Cloud Computing Technologies Adoption in Higher Education Inistitutes During COVID-19 Pandemic: Case Study

The global spread of Covid-19 has enhanced the adoption of virtual education and the use of technology in order to reduce the risk of contagion. Recently, the academics have turn out to be more technology-orientated and the educational establishments continue to seek opportunities in order to adopt an environment where all researchers could have access to high-quality scientific resources at anytime and anywhere in the world. Cloud computing is a model, which provides the users with services and application software on-demand, using the Internet. While the use of cloud computing applications is growing, there are ongoing research challenges related to guaranteeing security, privacy and the integration of the cloud applications. To the extent of our knowledge, the present study is the first from this type to thoroughly present the impacts of the COVID-19 pandemic on cloud computing environment in higher education institutions in Iraqi universities. This study will critically analysis the views of the faculty staff and researchers in Iraqi universities on cloud computing. It also tries to take a quick look at how Iraqi university staff responded to the epidemic in terms of research activities. The results shows that about 99% of the respondents considered the cloud applications to have great role in encouraging the research works in their institutes. Concerning the benefits of cloud services, the availability and saving cost were selected as most important factors that encouraging the adoption of cloud services. On the other hand, issues related to performance, lack of user control were selected as major problems in applying cloud services in Iraqi institutions Finally, it can be concluding that reducing the risks of the security, authorization, control and providing better performance of cloud services can enhance the use of cloud applications by researchers at Iraqi universities.

A Comparison Study of Data Mining Algorithms for blood Cancer Prediction

Cancer is a common disease that threats the life of one of every three people. This dangerous disease urgently requires early detection and diagnosis. The recent progress in data mining methods, such as classification, has proven the need for machine learning algorithms to apply to large datasets. This paper mainly aims to utilise data mining techniques to classify cancer data sets into blood cancer and non-blood cancer based on pre-defined information and post-defined information obtained after blood tests and CT scan tests. This research conducted using the WEKA data mining tool with 10-fold cross-validation to evaluate and compare different classification algorithms, extract meaningful information from the dataset and accurately identify the most suitable and predictive model. This paper depicted that the most suitable classifier with the best ability to predict the cancerous dataset is Multilayer perceptron with an accuracy of 99.3967%.

Occurrence of Anaplasmosis (Anaplasma Marginale) in cattle in Sulaimani province, Kurdistan region of Iraq

The goal of this study was to determine both the incidence of anaplasmosis (Anaplasma Marginale) and phylogenetic relationship between A. marginale isolates from cattle in Sulaimani province, Kurdistan Region- Iraq during (March 10th to April 10th 2021) and those from other Anaplasma spp. A total of two isolates were tested for the major surface protein (msp4) gene for this purpose. Eighty blood samples of cattle (51 males and 29 females) were examined using both microscopic examination and PCR tests. Overall results were 23/80 (28.7 5%) and 8/80 (10 %) using microscopic examination and PCR assay, respectively. Age and sex were not significant factors in the appearance of infection, since no statistically significant difference in infection rate has been observed among sex and age group of cattle (P value >0.05). The results also revealed that the accuracies of traditional method and PCR assays in the diagnosis of the disease were 81 %, and 100 respectively. There was moderate correlation (0.43) between both techniques by the Kappa (k) test. However, The PCR technique recorded the highest sensitivity (100%) and specify (100%) for A. marginale detection. In conclusion, by the findings of the present study, it has been confirmed for the first time that A. marginale is the causative agent of anaplasmosis of cattle in the study areas and the best technique for the detection of either acute or chronic cases in cattle was the PCR assay.

Prevalence of Bacterial Vaginosis among Married Women in Kalar District, Iraqi Kurdistan Region

Bacterial vaginosis (BV) is an inflammatory disease, caused by polymicrobial infection, including pathogenic bacteria which replace the vaginal normal flora and finally this replacement causes manifestations of several physiological and clinical symptoms among women within different ages. BV has become one of the main problems that make woman patients visit gynecological and obstetric consultant hospitals in most country. The present study is designed to determine the causative pathogen and the prevalence of bacterial vaginosis among married women patients in Kalar district. This cross-sectional study was performed from the beginning of March to the mid April-2021 among women who attended Obstetrics and Gynecological governmental hospital and out-patient clinics in Kalar City. Intra vaginal swabs have been collected in sterile Amies transport medium sticks and processed for isolation and identification of bacterial species depending on colony morphology, Gram’s stain and microbiological analysis protocols. Then socio-demographic and gynaecologic data were collected by questionnaire. Out of the 108 participant women who suffered from Gynecological diseases, 67(62.03%) of them exhibited bacterial vaginosis. From the 73 different isolated colonies, 18 bacterial species were identified; coagulase-negative staphylococci (CoNS) were the predominant cause of BV (32.84%), followed by E. coli (14.93%), Staphylococcus aureus (13.43%), Klebsiella pneumonia (8.96) and Micrococcus luteus (7.46%), while Proteus spp. and some uncommon bacteria display (1.49%) for each of them. The socio-demographic analysis between positive and negative woman patients revealed that the association between all studied risk factors and BV were statistically significant (P value < 0.05) except the age factor which was statistically non-significant meaning that the age was not associated with BV. In addition, the clinical symptom analysis showed that abnormal vaginal discharge, lower back pain, dysmenorrhea and strawberry were significantly associated with BV (P value < 0.05), while the rest of other factor did not exhibit statistically significant association.