COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the disease. Recent research findings have suggested that radiology images, such as X-rays, contain significant information to detect the presence of COVID-19 virus in early stages. However, to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not possible. Artificial Intelligence (AI) techniques, machine learning in particular, are known to be very helpful in accurately diagnosing many diseases from radiology images. This paper proposes an automatic technique to classify COVID-19 patients from their computerized tomography (CT) scan images. The technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network (AIRRCNN), which uses machine learning techniques for classifying data. We focus on the Advanced Inception based Recurrent Residual Convolution Neural Network, because we do not find it being used in the literature. Also, we conduct principal component analysis, which is used for dimensional deduction. Experimental results of our method have demonstrated an accuracy of about 99%, which is regarded to be very efficient.
The use of modern information and communication technologies (ICTs) in the learning process has many advantages, but, as recent research has shown, their introduction into the teaching process is rather slow and complex. The rapid development of ICT has caused many changes in society and, consequently, in the education process. The integration of ICT into the teaching process transforms traditional teaching into new teaching that is ready to respond to the demands and needs of a contemporary learner to increase the quality of education: better student motivation, use of different sources of knowledge, development of functional abilities of students, and the ultimate goal is to increase learning outcomes. For that reason, this article explores the possibilities and ways of introducing GeoGebra's mathematical software in geometry classes and its impact on teaching and understanding of processed material by students. In this study, we analyze the influence of educational software in mathematics lectures. The main goal of educational software is to improve teaching performances and make mathematics attractive for the teaching and learning process as well. Educational software represents a combination of ICT and electronic learning (e-learning). Taking into account the specifics of the application of these technologies in different scientific disciplines, the aim of this article is to analyze the impact of the teachers' scientific field on the effects of the application of these technologies in selected higher education institutions. The research included teachers from 10 faculties and 3 schools of applied studies, who provided answers to 20 survey questions. A questionnaire study was applied to obtain training and testing data for statistical evaluation. The statistical analysis was based on an adaptive neurofuzzy inference system (ANFIS). The results confirm that the educational software in mathematics lectures is a very important factor for improvement of the teaching process. The effects of this software on motivation, interest, and confidence of the course's participants were observed. The use of ICTs through the use of GeoGebra will be a new challenge for both teachers and students. An assessment of the motivation and achievement of two groups of students was carried out. The control group and experimental group attended
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