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Protective role of Ginseng in endomertriosis during covid-19
JiHyeon Song,YoungJoo Lee The Korean Society of Ginseng 2023 Journal of Ginseng Research Vol.47 No.2
The coronavirus disease 2019 (COVID) pandemic began in December 2019. Many countries have implemented restrictions such as mandatory mask wearing and social distancing. These measures have caused diverse and complex health problems, particularly in women's health, anxiety, and depression. This review examines an alternative approach to the treatment of endometriosis during the COVID pandemic. The efficacy of ginseng with anti-inflammatory activity and ability to relieve or prevent symptoms of endometriosis is discussed and reviewed.
Perceptions of Corporate Social Responsibility and Implications for the Nonprofit Sector
Jihyeon Song,Seongho An,Jiwon Suh World Association for Triple Helix and Future Stra 2023 Journal of Contemporary Eastern Asia Vol.22 No.1
While corporate social responsibility (CSR) has been considered an important philanthropic support for nonprofits worldwide, little is known about how perceptions of CSR are associated with actual CSR practices that may benefit nonprofit organizations in different institutional contexts. This study applies stakeholder theory to examine how South Korean firms perceive CSR outcomes, and how these perceptions lead to different CSR practices. We constructed a panel dataset using two waves of the Giving Korea survey of CSR and two additional sources. The results indicate that perceived CSR outcomes may play a critical role in CSR practices: 1) the more financial performance is perceived as an outcome, the more will be donated; 2) the more organizational culture is perceived as an outcome, the greater the engagement in employee volunteering; and 3) the more reputation is perceived as an outcome, the more nonprofit organizations are supported. From the findings, we discuss theoretical implications and provide suggestions for nonprofit organizations.
Evaluations of AI-based malicious PowerShell detection with feature optimizations
Song, Jihyeon,Kim, Jungtae,Choi, Sunoh,Kim, Jonghyun,Kim, Ikkyun Electronics and Telecommunications Research Instit 2021 ETRI Journal Vol.43 No.3
Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.