• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.54-69
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• 2023

This study investigated the distribution of users in urban parks to overcome the limitations of existing research, which made it difficult to determine where data came was collected. It aimed to provide implications for park planning and management based on user distribution using mobile signal data. Five urban parks in Seongdong-gu, Seoul, with various physical characteristics, were selected. Mobile signal data provided by the Seoul Big Data Campus was used to identify the distribution of user inflow through hot spot analysis per park. The relationship between urban context and park influence area was derived. Seoul Forest (P1) and Seongsu Park (P3), which have a high proportion of commercial spaces around the park, showed wider user hotspots compared to Eungbong Park (P2), Dokseodang Park (P4), and Daehyunsan Park (P5), which were located in residential areas. Parks with a significant presence of commercial spaces had a broader influence, while parks with larger sizes and gentle slopes exhibited wider influence areas. This study proposed a novel data-based approach to urban park planning and management based on the inflow distribution of park users. Through this research, valuable insights were derived that could be utilized for urban park planning and management, aiming to enhance the effectiveness and efficiency of park utilization.

• 재활복지
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• v.15 no.4
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• pp.351-375
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• 2011

The purpose of this study is to research the actual condition of on-line and off-line computer education in order to create more opportunities for the possibility of effective learning and u-Learning - based IT vocational education development of persons with hearing impairments. To carry out this study, we had a preliminary education of this study for a sign language interpreters who had working in a association of the deaf in Daegu, and had conducted a survey study participating for 100 persons with hearing impairments living in Daegu-Kyungbook Region. The results of this study were as follows: First, during on-line and offline education environment for persons with hearing impairments, factors such as teaching methods and contents, screen organization, learning effects, offering lecture material and subtitle, subtitle and sign language video of position and size, offering computer instructor who have a sign language interpreter with IT mentoring, on-line educational user environment setting must be considered factors for u-Learning - based IT vocational education development. Second, 74% of the subjects showed their interest in taking a course, after u-Learning contents are developed for persons with hearing impairments. Third, the subjects preferred IT device was a tablet PCs and IT certification related courses as their IT vocational education curriculum. Also, to link between professional training and job opportunity, subjects will need a IT vocational education courses such as computer intermediate and advanced levels.

• Information Systems Review
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• v.25 no.4
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• pp.161-181
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• 2023

As the post-COVID-19 consumer life environment is rapidly becoming non-face-to-face, changing non-face-to-face financial life services are having a significant impact on consumers' daily lives. People who do not have access to digital devices and services that have become essential goods are at risk of being left behind in the "digital blind spot," where they are marginalized not only in their daily lives but also in society and the economy as a whole (Kim Min-Jeung A, Kim Min-Jung B, Park Joo-Yung, 2022). In this study, we examined the effects of perceived control factors, Cognitive control, behavioral control, and decisional control, on intention to use digital finance. For this study, we surveyed 133 customers who are aware of and intend to use digital finance. The results show that cognitive control, behavioral control, and decisional control have significant effects on intention to use digital finance. In this relationship, the moderating effect of privacy concerns differs from the effect of decision control on intention to use digital finance. These findings suggest that digital financial services firms should consider whether to reduce or increase customer control. Based on these findings, we discuss marketing strategies and implications for digital financial services companies.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.57-68
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• 2023

With the technological development in the era of the 4th Industrial Revolution, technologies using HMD are being applied in various fields. HMD is especially useful in virtual reality fields such as AR/VR, and is very effective in receiving vivid impressions from users located in remote locations. According to these characteristics, the frequency of using HMD is increasing in the field related to collaboration. However, when HMD is applied to collaboration, communication between experts located in remote locations and workers located in the field is not smooth, causing various problems in terms of usability. In this paper, remote experts and workers in the field use HMD to solve various problems arising from collaboration, design/propose new types of HMD structures and functions that enable more efficient collaboration, and verify their usability using SUS evaluation techniques. As a result of the SUS evaluation, the new type of HMD structure and function proposed in this paper was 86.75points, which is believed to have greatly resolved the restrictions on collaboration and inconvenience in use of the existing HMD structure. In the future, when the HMD structure and design proposed in this paper are actually applied, it is expected that the application technology using HMD will expand rapidly.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.159-174
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• 2023

In this paper, we proposed a methodology to effectively determine the selection and prioritization of new business and innovation projects using AI technology. AI technology is a technology that can upgrade the business of companies in various industries and increase the added value of the entire industry. However, there are various constraints and difficulties in the decision-making process of selecting and implementing AI projects in the enterprise. In this paper, we propose a new methodology for prioritizing AI projects using Morphology, AHP, and TOPSIS. The proposed methodology helps prioritize AI projects by simultaneously considering the technical feasibility of AI technology and real-world user requirements. In this study, we applied the proposal methodology to a real enterprise that wanted to prioritize multiple AI projects in the HR field and evaluated the results. The results confirm the practical applicability of the methodology and suggest ways to use it to help companies make decisions about AI projects. The significance of the methodology proposed in this study is that it is a framework for prioritizing multiple AI projects considered by a company in the most reasonable way by considering both business and technical factors at the same time.

• Information Systems Review
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• v.23 no.3
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• pp.201-223
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• 2021

Smart home is an advanced Internet of Things (IoT) service that enhances the convenience of human daily life and improves the quality of life at home. Recently, with the emergence of smart home products and services to which artificial intelligence (AI) technology is applied, interest in smart home is increasing. To gain a competitive edge in the smart home market, companies are providing "personalized service" to users, which is a key service that can promote smart home use. This study investigates the factors affecting the value awareness of personalized service and intention to use smart home. This research focuses on four-dimensional motivated innovativeness (cognitive, functional, hedonic, and social innovativeness) and privacy risk awareness as key factors that influence the value awareness of personalized service of smart home. In particular, this study conducts a comparative analysis between the generation MZ (young people in late teens to 30s), who are showing socially differentiated characteristics, and the generation X and baby boomers in 40s to 50s or older. Based on the analysis results, this study derives the distinctive characteristics of generation MZ that are different from the older generation, and provides academic and practical implications for expanding the use of smart home services.

• Information Systems Review
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• v.23 no.3
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• pp.97-125
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• 2021

Blockchain is a core technology to solve personal information leakage and data management issues, which are limitations of existing Genomic Sequencing services. Due to continuous cost reduction and deregulation, the market size of Genomic Sequencing has been increasing, also the potential of services is expected to increase when Blockchain's security and connectivity are combined. We created our research model by combining the Technology Acceptance Model (TAM) and the Innovation Resistance Theory also analyzed the factors affecting the acceptance intention and innovation resistance of the Blockchain Based Genomic Sequencing Information Platform. A survey was conducted on 150 potential users of Blockchain and Genomic Sequencing services. The analysis was conducted by setting the four Blockchain variables: Security, transparency, availability, and diversity). Also, we set the Perceived Usefulness, Perceived risk, and Perceived Complexity for Technology Acceptance and Innovation Resistance variables and analyzed the effect of the characteristics of the Blockchain on acceptance intention and innovation resistance through these variables. Through this analysis, key variables that need to be considered important to reduce resistance and increase acceptance intention could be identified. This study presents innovation factors that should be considered in companies preparing a new Blockchain Based Genomic Sequencing Information Platform.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.109-118
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• 2024

With the increasing number of social media users worldwide, cases of social media being abused to perpetrate various crimes are increasing. Specifically, drug distribution through social media is emerging as a serious social problem. Using social media channels, the curiosity of teenagers regarding drugs is stimulated through clever marketing. Further, social media easily facilitates drug purchases due to the high accessibility of drug sellers and consumers. Among various social media platforms, we focused on Instagram, which is the most used social media platform by young adults aged 19 to 24 years in South Korea. We collected four types of information, including profile photos, introductions, posts in the form of images, and posts in the form of texts on Instagram; then, we analyzed the similarity among each type of collected information. The profile photos and posts in the form of image were analyzed for similarity based on the SSIM(Structural Simplicity Index Measure), while introductions and posts in the form of text were analyzed for similarity using Jaccard and Cosine similarity techniques. Through the similarity analysis, the similarity among various accounts for each collected information type was measured, and accounts with similarity above the significance level were determined as the same drug sales account. By performing logistic regression analysis on the aforementioned information types, we confirmed that except posts in image form, profile photos, introductions, and posts in the text form were valid information for tracking the same drug sales account.