• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.76-82
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• 2022

Recently, natural and social disasters in Korea are increasing, and new disasters such as COVID 19 and sinkholes, and large-scale disasters that combine natural and social disasters are occurring frequently. In order to reduce damage caused by disasters and effectively respond to disasters, the importance of disaster safety education is emerging because it is necessary to understand the awareness of disaster situations and the functional response process. Ministry of Public Interior and Security is providing disaster safety education for emergency managers through 54 specialized disaster safety education institutions. There is also a lack of experience facilities. This has a problem in that it makes it difficult for disaster safety personnel to effectively respond to disasters due to lack of experience in actual disaster sites. Also, unlike other education fields, the connection between disaster safety education contents and new technologies such as AI is still lacking. In this study, focusing on natural disaster, the current status and problems of domestic disaster safety education institutions and their contents are investigated and analyzed, and based on this, this study suggested improvement plans for domestic disaster safety education contents such as establishment of a unified disaster safety standard curriculum, production and distribution of disaster safety education experience contents using virtual reality technology and infotainment technology, and development of mobile AI tutoring service.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.331-339
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• 2022

The global process of transition from industrial to information society, as well as socio-economic changes taking place in Ukraine, require significant changes in many areas of state activity. It is especially connected with the reforms in the sphere of education. Today, national programs provide for the development of education on the basis of new progressive concepts, the introduction of the educational process of new pedagogical technologies and scientific achievements, the creation of a new system of information education, entrance of Ukaine into the transcontinental computer information system. Information technologies are qualitatively changing the key resources of development: this is no longer a space with fixed production, but primarily mobile finance and intelligence. They have a direct impact on the formation of personal growth, professional content and self-organization, emotional and psychological maturity and consciousness, and so on. One of the main factors in ensuring the stability and social education of the country's citizens is the culture of security, the formation and development of which is an urgent problem today. Comprehensive and systematic development of security culture will significantly increase the readiness of the population, the level of environmental, labor and patriotic education, reduce human losses, material damage from emergencies. Ecological education can be carried out more successfully only gradually and in accordance with the socio-psychological periods of one's development: kindergarten - school - college - university. The creation of such a system of environmental education should be enshrined as the basis of state environmental policy as a constitutional norm with the usage of information technology. Graduates of universities, who are the future of our country, after mastering the skills of basic environmental education must have a high level of environmental culture, which is, in turn, part of general human culture, and investigate environmental issues from the standpoint of their profession. It is known that with the help of environmental education the collective intelligence of society is formed, which can predict human activities and processes occurring in nature, and in some way to help with the elimination of crises. It is through environmental education that another system of human values is being formed, which places great emphasis on intangible wealth and solidarity, and great responsibility of humanity for the ecological state of the native country; provides a higher standard of living as a result of sustainable development, through the introduction of information technology in this system. To improve the quality of life, we need better knowledge, which must be implemented through information technology at the international level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of Korea Entertainment Industry Association
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• v.13 no.1
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• pp.25-34
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• 2019

The purpose of this study is to develop a mobile app for mental health self care of university student. User centered design is a research design that applies the subject's needs assessment, analysis, design, development, evaluation, modification and supplement to suit the subjects. In order to manage the mental health of university students, they consisted of four main areas of mental health problems: drinking, sleeping, depression, and stress. It is designed to enable self test content, analysis and notification of inspection results, and management plan for current status of each area. Based on this, I developed an Android based mental health self-care Application. The subject can enter his or her mental health status data to explain the normal or risk level for each result, and the subject can then select the appropriate intervention method that he or she can perform. In addition, we developed a mental health self care calendar that can display the present status of each of the four areas on a day by day basis, and the current status can be expressed in an integrated manner through animations and status bars. The purpose of this study was to develop a mental health self-care app that can be improved by continuous and improved programs.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.197-216
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• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.49 no.4
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• pp.291-298
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• 2023

In this study, a quantitative analysis method was developed using high-performance liquid chromatography (HPLC) to analyze the content of ceramide NP in lotion, cream, and cleanser formulations in cosmetics. The analysis was performed using a C18 column, and the mobile phase was set at a ratio of 70 : 30 for acetonitrile and methanol, the flow rate was set to 0.8 mL/min, and the column temperature was set to 20 ℃. The method was verified by analyzing specificity, linearity, limit of detection, limit of quantitation, accuracy, and precision in accordance with the ICH guidelines. As a result of validating the method, the linearity of the calibration curve was excellent (R² = 0.99984). The accuracy of the lotion, cream, and cleanser formulations was confirmed with a recovery rate ranging from 95.11% to 100.48%. The precision analysis showed a low relative standard deviation (RSD) of less than 0.26%. The limit of detection was 0.902 ㎍/mL, and the limit of quantitation was 2.733 ㎍/mL. Through this quantitative analysis method of ceramide NP applied in cosmetics, it is expected to assist in the quality control of products by enabling measurement even when it is difficult to separate the main peak due to the influence of interfering substances.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.97-107
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• 2024

This study aims to identify the factors that affect customer satisfaction as the market growth of rental subscription services for living environment home appliances increases. Unlike previous research, which focused on online subscriptions (e.g., digital content, over-the-top (OTT) services, e-books, and mobile devices), this study expands the scope to include rental subscriptions for household environmental appliances. Specifically, this study analyzes the factors influencing customer satisfaction among rental subscription service users by examining the effects of perceived usefulness, reliability, and convenience. The results show that users' perceived reliability and convenience of rental subscription services for living environment home appliances significantly affect customer satisfaction. Perceived usefulness, however, was not found to have a significant impact, as it is an abstract and subjective customer aspect. The implications of the results are as follows: First, standardized services must be strengthened to increase the reliability of rental subscription services. Additionally, it is necessary to improve convenience by developing additional services when managing regular visits tailored to the characteristics of each product. Providing customized services by integrating products and Information and Communications Technologies (ICT). Furthermore, effective customer management to increase customer satisfaction is crucial, as it can lead to cross-selling and up-selling opportunities. Lastly, venture start-ups should actively apply a subscription service business model.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.8-11
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• 2012

Purpose : Among quality control items, the radiochemical impurity must be below 10% of total radioactivity. In this regard, as the recently commercialized automatic synthesis module produces a large amount of 18F-FDG, radiolysis of radiopharmaceuticals is very likely to occur. Thus, this study compared the changes in radiochemical purity regarding radiolysis of $^{18}F$-FDG according to automatic synthesis module. Materials and methods : Cyclotron (PETtrace, GE Healthcare) was used to produce $^{18}F$ and automatic synthesis module (FASTlab, Tracerlab MX, GE Healthcare) was used to achieve synthesis into FDG. For radiochemical purity, Radio-TLC Scanner (AR 2000, Bioscan), GC (Gas Chromatograph, Agilent 7890A) was used to measure the content of ethanol included in $^{18}F$-FDG. Glass board applied with silica gel ($1{\times}10cm$) was used for stationary phase while a mixed liquid formed of acetonitrile and water (ratio 19:1) was used for mobile phase. High-concentration and low-concentration $^{18}F$-FDG were produced in each synthesis module and the radiochemical purity was measured every 2 hours. Results : The purity in low-concentration (below 2.59 GBq/mL) was measured as 99.26%, 98.69%, 98.25%, 98.09% in Tracerlab MX and as 99.09%, 97.83%, 96.89%, 96.62% in FASTlab according to 0, 2, 4, 6 hours changes, respectively. The purity in high-concentration (above 3.7 GBq/mL) was measured as 99.54%, 96.08%, 93.77%, 92.54% in Tracerlab MX and as 99.53%, 95.65%, 92.39%, 89.82% in FASTlab according to 0, 2, 4, 6 hours changes, respectively. Also, ethanol was not detected in GC of $^{18}F$-FDG produced in FASTlab, while 100~300 ppm ethanol was detected in Tracerlab MX. Conclusion : Whereas the change of radiochemical purity was only 3% in low-concentration $^{18}F$-FDG, the change was rapidly increased to 10% in high-concentration. Also, higher radiolysis were observed in $^{18}F$-FDG produced in FASTlab than Tracerlab MX. This is because ethanol is included in the synthesis stage of Tracerlab MX but not in the synthesis stage of FASTlab. Thus, radiolysis is influenced by radioactivity concentration than the inclusion of ethanol, which is the radioprotector. Therefore, after producing high-concentration $^{18}F$-FDG, the content must be diluted through saline to lower concentration.