The process of accurate localization of the basic components of human faces (i.e., eyebrows, eyes, nose, mouth, etc.) from images is an important step in face processing techniques like face tracking, facial expression recognition or face recognition. However, it is a challenging task due to the variations in scale, orientation, pose, facial expressions, partial occlusions and lighting conditions. In the current paper, a scheme includes the method of three-hierarchal stages for facial components extraction is presented; it works regardless of illumination variance. Adaptive linear contrast enhancement methods like gamma correction and contrast stretching are used to simulate the variance in light condition among images. As testing material

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

The rapid spread of the COVID-19 coronavirus in 2019 infected many people, primarily affecting the respiratory system. Both COVID-19 and type 2 diabetes have been associated with numerous risks that have become life-threatening. The study studied the link between galectin levels and some clinical characteristics in Iraqis with type 2 diabetes and COVID-19 against those without diabetes. The study included 120 patients and healthy men. Three groups were formed for this study depending on the initial mutant cell line: 80 samples of individuals with type 2 diabetes, aged 40–60 years, with and without COVID-19, were included in each of the first and second groups. The control group consisted of 40 research participants who were matched for ag

The aim of the present work, was measuring of uranium concentrations in 25 soil samples from five locations of Al-Kut city. The samples taken from different depths ranged from soil surface to 60cm step 15 cm, for this measurement of uranium concentrations .The most widely used technique SSNTDs was chosen to be the measurement technique. Results showed that the higher concentrations were in Hai Al- Kafaat which recorded 1.49 ± 0.054 ppm . The uranium content in soil samples were less than permissible limit of UNSCEAR(11.7ppm).

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf

The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting

In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

Background: Therapeutic communication is the basis of interactive relationships among nursing team and their children: that affords opportunities to establish rapport, understand the client’s experience, formulate individualized or client interventions and optimize health care resources.

Objectives: The main aim of the study is to determine the Effectiveness of Education Program on Nurses’ Knowledge about Communication Skills with Children.

Methodology: A quasi-experimental study was conducted in Children Welfare Teaching Hospitals from 7th, October 2018 to the 20th, May 2019. The program and instruments have been constructed by the researcher for the purpose of the study. A non- prob

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