Background: Quality of life in brain tumor patients is an emerging issue and has prompted neurosurgeons to recon¬sider the need for cognitive assessment in the course of treatment. To date there has been a lack of comprehensive neuropsychological assessment performed preoperatively and in the acute postoperative period in our hospitals.Objectives: to establish the effects of tumors and their surgical treatment, from a neuropsychological perspective, on cognitive functioning in patients with cerebral Gliomas. Methods: This is a prospective study conducted in the Neurosurgical Hospital in Baghdad, Iraq, during the period from January 1999 to January 2001. Any patient admitted during the period of the study with clinical history, signs, sy

Abstract:

      In this research we discussed the parameter estimation and variable selection in Tobit quantile regression model in present of multicollinearity problem. We used elastic net technique as an important technique for dealing with both multicollinearity and variable selection. Depending on the data we proposed Bayesian Tobit hierarchical model with four level prior distributions . We assumed both tuning parameter are random variable and estimated them with the other unknown parameter in the model .Simulation study was used for explain the efficiency of the proposed method and then we compared our approach with (Alhamzwi 2014 & standard QR) .The result illustrated that our approach

Background: Although the new treatment methods developed in recent years are aiming to minimize the need for cooperation of the patients; however, the latter still important factor the treatment. The aim of the study was to evaluate the cooperation level of Class III maloc-clusion patients with orthodontic treatment. Materials and methods: This study followed a cross-sectional style; the targeted population was patients with Class III malocclusion who were treated with three different types of orthopaedic appliances. Four questionnaires were delivered to the patient, patient’s parents, and orthodontists. Statistical analyses of the study were performed with SPSS 20.0 software. Descriptive analyses were presented using fre-quency, percenta

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

Abstract

The current research aims to identify the most prominent beliefs about coronavirus of Baghdad University students, as well as to identify the prominent beliefs toward coronavirus of male and female students. To achieve the research objectives, a questionnaire of (15) items was administered to a sample of (600) male and female students collected from ten different colleges at the university of Baghdad. The findings of the research illustrated that item (8) took priority as students believe there is a misleading about coronavirus spreading by governments, item (2) which indicated that coronavirus is man-made took level two, followed by item (10), it proposes that coronavirus emerged from the American milit

Objectives: To evaluate the families’ attitudes toward environment pollution, and determine the relationship
between families’ attitudes towards environment pollution and their demographic characteristics of age,
education, type of family, and socioeconomic status.
Methodology: A descriptive design is carried throughout the present study to evaluate families’ attitudes toward
environment pollution for the period of October 5th2013 to May 7th2014. A non-probability "purposive" sample of
(110) families’ is selected. The sample is comprised of two groups; (75) urban families’ and (35) rural ones. An
evaluation tool is designed and constructed for the purpose of the study. It is consisted of (4) main parts;
dem

Objective(s): To evaluate of nurses practice toward orthopaedic wound infection and to determine the
relationship between orthopaedic nurses practice and their demographic data characteristic
Methodology: A descriptive study was carried out at orthopaedic wards of Baghdad Teaching Hospital started
from February 1
st, 2011 to August 30th, 2011. A non-probability sample of (39) orthopaedic nurses who were
working in orthopaedic wards were selected from Baghdad Teaching Hospital. The data were collected through
the use of questionnaire , which consists of two parts (1)Demographic data form that consists of a(10) items
and (2) orthopaedic nurses practice form that consists of (4)sections contain (69) items, by mean of di

Background: Neural tube defects (NTDs) are said to be inherited in a multifactorial fashion, i.e. genetic-environmental interaction. Maternal nutritional deficiencies had long been reported to cause NTDs, especially folate deficiency during early pregnancy. More attention had been paid to the exact mechanism by which this deficiency state causes these defects in the developing embryo. The most significant of all researches was that connecting reduced folate and increased homocysteine level in maternal serum on one hand and the risk of developing a NTD baby on the other hand. Objectives : to determine the significance of homocysteine level in Iraqi mothers who gave birth to babies with NTDs as compared to normal controls. Patients, Materials

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a