• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.13-22
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• 2021

Recently network based attack technologies are rapidly advanced and intelligent, the limitations of existing signature-based intrusion detection systems are becoming clear. The reason is that signature-based detection methods lack generalization capabilities for new attacks such as APT attacks. To solve these problems, research on machine learning-based intrusion detection systems is being actively conducted. However, in the actual network environment, attack samples are collected very little compared to normal samples, resulting in class imbalance problems. When a supervised learning-based anomaly detection model is trained with such data, the result is biased to the normal sample. In this paper, we propose to overcome this imbalance problem through One-Class Anomaly Detection using an auto encoder. The experiment was conducted through the NSL-KDD data set and compares the performance with the supervised learning models for the performance evaluation of the proposed method.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.79-86
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• 2021

As the usefulness of big data analysis has been drawing attention, many studies in the business research area begin to use big data to predict firm performance. Previous studies mainly rely on data outside of the firm through news articles and social media platforms. The voices within the firm in the form of employee satisfaction or evaluation of the strength and weakness of the firm can potentially affect firm value. However, there is insufficient evidence that online employee reviews are valid to predict firm value because the data is relatively difficult to obtain. To fill this gap, from 2014 to 2019, we employed 97,216 reviews collected by JobPlanet, an online firm review website in Korea, and developed a machine learning-based predictive model. Among the proposed models, the LSTM-based model showed the highest accuracy at 73.2%, and the MAE showed the lowest error at 0.359. We expect that this study can be a useful case in the field of firm value prediction on domestic companies.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.9
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• pp.797-808
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• 2019

The purpose of this study is to collect the psychological responses that occur while watching TV home shopping ads in verbal form, and explore the possibility of measuring the effectiveness of TV home shopping ads using linguistic analysis. The psychological responses during watching positive and negative ads of participants(40 housewives and female college students) were collected in a linguistic form using a think aloud and self-report measurement. It was analyzed by KLIWC, a Korean language analysis program. As a result of the analysis, there was a difference in psychosocial variables as well as linguistic variables between positive and negative evaluation ads. Also, various variables of KLIWC were correlated with the variables of advertising effectiveness (purchase stimulus, ad attitude, product attitude, purchase intention) and advertising response variables. This suggests the possibility of constructing a psychological response profile and measurement of advertising effectiveness using language analysis.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.7-18
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• 2018

Recently, serverless computing is spotlighted. Because it supports the development of application based on micro-service. Micro-service means a small-scale service that can operate independently. Applications with micro-service units have the advantage of enabling individual updates, easy and fast deployment. In addition, it has the advantage of supporting various languages and platforms for each service. Therefore many enterprise are trying to change from monolithic architecture to micro-service based architecture. However, there is a lack of research on methods and baseline for micro-service construction. In this paper, a method is proposed to construct the micro-service unit by analyzing UML design in monolithic application. It also shows the proposed approach can reconstruct monolithic application into micro-service based unit by implementing the constructed micro-services in a real serverless platform environment. In addition, the results of the comparative evaluation with the related studies are presented.

• Nutrition Research and Practice
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• v.16 no.1
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• pp.132-146
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• 2022

BACKGROUND/OBJECTIVES: Various sodium reduction policies have been implemented. However, there are limitations in the aspect of actual field applicability and efficiency. For effective sodium reduction, cooperation with the field is required and consumer preference must be considered. Thus, this study aimed to develop a low-sodium burger considering field applicability and consumer preference. MATERIALS/METHODS: Focus group interviews and in-depth interviews on the sodium reduction measures were conducted with nine professionals in related fields to discuss practical methods for sodium reduction from September 7 to 21, 2018. By reflecting the interview results, a burger using a low-sodium sauce was developed, and preference analysis for sodium in the burger sauces and finished products was performed. The consumer preference for low-sodium burgers was evaluated on 51 college students on November 12, 2018. RESULTS: The results of the professional interview showed that it is desirable to practice sodium reduction gradually, and by reflecting this, the burger sauce was prepared by adjusting the ratio of refined salt to 15%, 30%, and 50%. The sodium content of the burger using low-sodium sauce was 399 mg/100 g in the control group, 362 mg/100 g in the H1 group, and 351.5 mg/100 g in the H2 group, showing a 9.3-11.9% decrease in sodium in the H1 and H2 groups. The preference evaluation on the low-sodium burgers showed a higher preference for burgers with 9.3-11.9% sodium reduction, which did not affect the overall taste. CONCLUSIONS: This study examined the potential for sodium reduction in the franchise foodservice industry. An approximate 10% sodium reduction resulted in an increase in consumer preference without affecting the strength of the taste. Thus, if applied gradually, sodium reduction at practical levels could increase the consumer preference without changing the taste or quality and could be applied in the franchise foodservice industry.

• Human Ecology Research
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• v.61 no.3
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• pp.389-399
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• 2023

Sustainable fashion products have different characteristics from typical fashion products. Therefore, this study focuses on shoes while exploring the expansion and development of sustainable fashion consumption as well as consumers' perceptions of the sustainability approaches practiced by shoe companies. In-depth interviews were conducted with 24 consumers, who had purchased sustainable shoes, in order to understand their purchase decision-making process and consumption characteristics, using the seven stages of the EBM model. In the "need recognition" stage, the survey participants' social background and family influences were categorized as macro factors, while their personal background influences were categorized as micro factors. In the "evaluation of alternatives" stage, participants reconfirmed whether or not to make a purchase based on the product's properties, such as price, brand value, and offered services. In the "purchase" stage, participants' purchase channels were determined according to their preferences as well as the selection pattern they followed until the final purchase within the chosen channel. In the "consumption" stage, the start of product ownership coincides with the start of using the products after making a purchase. In the "post-purchase assessment" stage, higher positive experiences led to a higher repurchase intention of sustainable shoes, while negative experiences caused participants to defer consumption and made them experience a sense of guilt for failing to consume sustainably. During the "post-purchase behavior" stage, which focused on the categories that the customers prioritized, many participants spread information about sustainable fashion to specific individuals through active online WOM behavior.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.85-92
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• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.787-796
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• 2021

Nitrogen (N) is the most important element during the process of plant growth, and the quality of crops varies depending on the amount of nitrogen present. Most of the nitrogen is used for plant growth, but approximately 10 - 20% of Nitrogen is carried away by the wind in the form of NH₃. This volatilized NH₃ reacts with various oxides in the atmosphere to generate secondary particulate matter. To address this, the present study attempts to reduce NH₃ occurring in the soil using biochar at a specific pH. Biochar was used as a treatment with 1% (w·w^-1) of the soil, and urea was applied at different levels of 160, 320, and 640 kg·N·ha^-1. NH₃ generated in the soil was collected using a dynamic column and analyzed using the indophenol blue method. NH₃ showed the maximum emission within 4 - 7 days after the fertilizer treatment, decreasing sharply afterward. NH₃ emission levels were reduced with the biochar treatment in all cases. Among them, the best reduction efficiency was found to be approximately 25% for the 320 kg·ha^-1 + pH 6.7 biochar treatment. Consequently, in order to reduce the amount of NH₃ generated in the soil, it is most effective to use pH 6.7 biochar and a standard amount (320 kg·N·ha^-1) of urea.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.777-784
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• 2017

In the architectural space, the user's behavior is the most important factor in evaluating the quality of architecture. Normally, the evaluation of user behavioral performance was carried out after a building was completed. Recently, interest in and efforts at pre-simulation based on information technology are accelerating. However, since existing user simulation technology is concerned mainly with simply escaping from a large space, it is impossible to simulate the behavior of multiple users in an architectural space. The present study strives to develop a human-figured intelligent agent for advanced user simulation based on ontology. The main purpose of the study is to employ the intelligent behaviors of a NPC(Non-player Character) to infer the ontology of both spatial and user information. In this paper, we intend to integrate ontology inference technology into the virtual space. And also, this study suggest the ontology visualization technology which illustrate the ontology-based information and their change in the spatial information.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.12
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• pp.819-829
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• 2017

In this paper, we describe the current state of speech recognition technology and identify the basic speech recognition technology and algorithms first, and then explain the code flow of API necessary for speech recognition technology. We use the application programming interface (API) of Google, Naver, and Daum KaKao, which have the most famous search engine among the speech recognition APIs, to create a voice recognition app in the Android studio tool. Then, we perform a speech recognition experiment on people's standard words and dialects according to gender, age, and region, and then organize the recognition rates into a table. Experiments were conducted on the Gyeongsang-do, Chungcheong-do, and Jeolla-do provinces where the degree of tongues was severe. And Comparative experiments were also conducted on standardized dialects. Based on the resultant sentences, the accuracy of the sentence is checked based on spacing of words, final consonant, postposition, and words and the number of each error is represented by a number. As a result, we aim to introduce the advantages of each API according to the speech recognition rate, and to establish a basic framework for the most efficient use.