Abstract
Objective(s): To evaluate housekeeping services staff work environment and their health status, as well as to determine the impact of the work environment upon their health status.
Methodology: A descriptive design is employed throughout the present study to evaluate housekeeping services staff work environment and their health status, as well as to determine the impact of the work environment upon their health status from November 3rd 2017 to June 30th 2018. A purposive “nonprobability” sample of (101) housekeeping staff is selected for the present study. An instrument is constructed for the purpose of the study and it is consists of (2) parts: (I) Evaluation of work environment, and (II) Evaluation of housekeeping st

Objective: The study aim is to identify factors that may contribute to children’s weight status variations. Methodology: A descriptive cross sectional study is carried out has been conducted at the AL- Samawah city in Primary Health Care Centers for the purpose of the screening children’s weight status of Age One to five Years Old. This study is started from December 16th 2018 to February 14th 2019. A(non propriety) purposive sample comprised of (20) primary health centers (10 main and 10 sub) are selected of 500 children who visit the primary health care center during the period for the purpose of the study; Data was collected through using a questionnaire designed and developed for the purpose of the study . It consists of two main

Background: Oral health is a mirror of general health. During pregnancy, oral health is affected by changes in saliva and oral hygiene measures which may lead to more dental caries.

Objective: Assess oral health status in relation to salivary antimicrobial peptides in pregnant women.

Subjects and Method: This cross sectional study was carried out in different primary health care centers in Rusafa sectors/ Baghdad city. The total sample included was 80 women (their  age range from 21-30). The study group consisted of 40 pregnant women: half of them were in the first trimester and  others were in third trimester, while the control group included 40 non-pregnant married fem

The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying

This research aims to clarify the importance of an accounting information system that uses artificial intelligence to detect earnings manipulation. The research problem stems from the widespread manipulation of earning in economic entities, especially at the local level, exacerbated by the high financial and administrative corruption rates in Iraq due to fraudulent accounting practices. Since earning manipulation involves intentional fraudulent acts, it is necessary to implement preventive measures to detect and deter such practices. The main hypothesis of the research assumes that an accounting information system based on artificial intelligence cannot effectively detect the manipulation of profits in Iraqi economic entities. The researche

Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

Buzurgan oil field suffers from the phenomenon of asphaltene precipitation. The serious negatives of this phenomenon are the decrease in production caused by clogging of the pores and decrease in permeability and wettability of the reservoir rocks, in addition to the blockages that occur in the pipeline transporting crude oil. The presence of laboratories in the Iraqi oil companies helped to conduct the necessary experiments, such as gas chromatography (GC) test to identify the components of crude oil and the percentages of each component, These laboratory results consider the main elements in deriving a new equation called modified colloidal instability index (MCII) equation based on a well-known global equation called colloidal in