COVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in order to select the best features that affect the prediction of the proposed model. These are the Recursive Feature Elimination (RFE) as wrapper feature selection and the Extra Tree Classifier (ETC) as embedded feature selection. Two classification methods are applied for classifying the features vectors which include the Naïve Bayesian method and Restricted Boltzmann Machine (RBM) method. The results were 56.181%, 97.906% respectively when classifying all features and 66.329%, 99.924% respectively when classifying the best ten features using features selection techniques.
Abstract The results of isolation, morphological and microscopic diagnosis, Chromic Agar, Vitik technology and Bact Alert showed that the diagnosis of fungi isolated from blood samples of end-stage renal patients who did not undergo dialysis and those who underwent dialysis was 60 samples for each type. The total number of fungal isolates isolated from people who did not undergo dialysis was 26 pathogenic fungal isolates, with a percentage frequency of 43.33%. In this study, 4 genera of pathogenic fungi were identified: Candida spp, Rhodotorula spp, Cryptococcus spp. and Aspergillus spp. The number of Candida isolates reached 13 isolates, with a frequency of 50%. The results also showed that the diagnosed species from the genus Rhodotorula
... Show MoreThe purpose of the current study was to explore the standards that teachers take into consideration when selecting and using assistive technology (AT), in addition to their knowledge and skills in this area. A quantitative, descriptive survey design was used and a convenience sample of 79 teachers of students with intellectual disabilities and autism spectrum disorder (ASD) participated in the current study. Based on the four main areas of the SETT Framework—student, environment, tasks, and tools—, teachers reported a lack consideration for most of the standards in each area. Among other findings, statistically significant differences were found between teachers’ standards of the SETT Framework, with teachers who had previous profe
... Show MoreGiven the importance of possessing the digital competence (DC) required by the technological age, whether for teachers or students and even communities and governments, educational institutions in most countries have sought to benefit from modern technologies brought about by the technological revolution in developing learning and teaching and using modern technologies in providing educational services to learners. Since university students will have the doors to work opened in all fields, the research aims to know their level of DC in artificial intelligence (AI) applications and systems utilizing machine learning (ML) techniques. The descriptive approach was used, as the research community consisted of students from the University
... Show MoreInformation about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites
... Show MoreBackground: techniques of image analysis have been used extensively to minimize interobserver variation of immunohistochemical scoring, yet; image acquisition procedures are often demanding, expensive and laborious. This study aims to assess the validity of image analysis to predict human observer’s score with a simplified image acquisition technique. Materials and methods: formalin fixed- paraffin embedded tissue sections for ameloblastomas and basal cell carcinomas were immunohistochemically stained with monoclonal antibodies to MMP-2 and MMP-9. The extent of antibody positivity was quantified using Imagej® based application on low power photomicrographs obtained with a conventional camera. Results of the software were employed
... Show MoreSoil compaction is one of the most harmful elements affecting soil structure, limiting plant growth and agricultural productivity. It is crucial to assess the degree of soil penetration resistance to discover solutions to the harmful consequences of compaction. In order to obtain the appropriate value, using soil cone penetration requires time and labor-intensive measurements. Currently, satellite technologies, electronic measurement control systems, and computer software help to measure soil penetration resistance quickly and easily within the precision agriculture applications approach. The quantitative relationships between soil properties and the factors affecting their diversity contribute to digital soil mapping. Digital soil maps use
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