• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.681-689
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• 2023

Objective : Subarachnoid hemorrhage (SAH) is a condition characterized by bleeding in the subarachnoid space, often resulting from the rupture of a cerebral aneurysm. Delayed cerebral ischemia caused by vasospasm is a significant cause of mortality and morbidity in SAH patients, and inflammatory markers such as systemic inflammatory response index (SIRI), systemic inflammatory index (SII), neutrophil-to-lymphocyte ratio (NLR), and derived NLR (dNLR) have shown potential in predicting clinical vasospasm and outcomes in SAH patients. This article aims to investigate the relationship between inflammatory markers and cerebral vasospasm after aneurysmatic SAH (aSAH) and evaluate the predictive value of various indices, including SIRI, SII, NLR, and dNLR, in predicting clinical vasospasm. Methods : A retrospective analysis was performed on a cohort of 96 patients who met the inclusion criteria out of a total of 139 patients admitted Trakya University Hospital with a confirmed diagnosis of aSAH between January 2013 and December 2021. Diagnostic procedures, neurological examinations, and laboratory tests were performed to assess the patients' condition. The Student's t-test compared age variables, while the chi-square test compared categorical variables between the non-vasospasm (NVS) and vasospasm (VS) groups. Receiver operating characteristic (ROC) curve analyses were used to evaluate the diagnostic accuracy of laboratory parameters, calculating the area under the ROC curve, cut-off values, sensitivity, and specificity. A significance level of p<0.05 was considered statistically significant. Results : The study included 96 patients divided into two groups : NVS and VS. Various laboratory parameters, such as NLR, SII, and dNLR, were measured daily for 15 days, and statistically significant differences were found in NLR on 7 days, with specific cut-off values identified for each day. SII showed a significant difference on day 9, while dNLR had significant differences on days 2, 4, and 9. Graphs depicting the values of these markers for each day are provided. Conclusion : Neuroinflammatory biomarkers, when used alongside radiology and scoring scales, can aid in predicting prognosis, determining severity and treatment decisions for aSAH, and further studies with larger patient groups are needed to gain more insights.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.861-866
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• 2023

Individuals with visual impairments face difficulties in accessing accurate information about medical services and medications, making it challenging for them to ensure proper medication intake. While there are healthcare laws addressing this issue, there is a lack of standardized solutions, and not all over-the-counter medications are covered. Therefore, we have undertaken the design of a mobile application that utilizes image recognition technology, barcode scanning, and QR code recognition to provide guidance on how to take over-the-counter medications, filling the existing gaps in the knowledge of visually impaired individuals. Currently available applications for individuals with visual impairments allow them to access information about medications. However, they still require the user to remember which specific medication they are taking, posing a significant challenge. In this research, we are optimizing the camera capture environment, user interface (UI), and user experience (UX) screens for image recognition, ensuring greater accessibility and convenience for visually impaired individuals. By implementing the findings from our research into the application, we aim to assist visually impaired individuals in acquiring the correct methods for taking over-the-counter medications.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.395-413
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• 2023

This research studied the effect of customer experience characteristics in offline home appliance stores on perceived value and revisit intention. Among the offline distribution of home appliances with more than 100 stores nationwide, two home appliance retailers (HiMart, E-Land), three hypermarkets (E-Mart, Homeplus, Lotte Hi-Mart), and two home appliance stores (LG Best Shop, Samsung Digital Plaza) were selected, and a survey was conducted on men and women in their 20s or older in Seoul, Gyeonggi, and Incheon who had visited and purchased the home appliance store within the last 6 months. As a result of the survey, a statistical analysis was conducted on a total of 330 samples using the PLS (Partial Least Squares) structural equation model and SPSS statistical package. Through this study, the following research results can be obtained. First, educational experience, deviant experience, and aesthetic experience had a positive (+) effect on the functional value. However, entertainment experience did not affect functional value. Second, educational experience, deviant experience, and aesthetic experience all had a positive (+) effect on emotional value. Third, both functional and sensory values had a positive (+) effect on the revisit intention. Fourth, it was confirmed that brand loyalty had no moderating effect between functional value and sensory value revisit intention. The results of this study show the structural relationship between customer experience characteristics, perceived value (functional value, sensory value), and revisit intention. This result provides guidelines on what activities home appliance offline stores should do at a time when online channels threaten the survival of offline channels.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.115-125
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• 2022

As the digital economy has emerged as a means of building a new business order and creating new values, the number of small business owners participating in digital platforms is gradually increasing. This study aims to check whether small business owners participating in the digital platform are being helped to properly respond to the market environment and establish and implement strategies necessary for growth through learning within the platform. To this end, this study attempted to examine the effect of the absorptive capacity of small business owners using e-commerce platforms on market orientation and the moderating effect of vicarious learning and experiential learning, which are two types of learning within the platform. As a result of verifying the hypothesis through the survey, it was found that the absorption capacity of small business owners using digital platforms positively affected their market orientation. In addition, as a result of the moderating effect analysis, it was found that vicarious learning within the platform strengthens the relationship between absorptive capacity and market orientation. This result implies that small business owners can not only prepare for market uncertainties through indirect learning (vicarious learning) but also establish strategies to provide products and services that meet the market's needs. On the other hand, the effect of experiential learning was found to lower market orientation, which means that previous business experiences can rather lower attention to the environment. The significance and implications of the study were presented.

• Information Systems Review
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• v.23 no.3
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• pp.159-176
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• 2021

Obviating user churn is a prominent strategy to capitalize on online games, eluding the initial investments required for the development of another. Extant literature has examined factors that may induce user churn, mainly from perspectives of motives to play and game as a virtual society. However, such works largely dismiss the service aspects of online games. Dissatisfaction of user needs constitutes a crucial aspect for user churn, especially with online services where users expect a continuous improvement in service quality via software updates. Hence, we examine the relationship between a game's quality management and its user base. With text mining and survival analysis, we identify complaint factors that act as key predictors of user churn. Additionally, we find that enjoyment-related factors are greater threats to user base than usability-related ones. Furthermore, subsequent quasi-experiment shows that improvements in the complaint factors (i.e., via game patches) curb churn and foster user retention. Our results shed light on the responsive role of developers in retaining the user base of online games. Moreover, we provide practical insights for game operators, i.e., to identify and prioritize more perilous complaint factors in planning successive game patches.

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

• Information Systems Review
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• v.24 no.2
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• pp.67-88
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• 2022

This study proposed a research model that can explain the usage intentions of users and non-users by considering the performance aspects of personal mobility and external environmental factors based on the SOR (Stimulus-Organism-Response) model, A survey was conducted targeting domestic users and non-users, and research models and hypotheses were verified through Partial Least Square (PLS) and Artificial Neural Network (ANN). As a result of the analysis, it was confirmed that the users' perceived satisfaction and perceived trust had a positive effect on their intention to use, and that perceived risk and environmental value had a significant relationship with perceived satisfaction and perceived trust. For non-users, it was found that there was a positive correlation between perceived satisfaction and intention to use, and it was verified that perceived risk and environmental value, like users, were significant antecedents of perceived satisfaction and perceived trust. Among the remaining variables, the perceived mobility of users and the perceived ease of use of non-users were respectively presented as important influencing factors on perceived satisfaction.

• Journal of Life Science
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• v.34 no.6
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• pp.434-441
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• 2024

Mammary gland tumors are the most common tumors detected in non-spayed female dogs and pose a significant clinical challenge. Due to the strong similarity between canine mammary tumors (CMT) and human breast cancer (HBC), biomarkers identified in HBC can also be detected in CMT. These biomarkers have been shown to offer valuable insights into early diagnosis, prognosis, and treatment strategies. The purpose of this article is to provide a concise overview of CMT biomarkers based on the current literature. Traditional treatments for CMT in dogs typically begin with surgery, followed by chemotherapy, radiotherapy, or hormonal therapy. However, these treatments alone are not always fully effective. A diagnostic biomarker can detect the presence of a disease or the characteristics of a disease and classify an individual's status. Prognostic biomarkers focus on predicting the expected progression, recurrence, or survival of the disease in patients. By utilizing advances in understanding the mechanism of canine-specific mammary gland tumors, the estimation of biomarkers offers hope for improved outcomes in cancer patients. Novel technologies, such as single-cell RNA sequencing analysis, could provide a valuable resource for deciphering intra- and inter-tumoral heterogeneity. This review paper explores current research on CMT biomarkers and suggests directions for their development.