Breast cancer is one of the most common malignant diseases among women;
Mammography is at present one of the available method for early detection of
abnormalities which is related to breast cancer. There are different lesions that are
breast cancer characteristic such as masses and calcifications which can be detected
trough this technique. This paper proposes a computer aided diagnostic system for
the extraction of features like masses and calcifications lesions in mammograms for
early detection of breast cancer. The proposed technique is based on a two-step
procedure: (a) unsupervised segmentation method includes two stages performed
using the minimum distance (MD) criterion, (b) feature extraction based on Gray
level Co-occurrence matrices GLCM for the identification of masses and
calcifications lesions. The method suggested for the detection of abnormal lesions
from mammogram image segmentation and analysis was tested over several images
taken from National Center for Early Detection of cancer in Baghdad.
The aim of the current research is to reveal the effect of using brain-based learning theory strategies on the achievement of Art Education students in the subject of Teaching Methods. The experimental design with two equal experimental and control groups was used. The experimental design with two independent and equal groups was used, and the total of the research sample was (60) male and female students, (30) male and female students represented the experimental group, and (30) male and female students represented the control group. The researcher prepared the research tool represented by the cognitive achievement test consisting of (20) questions, and it was characterized by honesty and reliability, and the experiment lasted (6) weeks
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This study was conducted in 2018, at Technical College of Applied Sciences, Sulaimani Polytechnic University, Kurdistan Region-Iraq. The aim of the study was to determinate nutritional compositions and some elemental contents in marketable white button mushroom (Agaricus bisporus) that is collected in local markets of Kurdistan Region-Iraq. Five different samples (A: Penjwen product fresh, B: Sulaimani product fresh, C: American caned, D: Valencia Netherlandcaned and E: Erbil product fresh) were collected to be observed. The elements were analyzed by Atomic Absorption Spectrometry methods, and their chemical compassions were determined, too. The collected data were analyzed by One Way ANOVA. The highest fat, protein, fiber and d
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Abstract
For sparse system identification,recent suggested algorithms are 
-norm Least Mean Square ( -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named -ZA-LMS,
In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.
Public relations are amongst the social sciences that rely on scientific methods in achieving new knowledge or resolving existing problems by means of its scientific researches that are often applied and require a classification in terms of their results’ analysis. It also requires subtle statistical processes whether in constructing their material or in analyzing and interpreting their results.
This research seeks to identify the relation between public relations and statistics, and the significance a researcher or practitioner in the domain of public relations should assign to statistics being one of the important criteria in identifying the accuracy and object
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Face detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method.
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Background: The world health organization estimates that worldwide 2 billion people still have iodine deficiency Objectives: Is to make comparison between the effect of identification of recurrent laryngeal nerve (RLN) and non-identification of the nerve on incidence of recurrent laryngeal nerve injury (RLNI) in different thyroidectomy procedures.
Type of the study: cross –sectional study.
Methods: 132 patients with goiters underwent thyroidectomy .Identification of RLN visually by exposure were done for agroup of them and non-identification of the nerves for the other group. The outcomes of RLNI in the two groupsanalyzed statistically for the effect of
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