Abstract To estimate the seroprevalence of HCV infection among HIV-infected haemophiliacs and to demonstrate the most prevalent HCV genotype, 47 HIV-infected haemophilia patients were screened for anti-HCV antibodies. By performing polymerase chain reaction and DNA enzyme immunoassay, HCV-RNA was detected with subsequent genotyping. Seroprevalence of anti-HCV antibodies was 66.0%. Of 31 HCV/HIV co-infected patients, 21 (67.7%) had no history of blood transfusion. We detected 4 HCV genotypes: 1a, 1b, 4 and 4 mixed with 3a, HCV-1b being the most frequent. Contaminated factor VIII (clotting factor) could be responsible for disease acquisition.

Objective(s): To identify the relationship between demographic characteristics of patients with renal
failure and to find out the relationship between some risk factors like (family history, alcohol drinking,
smoking and chronic disease) with renal failure patients.
Methodology: Case control study design was carried out in order to achieve the objectives of the
study by using the assessment technique in Baghdad teaching hospital from March 5
th, 2017 to October
10th
, 2017, The sample was (cases & control) sample, present study include 200 cases, 100 was case
study the patient who entered in Baghdad teaching hospital, while another 100 was control study. The
data was collected by interview questionnaire inc

Background: The early detection of oral cancers gives the greatest chance of curing and the delay in presentation has a significant impact on the associated morbidity and mortality. The lack of general dental practitioners (GDP) knowledge in early cancer detection has been shown to contribute to delays in referral and treatment. The aim of this study was to investigate the oral cancer awareness among a group of GDP by assessing their knowledge of detection and prevention of oral cancer. Materials and methods: A questionnaire based study was designed. The questionnaire was delivered to 200 GDP assessing oral examination method, knowledge of oral cancer risk factors, clinical appearance and the most common site, point of referral and requests

Aims: The present study aims at assessing mothers’ knowledge of breastfeeding in Kirkuk governorate,
besides determining the relationship between mothers’ knowledge and some of their demographic
attributes.
Methodolgy: A descriptive study was used the assessment approach and applied on mothers in Kirkuk
governorate from January 15th 2011 to July 25th
, 2011. Non-probability sampling a convenience sample of
(72) mothers, attending pediatric general hospital in Kirkuk governorate for following up the health status
of their children, was selected for the purpose of the study. A questionnaire was developed for the
purpose of the study. It was comprised of two parts; the first part includes the mothers' demographic

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff