Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

A study on the treatment and reuse of oily wastewater generated from the process of fuel oil treatment of gas turbine power plant was performed. The feasibility of using hollow fiber ultrafiltration (UF) membrane and reverse osmosis (RO) membrane type polyamide thin-film composite in a pilot plant was investigated. Three different variables: pressure (0.5, 1, 1.5 and 2 bars), oil content (10, 20, 30 and 40 ppm), and temperature (15, 20, 30 and 40 ᵒC) were employed in the UF process while TDS was kept constant at 150 ppm. Four different variables: pressure (5, 6, 7 and 8 bar), oil content (2.5, 5, 7.5 and 10 ppm), total dissolved solids (TDS) (100, 200,300 and 400 ppm), and temperature (15, 20, 30 and 40 ᵒC) were mani

 Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat

Abstract. Geographical and remote sensing, which gave a picture of the change in the area of agricultural land in the study area for selected years (1980, 1990, 2000, 2010, 2020). In particular and Iraq in general, as this led to a reduction in the areas of agricultural lands and in return an increase in arid lands and their desertifica- tion and the loss of their productive value, which as a result leads to the migration of the population, the change of land sex, the failure to achieve food security and dependence on the import of the food basket.

In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

The novel Vierordt’s approach, or simultaneous equation method, was created and validated for the concurrent determination of vincristine sulfate (VCS) and bovine serum albumin (BSA) in pure solutions utilizing UV spectrophotometry. It is simple, precise, economical, rapid, reliable, and accurate. This method depends on measuring absorbance at two wavelengths, 296 nm and 278 nm, which correspond to the λ_max of VCS and BSA in deionized water, respectively. The calibration curves of VCS and BSA are linear at concentration ranges of 10–60 μg/mL and 200–1600 μg/mL, with correlation coefficient values (R²) of 1 and 0.999, respectively. The limits of detection (LOD) and quantification (LO

The present study evaluates the effects of Ginkgo biloba extract as monotherapy on the glycemic status, insulin resistance (IR), body mass index (BMI), and visceral adiposity index (VAI), in addition to the inflammatory markers, oxidative status and leptin level in patients with metabolic syndrome in comparison with metformin.

The study is a randomized, double-blind pilot study conducted during the period May to September, 2020. Fifty patients were recruited in the study and they were allocated into two groups (25 per each group): Ginkgo biloba and Metformin groups, they received (120 mg Ginkgo biloba extract/ capsule) and (500 mg Metformin/ capsule) respectively; orally as a single dose for 90 days. Blood samples were taken at z

Background: Bone defect healing is a multidimensional procedure with an overlapping timeline that involves the regeneration of bone tissue. Due to bone's ability to regenerate, the vast majority of bone abnormalities can be restored intuitively under the right physiological conditions. The goal of this study is to examine the immunohistochemistry of bone sialoprotein in order to determine the effect of local application of bone sialoprotein on the healing of a rat tibia generated bone defect. Materials and Methods: In this experiment, 48 albino male rats weighing 300-400 grams and aged 6-8 months will be employed under controlled temperature, drinking, and food consumption settings. The animals will be subjected to a surgical procedure o

Has been studied both processes Almetzaz and extortion of a substance Alklanda Maysan different amounts of Alcaúlan Guy 70% alcohol solution using the method when the wavelength