Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither no

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

The Present Work includes the study of the population dynamics of Armadillidium vulgare in AL- Jadiriya region in Baghdad. Monthly samples were collected using a quadrat 0.0625 m2 from November 2007 to November 2008.. The population density of A.vulgare, ranged from 880 ind/m2 in May to251 ind/m2 in January respectively. This species showed high aggregation dispersion in the study area. The sex ratio showed that the number of females were more than that of males and significantly differd (P < 0.05) during the reproductive months. Furthermore, it was found that the juveniles of species were present at most time of the year, But the large sized groups have been observed during summer and spring. And showed a positive linear correlations betwe

Diabetes mellitus type 2 (T2DM) formerly called non-insulin dependent diabetes mellitus (NIDDM) or adult-onset diabetes is a common disease. Rheumatoid factor is a well-established test used in the diagnosis and follows the prognosis of rheumatoid arthritis (RA). Rheumatoid factor is sometimes found in serum of patients with other diseases including diabetes mellitus (DM), due to the presence of pro-inflammatory cytokines such as TNF- α which play an important role in chronic inflammatory and autoimmune diseases like rheumatoid arthritis (RA). The aim of the study is to investigate the associations between type 2 diabetes mellitus (T2DM) and rheumatoid arthritis (RA) in scope of rheumatoid factor (RF), hyperglycemia a

Objectives: To determine Smartphone addiction among primary school students and its impacts. The samples of the study were240primary school students in derived from stratified random sampling. The questionnaire was used to collect the data. The data were then an- analyzed using correlation statistics. It also caused a negative impact on demic performance of the primary school students.
Methodology: A cross- sectional study in assessment approach in applied in order to achieve the earlier stated objectives. The study was initiated from October 1st, 2019 to April 30th, 2020. Simple random sampling (probability) sample of (240) Pupils study In primary school at Al-Rusafa first directorate schools in Baghdad City.
Results: The study re