Hanyang Cyber University

About: Hanyang Cyber University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: The Internet & Mental health. The organization has 69 authors who have published 158 publications receiving 1274 citations.

Review of Machine Learning Algorithms for Diagnosing Mental Illness

Abstract: Objective Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. Methods Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naive Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. Results Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naive Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. Conclusion Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Does customer engagement in corporate social responsibility initiatives lead to customer citizenship behaviour? The mediating roles of customer-company identification and affective commitment

Abstract: This study examines how customers' perceptions of corporate social responsibility (CSR) affect their customer citizenship behaviour and the mediated link through customer‐company identification and affective commitment. Working with a sample of 615 banking service customers in South Korea, structural equation modelling is employed to test the research hypotheses. The results of this study suggest that customers' perceptions of CSR are positively related to their customer citizenship behaviour. Second, customer‐company identification was found to mediate the positive relationship between customers' perceptions of CSR and customer citizenship behaviour. Third, customers' affective commitment towards their organization also mediated the positive relationship between customers' perceptions of CSR and customer citizenship behaviour. Finally, the relationship between customers' perceptions of CSR and customer citizenship behaviour is sequentially and partially mediated by customer‐company identification and affective commitment. The theoretical and managerial implications of the results and the limitations of the study are discussed, and future research directions are suggested.

The influence of pretask instructions and pretask planning on focus on form during Korean EFL task-based interaction

Abstract: From an information processing perspective, humans have a limited attentional capacity for processing information. Thus, second language (L2) researchers have investigated the role of pretask plann...

A low-cost visual motion data glove as an input device to interpret human hand gestures

Abstract: Motion data gloves are frequently used input devices that interpret human hand gestures for applications such as virtual reality and human-computer interaction. However, commercial motion data gloves are too expensive for consumer use, and this has limited their popularity. This paper presents an inexpensive motion data glove to overcome this obstacle. To lower costs, we designed our glove to use single-channel video instead of expensive motion-sensing fibers or multi-channel video. Our visual motion data glove is composed of an inexpensive consumer glove with attached thin-bar-type optical indicators and a closed-form reconstruction algorithm that can overcome the common disadvantages of single-channel video approaches, i.e., occlusion and the need for inconvenient iterative reconstruction algorithms. Our low-cost visual motion data gloves are used to interpret human hand gestures, and the resulting performance is evaluated.