ZM Al-Bahrani, Medico Legal Update, 2021

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

In this research we assumed that the number of emissions by time (𝑡) of radiation particles is distributed poisson distribution with parameter (𝑡), where  < 0 is the intensity of radiation. We conclude that the time of the first emission is distributed exponentially with parameter 𝜃, while the time of the k-th emission (𝑘 = 2,3,4, … . . ) is gamma distributed with parameters (𝑘, 𝜃), we used a real data to show that the Bayes estimator 𝜃 ∗ for 𝜃 is more efficient than 𝜃̂, the maximum likelihood estimator for 𝜃 by using the derived variances of both estimators as a statistical indicator for efficiency

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

     Ground water is a vital source for agricultural sector and rural communities. The global climate change is expected to change the hydrometeorological processes parameters. The climate considered as part of the southern Iraqi desert general climate with long, extremely hot, and dry summer and short wet period with little rain.  So it is vital to investigate the groundwater quality for irrigation purposes. The meteorological data of Samawa meteorological station for the period 1980-2015 was used to evaluate the climatic conditions for Muthana Governorate. It was found that the averages of annual rainfall was 105.7mm and the everages of evaporation is 3182 mm, while the mean monthly relative humidity % , mean t

      Today, the world is living in a time of epidemic diseases that spread unnaturally and infect and kill millions of people worldwide. The COVID-19 virus, which is one of the most well-known epidemic diseases currently spreading, has killed more than six million people as of May 2022. The World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) after an outbreak of SARS-CoV-2 infection. COVID-19 is a severe and potentially fatal respiratory disease caused by the SARS-CoV-2 virus, which was first noticed at the end of 2019 in Wuhan city. Artificial intelligence plays a meaningful role in analyzing medical images and giving accurate results that serve healthcare workers, especially X-ray images, which are co

Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons.

The study area is part of the city of Samawa in Al Muthanna Governorate in southern Iraq. The study  area is located to the west of Samawa city bounded by the north latitudes 31⁰11^'-31^o42^' and east longitudes 44^o58^'- 45⁰16^'and its groundwater resources are developed for supply and irrigation purposes. In order to evaluate the quality of groundwater in the study area, twenty three groundwater samples were collected and analyzed for physical and chemical parameters. Hydrochemical analysis showed that the groundwater of the study area is excessively mineralized  and very hard. The increase in flow length of groundwater in the study area caused a change in water

Corona Virus Disease-2019 (COVID-19) is a novel virus belongs to the corona virus's family. It spreads very quickly and causes many deaths around the world. The early diagnosis of the disease can help in providing the proper therapy and saving the humans' life. However, it founded that the diagnosis of chest radiography can give an indicator of coronavirus. Thus, a Corner-based Weber Local Descriptor (CWLD) for COVID-19 diagnostics based on chest X-Ray image analysis is presented in this article. The histogram of Weber differential excitation and gradient orientation of the local regions surrounding points of interest are proposed to represent the patterns of the chest X-Ray image. Support Vector Machine (SVM) and Deep Belief Network (DBN)

The glycated haemoglobin A1c(HbA1c) and Fasting blood glucose(FBG) effect on type1 diabetic pateints as a screening tests and as a gold standard for assessing glycemic control in subjects with diabetes were studied . Ninety one blood samples were collected in a peroid between June and the end of November 2012 at AL- Kindy Diabetic Center and Central Child Hospital,48 Females and 43 Males , aging between (11 month- 18 year), are divided into three groups, newly diagnosed , ongoing and healthy control group, with duration of disease between(1 day-3months) and (from birth-8 years) for newly diag