• Journal of Dental Rehabilitation and Applied Science
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• v.38 no.4
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• pp.196-203
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• 2022

Purpose: This study aimed to evaluate the accuracy and clinical usability of an identification model using deep learning for 79 dental implant types. Materials and Methods: A total of 45396 implant fixture images were collected through panoramic radiographs of patients who received implant treatment from 2001 to 2020 at 30 dental clinics. The collected implant images were 79 types from 18 manufacturers. EfficientNet and Meta Pseudo Labels algorithms were used. For EfficientNet, EfficientNet-B0 and EfficientNet-B4 were used as submodels. For Meta Pseudo Labels, two models were applied according to the widen factor. Top 1 accuracy was measured for EfficientNet and top 1 and top 5 accuracy for Meta Pseudo Labels were measured. Results: EfficientNet-B0 and EfficientNet-B4 showed top 1 accuracy of 89.4. Meta Pseudo Labels 1 showed top 1 accuracy of 87.96, and Meta pseudo labels 2 with increased widen factor showed 88.35. In Top5 Accuracy, the score of Meta Pseudo Labels 1 was 97.90, which was 0.11% higher than 97.79 of Meta Pseudo Labels 2. Conclusion: All four deep learning algorithms used for implant identification in this study showed close to 90% accuracy. In order to increase the clinical applicability of deep learning for implant identification, it will be necessary to collect a wider amount of data and develop a fine-tuned algorithm for implant identification.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.35-43
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• 2023

This study tried to find out the applicability and effectiveness of the AI-based adaptive learning system in university classes by operating an AI-based adaptive learning system on a pilot basis. To this end, an AI-based adaptive learning system was applied to analyze the operation results of 42 learners who participated in basic mathematics classes, and a survey and in-depth interviews were conducted with students and professors. As a result of the study, the use of an AI-based customized learning system improved students' academic achievement. Both instructors and learners seem to contribute to improving learning performance in basic concept learning, and through this, the AI-based adaptive learning system is expected to be an effective way to enhance self-directed learning and strengthen knowledge through concept learning. It is expected to be used as basic data related to the introduction and application of basic science subjects for AI-based adaptive learning systems. In the future, we suggest a strategy study on how to use the analyzed data and to verify the effect of linking the learning process and analyzed data provided to students in AI-based customized learning to face-to-face classes.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.2
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• pp.1-11
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• 2018

Recently ICT, new technologies such as IoT, Cloud, and Artificial Intelligence are changing the information society explosively. But personal information leakage incidents of consignee's company are increasing more and more because of the expansion of consignment business and the latest threats such as Ransomware and APT. Therefore, in order to strengthen the security of consignee's company, this study derived the checklists through the analysis of the status such as the feature of consignment and the security standard management system and precedent research. It also analyzed laws related to consignment. Finally we found out the relative importance of checklists after it was applied to proposed AHP(Analytic Hierarchy Process) Model. Relative importance was ranked as establishment of an internal administration plan, privacy cryptography, life cycle, access authority management and so on. The purpose of this study is to reduce the risk of leakage of customer information and improve the level of personal information protection management of the consignee by deriving the check items required in handling personal information of consignee and demonstrating the model. If the inspection activities are performed considering the relative importance of the checklist items, the effectiveness of the input time and cost will be enhanced.

• Journal of Service Research and Studies
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• v.12 no.1
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• pp.1-24
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• 2022

This study was conducted to derive a new model for popular democracy in the modern society. The problem is exacerbated as the original model of democracy, isonomia, has faded and modern democracy is operated as a democracy in which the masses rule. Democracy is exposing a bigger problem due to the economic inequality caused by the majority rule and the capitalist economic system. Popular democracy, in which all the masses are equally qualified as sovereigns, is a structure in which cunning politicians can easily seize power if the voters are not wise. Isonomia is a system in which free people who solve economic problems on their own and have honesty, courage, dedication, sacrifice and sincerity set laws with the same qualifications, and keep the laws themselves, is a system that depends heavily on the conscience and virtue of free people. Modern popular democracy embodies the absolute equality of the sovereign regardless of economic power, but without the spirit of sacrifice and dedication to the community, those who can win popularity by wrapping themselves up as if they are sacrificial and capable people may seize power. This study presents a service-oriented democracy as a system that can maintain the merits of modern popular democracy while embodying the essence of isonomia. The system was implemented focusing on the qualifications for virtue, such as honesty, courage, sacrifice, integrity, etc., fair to all regardless of wealth. Human imperfections have been supplemented by the use of artificial intelligence. The assumption about the nature of the sovereign and public officials' agents was newly established, and the winner-take-allism problem was solved. This study proved that both the philosophy of Isonomia and the merits of Democracy can be realized through service-oriented democracy based on the common principle of humanity and the philosophy of service. Service-oriented democracy can be called revision democracy.

• Journal of Service Research and Studies
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• v.11 no.2
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• pp.1-19
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• 2021

This study was conducted to derive a model of a sustainable politics and administration system that will enhance human happiness. A new long-term sustainable development model has been established based on the experience of the politics and administration system over the past thousands of years. Currently, the democratic political system and the bureaucratic administrative system dominate, but they are facing many problems. In this study, we analyzed the politics and administration system experienced by human society, and derived a model of a politics and administration system that is ideal for the present and future societies and is sustainable in the long term. The necessary condition should be a model that can solve the problems of the current politics and administration system. It must be a model that is faithful to the characteristics and essence of modern society. And as a sufficient condition to ensure long-term sustainability, it must be based on the common principles of human society. After analyzing the problems of the current system and analyzing the conditions required for the new system, the axioms that are the basis of the politics and administration system were presented. Based on the axioms, the structure and operation model of a new politics and administration system were derived. The derived model was named as a servicism politics and administration system. It is a dynamic model in which two opposing opponents recognize each other's contradictions and balance them dialectically in the space-time dimension.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.299-304
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• 2023

Recently, drones have been used as a major means of attack drones in major wars around the world, and it seems likely that they will evolve into game changers in the future. Recently, drones have been used as a major means of attack drones in major wars around the world, and it seems likely that they will evolve into game changers in the future. In the major wars of the 21century, attack drones are used for precision fire-guided or self-destruct attacks, For the purpose of cognitive warfare, its territory is expanding not only to land and air, but also to sea and water. These attack drones will perform multi-domain operations, and for this purpose, the level of autonomy will be improved and High-Low Mix We will continue to develop by strengthening concept-based scalability. North Korea has also been making considerable efforts to operate attack drones for a long time, and activities such as third-country-level self-explosive drones, artificial intelligence-based clustered self-explosive drones, and self-destructive stealth unmanned semi-submersible are expected. In addition to North Korea's provocations and attacks, it is hoped that there will be a need for active follow-up research on our military's countermeasures and utilization plans.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1125-1134
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• 2023

Out of the total 17,000 reservoirs in Korea, 13,600 small agricultural reservoirs do not have hydrological measurement facilities, making it difficult to predict water storage volume and appropriate operation. This paper examined univariate and multivariate long short-term memory (LSTM) modeling to predict the storage rate of agricultural reservoirs using remote sensing and artificial intelligence. The univariate LSTM model used only water storage rate as an explanatory variable, and the multivariate LSTM model added n-day accumulative precipitation and date of year (DOY) as explanatory variables. They were trained using eight years data (2013 to 2020) for Idong Reservoir, and the predictions of the daily water storage in 2021 were validated for accuracy assessment. The univariate showed the root-mean square error (RMSE) of 1.04%, 2.52%, and 4.18% for the one, three, and five-day predictions. The multivariate model showed the RMSE 0.98%, 1.95%, and 2.76% for the one, three, and five-day predictions. In addition to the time-series storage rate, DOY and daily and 5-day cumulative precipitation variables were more significant than others for the daily model, which means that the temporal range of the impacts of precipitation on the everyday water storage rate was approximately five days.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.