Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature extraction step to enhance and preserve the fine details of the breast MRI scans boundaries by using fractional integral entropy FIE algorithm, to reduce the effects of the intensity variations between MRI slices, and finally to separate the right and left breast regions by exploiting the symmetry information. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, all extracted features significantly improves the performance of the LSTM network to precisely discriminate between pathological and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 326 T2W-TSE images and 326 STIR images is 98.77%. The experimental results demonstrate that FIE enhancement method improve the performance of CNN in classifying breast MRI scans. The proposed model appears to be efficient and might represent a useful diagnostic tool in the evaluation of MRI breast scans.
The research aims to identify the academic problems of family counseling diploma students at Saudi Universities. In addition, to identify the differences in these problems according to gender, marital status, place of study, academic specialization, and GPA. The sample consisted of (491) students. The researcher has used one questionnaire for academic problems prepared by the researcher. The research revealed the following results: There were academic problems among family counseling diploma students at Saudi Universities, the most problems were related to the systems and administrations of the university, then the field training, the buildings, classrooms and campus facilities, then the academic courses, after that the exams, then
... Show MoreThe advancement of cement alternatives in the construction materials industry is fundamental to sustainable development. Geopolymer is the optimal substitute for ordinary Portland cement, which produces 80% less CO2 emissions than ordinary Portland cement. Metakaolin was used as one of the raw materials in the geopolymerization process. This research examines the influence of three different percentages of sulfate (0.00038, 1.532, and 16.24) % in sand per molarity of NaOH on the compressive strength of metakaolin-based geopolymer mortar (MK-GPM). Samples were prepared with two different molarities (8M and 12M) and cured at room temperature. The best compressive strength value (56.98MPa) was recorded with 12M w
... Show MorePortland cement concrete is the most commonly used construction material in the world for decades. However, the searches in concrete technology are remaining growing to meet particular properties related to its strength, durability, and sustainability issue. Thus, several types of concrete have been developed to enhance concrete performance. Most of the modern concrete types have to contain supplementary cementitious materials (SCMs) as a partial replacement of cement. These materials are either by-products of waste such as fly ash, slag, rice husk ash, and silica fume or from a geological resource like natural pozzolans and metakaolin (MK). Ideally, the utilization of SCMs will enhance the concrete performance, minimize
... Show MoreIn this paper, we employ the maximum likelihood estimator in addition to the shrinkage estimation procedure to estimate the system reliability (
In this work, a Photonic Crystal Fiber (PCF) sensor based on the Surface Plasmon Resonance (SPR) technology was proposed. A thin layer of gold (Au) was deposited on a D-shaped Photonic Crystal Fiber (PCF), which was coated with plasmonic chemically stable gold material with a thickness of 40nm. The performance parameters like sensitivity including wavelength sensitivity and amplitude sensitivity and resolution were evaluated by simulation using COMSOL software. The proposed sensor was created by using the finite element approach, it is numerically examined. The results show that the surface of D-shaped Photonic Crystal Fiber coated with Au behaves as a sensor to detect the refractive index (IR) of toxic metal ions. The impacts of the str
... Show More"The aim of the research is to identify the availability of the dimensions of the research variables represented by organizational symmetry and the quality of work-life at the University of Information and Communications Technology, which is one of the formations of the Ministry of Higher Education and Scientific Research in Baghdad, in addition to knowing the relationship and influence between them. The research relied on the descriptive analytical approach based on peer description. The research was analyzed and the research sample consisted of (148) individuals, the sample was chosen using the comprehensive inventory method, data was obtained by relying on the questionnaire which was prepared from ready-made m
... Show MoreThe use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree mode
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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
... Show MoreCodes 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
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