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Showing 3 results for Covid-19
Dr. Mehrdad Fadaei Pellehshahi, Dr. Sohrab Kordrostami, Dr. Amir Hosein Refahi Sheikhani, Dr. Marzieh Faridi Masouleh, Dr. Soheil Shokri,
Volume 11, Issue 2 (2-2020)
Abstract
In this study, an alternative method is proposed based on recursive deep learning with limited steps and prepossessing, in which the data is divided into A unit classes in order to change a long short term memory and solve the existing challenges. The goal is to obtain predictive results that are closer to real world in COVID-19 patients. To achieve this goal, four existing challenges including the heterogeneous data, the imbalanced data distribution in predicted classes, the low allocation rate of data to a class and the existence of many features in a process have been resolved. The proposed method is simulated using the real data of COVID-19 patients hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020, which has led to recovery or death. The obtained results are compared against three valid advanced methods, and are showed that the amount of memory resources usage and CPU usage time are slightly increased compared to similar methods and the accuracy is increased by an average of 12%.
Dr Ahmad Latifian,
Volume 13, Issue 1 (6-2022)
Abstract
This study evaluates the effectiveness of factors affecting the development of virtual education in the era of COVID-19 based on SCORM model from the viewpoint of students at Ferdowsi University of Mashhad. SCORM includes six dimensions that provide a comprehensive picture of the implementation process of a web-based learning management system. This research is a descriptive survey in terms of practical purpose and in terms of the data collection method. The statistical population of this research includes all postgraduate students in 2019. According to Morgan's table, the sample size is 370 people. The data collection tool is a researcher-made questionnaire, and the validity and reliability of the questionnaire are confirmed. The stratified random sampling method is used to distribute the questionnaires. One-sample t-test and "SPSS" software are used to test the research hypotheses. The results of the research showed that in the era of COVID-19 pandemic, virtual education has been effective and at a higher than average level in all six dimensions of SCORM standard (Accessibility, Consistency, Financial facilities, Durability, ability to run, reusability) and every six hypotheses are confirmed. Based on this, it is concluded that the virtual education programs planned at the Ferdowsi University of Mashhad are on the right path and can be strengthened and developed by receiving periodic feedback. Also, considering the successful performance of virtual education in the pandemic, in the future, this technology can be used as an effective supplement in educational and research activities in the university.
Dr. Mehrdad Fadaei Pellehshahi, Prof. Sohrab Kordrostami, Dr. Amir Hossein Refahi Sheikhani, Dr. Marzieh Faridi Masouleh, Dr Soheil Shokri,
Volume 13, Issue 2 (12-2022)
Abstract
In this paper, a new method is presented using a combination of deep learning method, specifically recursive neural network, and Markov chain. The aim is to obtain more realistic results with lower cost in predicting COVID-19 patients. For this purpose, the BestFirst algorithm is used for the search section, and the Cfssubseteval algorithm is implemented for evaluating the features in the data preprocessing section. The proposed method is simulated using the real data of COVID-19 patients who were hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020. The obtained results were compared with three valid advanced methods. The results showed that the proposed method significantly reduces the amount of memory resource usage and CPU usage time compared to similar methods, and at the same time, the accuracy also increases significantly.
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