Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

This study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

Wind turbine (WT) is now a major renewable energy resource used in the modern world. One of the most significant technologies that use the wind speed (WS) to generate electric power is the horizontal-axis wind turbine. In order to enhance the output power over the rated WS, the blade pitch angle (BPA) is controlled and adjusted in WT. This paper proposes and compares three different controllers of BPA for a 500-kw WT. A PID controller (PIDC), a fuzzy logic controller (FLC) based on Mamdani and Sugeno fuzzy inference systems (FIS), and a hybrid fuzzy-PID controller (HFPIDC) have been applied and compared. Furthermore, Genetic Algorithm (GA) and Particle swarm optimization (PSO) have been applied and compared in order to identify the

     In this research, several estimators concerning the estimation are introduced. These estimators are closely related to the hazard function by using one of the nonparametric methods namely the kernel function for censored data type with varying bandwidth and kernel boundary. Two types of bandwidth are used:  local bandwidth and global bandwidth. Moreover, four types of boundary kernel are used namely: Rectangle, Epanechnikov, Biquadratic and Triquadratic and the proposed function was employed with all kernel functions. Two different simulation techniques are also used for two experiments to compare these estimators. In most of the cases, the results have proved that the local bandwidth is the best for all the

This study included 50 blood serum samples that collected from children with age ranged between 7-12 years. Thirty five samples collected from children with Type 1 Diabetes Mellitus (T1D), and 15 blood serum samples collected from healthy children as a control sample. The polymorphism of IL-4 -590 (C>T) gene, which amplified by using amplification refractory mutation system (ARMS-PCR) was showed high percentage of C allele frequency in T1D patients sample in comparison with T allele frequency, and the C allele revealed as etiological faction with risk by having T1D disease, whereas the T allele showed high frequency from the C allele frequency in control sample, and the T allele revealed as preventive faction from infection by this disease.

The need for detection and investigation of the causes of pollution of the marshes and submit a statistical study evaluated accurately and submitted to the competent authorities and to achieve this goal was used to analyze the factorial analysis and then obtained the results from this analysis from a sample selected from marsh water pollutants which they were: (Electrical Conductivity: EC, Power of Hydrogen: PH, Temperature: T, Turbidity: TU, Total Dissolved Solids: TDS, Dissolved Oxygen: DO). The size of sample (44) sites has been withdrawn and examined in the laboratories of the Iraqi Ministry of Environment. By illustrating SPSS program) the results had been obtained. The most important recommendation was to increase the pumping of addit

Background: Hypertension is probably the most important public health problem around the world. People with periodontal disease may be at greater risk of hypertension. The inflammatory effects of periodontal disease help to promote endothelial dysfunction in arteries which may lead to changes in blood pressure. Salivary MMP-8 has been associated with both periodontal disease and prevalent hypertension. Aim of study: This study was conducted to measure salivary matrix metalloproteinase - 8, in relation to periodontal health condition among a group of patients with hypertension in comparison with control group. Materials and methods: Ninety subjects, aged 45-50 years old were included in this study, seeking treatment for chest pain in Ibn-A

Background: The immunogenetic predisposition
may be considered as an important factor for the
development of Type 1 Diabetes Mellitus (T1DM)
in association with the HLA antigens.
Objective:This study was designed to investigate
the role of HLA-class II antigens in the etiology of
type T1DM and in prediction of this disease in
siblings, and its effect on expression of glutamic
acid decarboxylase autoantibodies (GADA).
methods:Sixty children who were newly diagnosed
type 1 diabetes (diagnosed less than five months)
were selected. Their age ranged from 3-17 years.
Another 50 healthy siblings were available for this
study, their ages range from 3-16 years. Eighty
apparently healthy control subjects,