for 20 patients. Patients were asked to fast 6 hours prior to the examination and the patients with heart rates above 65 beats per minute were given cardio-

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a

The paper aims is to solve the problem of choosing the appropriate project from several service projects for the Iraqi Martyrs Foundation or arrange them according to the preference within the targeted criteria. this is done by using Multi-Criteria Decision Method (MCDM), which is the method of Multi-Objective Optimization by Ratios Analysis (MOORA) to measure the composite score of performance that each alternative gets and the maximum benefit accruing to the beneficiary and according to the criteria and weights that are calculated by the Analytic Hierarchy Process (AHP). The most important findings of the research and relying on expert opinion are to choose the second project as the best alternative and make an arrangement acco