• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.99-105
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• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.797-807
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• 2023

This research aims to investigate the alterations in typhoon patterns attributable to climate change and to quantitatively assess the risk of damage to residential structures. The increasing prevalence of climate anomalies and severe weather events, a consequence of global warming, is causing escalating damage globally. Notably, numerous countries are facing substantial devastation due to shifts in typhoon trajectories. Despite this, there exists a gap in empirical research quantifying the impact of these changes on building integrity and the associated risk alterations driven by climate change. In addressing this gap, our study analyzes the frequency and intensity of typhoons impacting Korea, examining the evolution of these meteorological phenomena. Furthermore, we employ the Korean Typhoon Vulnerability Function for residential facilities to quantify the altered risk posed by these changing patterns. The outcomes of this study provide the private sector with essential data to formulate diverse scenarios and business strategies in response to the escalating risks of typhoon-related damage. Additionally, it equips governmental bodies with the necessary insights to develop comprehensive risk management strategies to mitigate the effects of future typhoons.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.95-103
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• 2024

With the rapid decline of the school-age population, the competition for admissions has increased and marketing through digital channels has become more important, so universities are investing more resources in online promotion and communication to recruit new students. This study uses Google Analytics, a web log analysis tool, to track the visitor behavior of a university admissions website and establish a digital marketing strategy based on it. The analysis period was set from July 1, 2023, when Google Analytics 4(GA4) was integrated, to January 10, 2024, when the college admissions process was completed. The analysis revealed interesting patterns such as geographical information based on visitors' access location, devices(operating systems) and browsers used by visitors, acquisition channels through visitors traffic, conversions on pages and screens that visitors engaged with and visitor flow. Based on this study, we expect universities to find ways to strengthen their admission promotion through digital marketing and effectively communicate with applicants to gain a competitive edge.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.51-58
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• 2024

This paper examines the differences between the Korean and American stock markets using the Kospi and S&P 500 indices and discusses policy implications through them. To this end, in addition to the existing time series analysis method, a deep learning method was used to compare markets, and the comparison was made in terms of stock price forecasting ability and data generation ability. In monthly data, the difference between time series was not large, and in daily data, the difference in terms of stability was weak, and there was no significant difference in predictive power or simulation data generation. As shown in the results of this study, if there is not much difference in market price movement patterns between Korea and the United States, tax benefits for long-term stocks investment will be effective against the side effects of short selling.

• Convergence Security Journal
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• v.24 no.2
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• pp.39-45
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• 2024

The purpose of security control in the public sector is to secure the safety of administrative services for the public by preventing resource loss or information infringement in information systems and information and communication networks. The security control system is a process that performs real-time detection, analysis, response, and reporting through system vulnerability analysis and security system detection pattern optimization. This study aims to objectively identify the current situation of the mismatch between the supply and demand of cyber security control centers currently in operation and specialized security control companies that can be entrusted to operate them, and to derive and propose practical and institutional improvement measures. Considering that the operation of security control centers in the public sector is expected to increase in the future, research on the practical supplementation required for the operation process of security control centers and the improvement of the designation system of security control specialized organizations has fundamental and timely significance, and it is an area that requires continuous research in terms of strategic industrialization.

• Information Systems Review
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• v.25 no.3
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• pp.1-26
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• 2023

A theoretical model of information overload (Jackson and Farzaneh, 2012) with its three influential components (i.e., time, technology, and social networks) was empirically tested in the context of news consumption behavior considered as a communicative outcome. Using a national sample of South Korean adults (N = 1166), data analyses identified perceived information overload and large/diverse social networks positively associated with active and passive news consumption. Findings may imply the existence of individually varying cognitive threshold (i.e., tipping point), if crossed individuals cannot process information any further. News consumers may keep searching and receiving information to verify factuality of news even when they feel overloaded.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.851-860
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• 2024

This study analyzes the impact of online streaming services, particularly Over The Top (OTT) services, on consumer media consumption patterns in the digital age. It examines how consumer-centric service values affect OTT platform choice, consumer satisfaction, and brand loyalty, focusing on various factors such as content diversity, ease of use, affordability, brand awareness, and personalized services. The findings reveal that content diversity, ease of use, and affordability are significant factors positively influencing consumer satisfaction and brand loyalty, thereby motivating OTT platform selection. Contrary to expectations, personalized services did not have a significant impact. This research provides critical insights for OTT service providers to enhance consumer-centric values and develop competitive service strategies.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.4
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• pp.315-326
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• 2024

In this study, the Delphi method and AHP (analytic hierarchy process) were used to evaluate tunnel collapse risk from a comprehensive and multifaceted perspective. Influence factors were established through literature reviews, previous studies, and brainstorming sessions with expert groups, resulting in the construction of five main classification systems. A panel of 21 experts was formed, and three rounds of Delphi surveys were conducted to prevent errors and biases in the expert judgment process, thereby enhancing reliability. Ultimately, 14 influence factors were identified through CVR (content validity ratio) and COV (coefficient of variation) analyses of the experts' responses. Subsequently, the AHP method was applied to assess the relative importance of each influence factor and calculate the final composite weights. The timing of support and reinforcement had the highest priority, followed by groundwater inflow, joint conditions, support pattern levels, and auxiliary methods. These findings help identify the key factors affecting tunnel collapse risk and provide a foundation for developing strategies to improve tunnel safety.

• The Korean Journal of Applied Statistics
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• v.37 no.5
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• pp.675-690
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• 2024

In modern society, the rapid growth of streaming services has significantly changed the way people consume television. This study aims to analyze the causal impact of streaming service usage on IPTV viewing. To achieve this, we compared users who use streaming services with those who do not while controlling for many possible confounders. We employed causal inference matching methods, focusing particularly on several matching techniques to compare groups with similar characteristics. Additionally, we used regression methods using matching-driven weights to assess the statistical significance of the causal effect. The results indicate that streaming service usage has a significant impact on how IPTV is consumed. These findings provide important insights for content providers, broadcasters, and advertisers to understand viewer behavior patterns and make strategic decisions accordingly. This study offers new insights into the relationship between streaming services and traditional TV viewing and can serve as a foundation for future related research.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.1-9
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• 2024

In this paper, we propose a new approach by analyzing driver behavior and gaze changes within the vehicle in real-time to assess and predict the risk of traffic accidents. Utilizing data analysis and machine learning algorithms, this research precisely measures drivers' abnormal behaviors and gaze movement patterns in real-time, and aggregates these into an overall Risk Score to evaluate the potential for traffic accidents. This research underscores the significance of internal factors, previously unexplored, providing a novel perspective in the field of traffic safety research. Such an innovative approach suggests the feasibility of developing real-time predictive models for traffic accident prevention and safety enhancement, expected to offer critical foundational data for future traffic accident prevention strategies and policy formulation.