COVID-19 (Coronavirus disease-2019), commonly called Coronavirus or CoV, is a dangerous disease caused by the SARS-CoV-2 virus. It is one of the most widespread zoonotic diseases around the world, which started from one of the wet markets in Wuhan city. Its symptoms are similar to those of the common flu, including cough, fever, muscle pain, shortness of breath, and fatigue. This article suggests implementing machine learning techniques (Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine) by Python to classify a series of chest X-ray images that include viral pneumonia, COVID-19, and healthy (Not infected) cases in humans. The study includes more than 1400 images that are collected from the Kaggle platform. The experimental outcomes of this study confirmed that the supported vector machine technique has high accuracy and excellent performance in the classification of the disease, as reflected by values of 91.8% accuracy, 91.7% sensitivity, 95.9% specificity, 91.8% F1-score, and 97.6% AUC.
Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments ach
... Show MoreAutorías: Muwafaq Obayes Khudhair, Sanaa Rabeea Abed, Hayder Talib Jasim. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 1, 2023. Artículo de Revista en Dialnet.
The study entitled (Anthropometric Treatments of the Study Seat Units Used in Elementary stages) highlighted the relations between the sizes of dimensions of the study seats and the different anthropometric sizes of the students. The study problem is manifested in the following question: what are the anthropometric treatments used in the design of the study seats in the elementary stages? The research aims at finding design treatments for the anthropometric variables of the study seats used in the elementary stages, because the study seats have to do with preserving students health and safety through providing an ideal seating mechanism compatible with the anthropometric variables which enhances comfort, safety and focus in the mo
... Show MoreThe effect of substitution of Ni on Cu in (Bi0.8Pb0.2)2(Sr0.9Ba0.1)2 Ca2Cu3-x Nix O10+? for (x=0,0.1….1,2,3) superconductor system and sintering time has been investigated .The samples were prepared by solid-state reaction methods. The results show that the optimum sintering temperature is equal to 850 ºC, and the sintering time is equal to 140 h. The highest transition temperature (Tc) obtained for (Bi0.8Pb0.2)2(Sr0.9Ba0.1)2 Ca2Cu3-x NixO10+? composition was 113 with x=0.8 Phase analyses of the samples by X-ray diffraction (XRD) analysis showed an orthorhombic structure with a high Tc phases (2223) as a dominant phase and low Tc phase (2212) in addition to some impurity phases.
The problem of the paper focused on the role of the learning organization in the crisis management strategy, and the extent of the actual interest in both the learning organization and the crisis management and aimed at diagnosing and analyzing that and surrounding questions. The Statistical Package for the Social Sciences (SPSS) program was used to calculate the results and the correlation coefficient between the two main variables. The methodology was descriptive and analytical. The case study was followed by a questionnaire that was distributed to a sample of 31 teachers. The paper adopted a seven-dimensional model of systemic thinking that encourages questioning, empowerment, provision of advanced technologies, and strategic lea
... Show MoreMutations in genes encoding proteins necessary for detoxifying oxidative stress products have been predicted to increase susceptibility to lung cancer (LC). Despite this, the association between waterpipe tobacco smoking (WP), genetic polymorphisms, and LC risk remains poorly understood. This is the first study to explore the relationship between WP tobacco smoking and these genetic factors. Previously, we investigated the association of GSTP1 SNPs (rs1695-A/G and rs1138272-C/T) with LC in Iraqi males who smoke WP. Here, we expanded our analysis to include GSTM1 (active/null) and GSTT1 (active/null) genotypes, both individually and in combination with GSTP1 SNPs. Multiplex PCR and RFLP-PCR assays were utilized to determine the genotypes of
... Show MoreCo(II) ion was determined by a new, accurate, sensitive and rapid method via a
continuous flow injection analysis (CFIA) with a chemiluminescence reaction based on
the oxidation of Luminol which is loaded on poly acrylic acid gel beads by hydrogen
peroxide in presence of Cobalt (II) ion as a chemiluminescence catalyst. Chemical and
physical parameters were investigated to obtain the best conditions. Linear dynamic
range of Cobalt (II) ion was from 0.1-20.0 μg.ml-1 with a correlation coefficient r =
0.9758, limit of detection (L.O.D) 0.2 ng/sample from the step wise dilution of lowest
concentration in the calibration graph with the percentage relative standard deviation for
3 μg.ml-1 Co(ll) solution is 0.8537% (n
The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s
... Show MoreTraffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
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