درخت حوزه‌های تخصصی

The purpose of this study was to identify and analyze the macro requirements of online education with emphasis on the corona pandemic. The method of conducting research is mixed (qualitative-quantitative). To conduct this research, in addition to the documentary study, the content analysis technique with MAXQDA12 software was used to identify the dimensions and indicators and to determine the share of each factor, the confirmatory factor analysis method of smart pls3 software was used. The statistical population in this study was in the qualitative part of all experts in the field of educational management and governmental and higher education, which was conducted after 17 saturated interviews. In the quantitative section, all faculty members of Tehran universities were selected according to Morgan's table, 353 samples were selected by simple random method. Finally, 7 dimensions and 41 indicators were identified. The structural equation method of smartpls3 software was used to draw the pattern. The results showed that the dimensions of Social and cultural requirements (0.874), Infrastructure requirements (0.872), Educational and research requirements (0.796), Policy and strategic requirements (0.791), Financial and economic requirements (0.779), Legal requirements (0.716) and Managerial and organizational requirements (0.694) are the most important in the macro requirements of online education based on the Corona pandemic, respectively..

Presenting an audit report is the basis for developing competitive strategies that, in the form of operational transparency, are able to guide stakeholders in financial decisions and inform them of operational information and facts. Because auditors have a crucial role to play in reporting these reports, they need to be more balanced, both professionally and on the basis of psychological functions, in finding performance gaps with the functional realities of companies. Therefore, knowing the individual characteristics in this area can help to develop the functions of the audit report. This is because auditors, like any person in charge of a profession, may face disruptions and negative performance characteristics that make it difficult for them to disclose corporate performance facts. Hence this study was to analyze the causes of alienation of conduct in a professional career path for a more comprehensive understanding of the auditor's job is frustration. This research is a mix of methods because through the analysis of grounded theory and with the participation of 17 auditing experts, it seeks to identify the components and statements related to the causes of behavioral alienation in the professional career path of trusted auditors of the Stock Exchange Organization. Then, through the link analysis method and with the participation of trusted auditors of the stock exchange organization, it tries to examine the stimuli and consequences of behavioral alienation in the professional career path of auditors in the form of a systematic representation model. The results obtained from the qualitative part of the existence of three cultural; Social and structural dimensions in the form of 7 categories. The results in the quantitative part also showed that the lack of symbols of professional behavior in the auditors of the stock exchange organization under the component of cultural dimension is the most important functional stimulus in auditors' professional frustrations as causes of professional behavior alienation that can result from this cultural disorder. Cause social conflicts in the professional work of certified auditors of the stock exchange organization.

Falls are a serious concern among the elderly due to being a major cause of harm to their physical and mental health. Despite their potential for harm, they can be prevented with proper care and monitoring. As such, the motivation for this research is to implement an algorithmic solution to the problem of falls that leverages the benefits of Machine Learning to detect falls in the elderly. There are various studies on fall detection that works on one dataset: wearable, environmental, or vision. Such an approach is biased against low fall detection and has a high false alarm rate. According to the literature, using two datasets can result in high accuracy and lower false alarms. The purpose of this study is to contribute to the field of Machine Learning and Fall Detection by investigating the optimal ways to apply common machine and deep learning algorithms trained on multimodal fall data. In addition, it has proposed a multimodal approach by training two separate classifiers using both Machine and Deep Learning and combining them into an overall system using sensor fusion in the form of a majority voting approach. Each trained model outputs an array comprising three percentage numbers, the average of the numbers in the same class from both arrays is then computed, and the highest percentage is the classification result. The working system achieved results were 97% accurate, with the highest being achieved by the Convolutional Neural Network algorithm. These results were higher than other state-of-the-art research conducted in the field.