• 한국전산유체공학회:학술대회논문집
• /
• 한국전산유체공학회 2011년 춘계학술대회논문집
• /
• pp.183-186
• /
• 2011

There are numerous crashed deaths or near-miss accidents in everywhere. The outbreak place is not just developing countries such as India and Iran, but also leading countries including Japan and German. The crashed death of pedestrian seems to be an unavoidable accident. However, it was revealed in social psychology field that the accident can be treated as a kind of physical phenomenon. In this study, we apply discrete element method frequently used in the field of two-phase flow to pedestrian flow with collective behavior psychology. This approach is a field of social science and physics, called computational sociology. The acquired results show that emergency exit size can be related with the wall slope of the exit.

• Journal of applied mathematics & informatics
• /
• 제41권5호
• /
• pp.1129-1143
• /
• 2023

Social media platforms have become an integral part of our daily lives, and they generate vast amounts of data that can be analyzed for various purposes. However, the quality of the data obtained from social media is often questionable due to factors such as noise, bias, and incompleteness. Enhancing data quality is crucial to ensure the reliability and validity of the results obtained from such data. This paper proposes an enhanced decision-making framework based on Business Decision Management Systems (BDMS) that addresses these challenges by incorporating a data quality enhancement component. The framework includes a backtracking method to improve plan failures and risk-taking abilities and a steep optimized strategy to enhance training plan and resource management, all of which contribute to improving the quality of the data. We examine the efficacy of the proposed framework through research data, which provides evidence of its ability to increase the level of effectiveness and performance by enhancing data quality. Additionally, we demonstrate the reliability of the proposed framework through simulation analysis, which includes true positive analysis, performance analysis, error analysis, and accuracy analysis. This research contributes to the field of business intelligence by providing a framework that addresses critical data quality challenges faced by organizations in decision-making environments.

• 인터넷정보학회논문지
• /
• 제20권6호
• /
• pp.85-93
• /
• 2019

Computational Science and Engineering is a convergence study that understands and solves complex problems such as science, engineering, and social phenomena through modeling using computing resources. Computational science and engineering combines algorithms, computational and informatics, and infrastructure. The importance of computational science is increasing with the improvement of computer performance and the development of large data processing technology. In Korea, Korea Institute of Science and Technology Information (KISTI) has been developing national computational science engineering software and utilization technology by combining basic science and computing technology through EDISON project. The EDISON project builds an open EDISON platform and integrates and services information systems in seven areas of computational science and engineering (computational thermal fluids, nanophysics, computational chemistry, structural dynamics, computational design, and computational medicine). Using this, we have established a web-based curriculum to lay the groundwork for fostering scientific talent and commercializing computational science and engineering software. The purpose of this study is to derive the quality characteristic factors of computational science platform and to empirically examine the effect on user satisfaction. This paper examines how the quality characteristics of information systems, the computational science engineering platform, affect the user satisfaction by modifying the research questions according to the propensity of the computational science platform by referring to the success factors of DeLone and McLean's information system. Based on the results of this study, we will suggest strategic implications for platform improvement by searching the priority of quality characteristics of computational science platform.

• Journal of applied mathematics & informatics
• /
• 제40권1_2호
• /
• pp.289-303
• /
• 2022

This article examines the multidimensional index extraction method of the disability social security system based on data mining. While creating the data warehouse of the social security system for the disabled, we need to know the elements of the social security indicators for the disabled. In this context, a clustering algorithm was used to extract the indicators of the social security system for the disabled by investigating the historical dimension of social security for the disabled. The simulation results show that the index extraction method has high coverage, sensitivity and reliability. In this paper, a multidimensional extraction method is introduced to extract the indicators of the social security system for the disabled based on data mining. The simulation experiments show that the method presented in this paper is more reliable, and the indicators of social security system for the disabled extracted are more effective in practical application.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제4권4호
• /
• pp.547-559
• /
• 2010

As web-based online communities are rapidly growing, the agents in the communities need to know their measurable belief of trust for safe and successful interactions. In this paper, we propose a computational model of trust resulting from available feedbacks in online communities. The notion of trust can be defined as an aggregation of consensus given a set of past interactions. The average trust of an agent further represents the center of gravity of the distribution of its trustworthiness and untrustworthiness. Furthermore, we precisely describe the relationships among reputation, trust and average trust through concrete examples showing their computations. We apply our trust model to online social networks in order to show how trust mechanisms are involved in the rational purchasing decision-making of buyers and sellers, and we summarize our simulation results.

• International Journal of Computer Science & Network Security
• /
• 제24권8호
• /
• pp.61-71
• /
• 2024

The dynamic nature of Online Social Networks (OSNs), especially on platforms like Twitter, presents challenges in identifying and responding to community attacks, particularly within Arabic content. The proposed integrated system addresses these challenges by achieving 91% accuracy in detecting real-time community event attacks while efficiently managing computational costs. This is accomplished through the use of specialized integrated approach clustering to detect both major and minor attacks. Additionally, the system leverages clustering algorithms, temporal modules, and social network graphs to identify events, map communities, and analyze online dynamics. An extensive parameter sensitivity analysis was conducted to fine-tune the algorithm, and the system's effectiveness was validated using a benchmark dataset, demonstrating substantial improvements in event detection.

• Journal of Information Technology Applications and Management
• /
• 제20권2호
• /
• pp.177-191
• /
• 2013

Social network has been expected to increase the value of social capital through online user interactions which remove geographical boundary. However, online users in social networks face challenges of assessing whether the anonymous user and his/her providing information are reliable or not because of limited experiences with a small number of users. Therefore. it is vital to provide a successful trust model which builds and maintains a web of trust. This study aims to propose a prediction method for the interpersonal trust which measures the level of trust about information provider in Facebook. To develop the prediction method. we first investigated behavioral research for trust in social science and extracted 5 antecedents of trust : lenience, ability, steadiness, intimacy, and similarity. Then we measured the antecedents from the history of interactive behavior and built prediction models using the two decision trees and a computational model. We also applied the proposed method to predict interpersonal trust between Facebook users and evaluated the prediction accuracy. The predicted trust metric has dynamic feature which can be adjusted over time according to the interaction between two users.

• 한국경영과학회지
• /
• 제25권1호
• /
• pp.93-104
• /
• 2000

Passive agents participate in team activities passively, that is, only upon requst, whereas active agents involve themselves voluntarily. Teams composed of active agents are generally believed to perform better than those with passive agents. In this paper, by using a computational simulation model we examine the effect of agent activeness on the efficiency of decision-making teams that access different amout of information. "Team-Soar" is a computational fraemwork that consists of a group of interconnected individual Al agents (i.e., Soar). A simulation experiment using Tearm-Soar was performed. Results of the simulation provide valuable insights on the roles of agent activeness. For example, the impact of having more active agents becomes more sigfniciant as the amout of information to process increases and when the team decision efficiency is important. Some of the results are counter-intultive and therefore provides an opportunity to understand the roles of the agnet activeness more deeply. For instance, the simulation results reveal that having more active agents did not always enhance team efficiency. Conclusively, the simulation experiment demonstrates how computational models contribute to the research of agents social characteristics.teristics.

• Asian Journal of Innovation and Policy
• /
• 제11권2호
• /
• pp.202-221
• /
• 2022

The media has depicted an abrupt socio-technological change in the Republic of Korea with the 4th Industrial Revolution. Because technologies cannot realize their potential without social acceptance, studying conflicts incurred by such a change is imperative. However, little literature has focused on conflicts caused by technologies. Therefore, the current study investigated media coverage regarding conflicts related to the 4th Industrial Revolution from 2016 to 2020 in the Republic of Korea, applying text-mining techniques. We found that the overall amount and coverage pattern conforms to the issue attention cycle. Also, the three major topics ("SMEs & Startups," "Mobility Conflict," and "Human & Technology") indicate quarrels between conflicting social entities. Moreover, the temporal change in media coverage implies the political use of the term rather than technological. However, we also found the media's deliberative discussion on the socio-technological impact. This study is significant because we expanded the discussion on media coverage of technologies to the realm of social conflicts. Furthermore, we explored the news articles of the recent five years with a text-mining approach that enhanced the objectivity of the research.

• Journal of Information Processing Systems
• /
• 제17권2호
• /
• pp.213-226
• /
• 2021

Given that most of the link prediction algorithms for signed social networks can only complete sign prediction, a novel algorithm is proposed aiming to achieve both link prediction and sign prediction in signed networks. Based on the structural balance theory, the local link tightness and global link tightness are defined respectively by using the structural information of paths with the step size of 2 and 3 between the two nodes. Then the total similarity of the node pair can be obtained by combining them. Its absolute value measures the possibility of the two nodes to establish a link, and its sign is the sign prediction result of the predicted link. The effectiveness and correctness of the proposed algorithm are verified on six typical datasets. Comparison and analysis are also carried out with the classical prediction algorithms in signed networks such as CN-Predict, ICN-Predict, and PSNBS (prediction in signed networks based on balance and similarity) using the evaluation indexes like area under the curve (AUC), Precision, improved AUC', improved Accuracy', and so on. Results show that the proposed algorithm achieves good performance in both link prediction and sign prediction, and its accuracy is higher than other algorithms. Moreover, it can achieve a good balance between prediction accuracy and computational complexity.