• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.10-27
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• 2018

The purpose of this study is to explore the meaning of happiness and unhappiness in Korean society through text mining analysis. Similar words with keywords(happiness/unhappiness) from online news portal are extracted using Word2Vec and TF-IDF method. We also use the K-LIWC dictionary to perform the sentiment analysis of words associated with happiness and unhappiness. In TF-IDF analysis, happiness and unhappiness are highly related to social factors and social issues of the year. In Word2Vec analysis, 'Hope' has been similar with happiness for six years. In K-LIWC analysis, 'money/financial issues', 'school', 'communication' is highly related with happiness and unhappiness. In addition, 'physical condition and symptom' is highly related to unhappiness. Implications, limitations, and suggestions for future research are also discussed.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.7-15
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• 2022

Recently, due to the development of AI and big data technologies following the advent of the era of the 4th Industrial Revolution, the emerging metaverse industry is emerging as a new business, and in particular, from this point of view, this paper analyzes the history of metaverse and the pros and cons of "Geppetto", which is the most popular in the Korean metaverse market, and aims to give an appropriate direction for future development based on this. In order to carry out this study, we first used SWOT analysis techniques as an initial enterprise analysis method to examine the strengths and weaknesses, opportunities and threat requirements, and derive the status of each factor. Based on the factors in each of the subsequent derivatives, we wanted to explore the TOWS development strategy and present significant implications based on this.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.201-209
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• 2024

The COVID-19 pandemic has significantly impacted the aviation industry, leading to worldwide changes in travel restrictions and security measures. This study analyzes 59,818 reviews of 147 airlines from the SKYTRAX website between 2016 and 2023 to understand the changes in airline service satisfaction before and after the pandemic. Using sentiment analysis, the study compares overall satisfaction, review sentiment, and attributes influencing satisfaction. The results show a statistically significant (p<0.001) decrease in overall satisfaction post-COVID-19, with reduced positive sentiment and increased negative sentiment for all airline selection attributes, except cabin and in-flight services. Flight operation services had the most significant impact on overall satisfaction during both periods. This quantitative analysis of global major airlines' satisfaction attributes before and after COVID-19 contributes to enhancing future service satisfaction in the airline industry.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.334-343
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• 2023

Purpose: In this study, the cavity data found through ground cavity exploration was combined with underground facilities to derive a correlation, and the ground subsidence prediction map was verified based on the AI algorithm. Method: The study was conducted in three stages. The stage of data investigation and big data collection related to risk assessment. Data pre-processing steps for AI analysis. And it is the step of verifying the ground subsidence risk prediction map using the AI algorithm. Result: By analyzing the ground subsidence risk prediction map prepared, it was possible to confirm the distribution of risk grades in three stages of emergency, priority, and general for Busanjin-gu and Saha-gu. In addition, by arranging the predicted ground subsidence risk ratings for each section of the road route, it was confirmed that 3 out of 61 sections in Busanjin-gu and 7 out of 68 sections in Sahagu included roads with emergency ratings. Conclusion: Based on the verified ground subsidence risk prediction map, it is possible to provide citizens with a safe road environment by setting the exploration section according to the risk level and conducting investigation.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.76-86
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• 2016

SQL Injection attacks are the most widely used and also they are considered one of the oldest traditional hacking techniques. SQL Injection attacks are getting quite complicated and they perform a high portion among web hacking. The big data environments in the future will be widely used resulting in many devices and sensors will be connected to the internet and the amount of data that flows among devices will be highly increased. The scale of damage caused by SQL Injection attacks would be even greater in the future. Besides, creating security solutions against SQL Injection attacks are high costs and time-consuming. In order to prevent SQL Injection attacks, we have to operate quickly and accurately according to this data analysis techniques. We utilized data analytics and machine learning techniques to defend against SQL Injection attacks and analyzed the execution plan of the SQL command input if there are abnormal patterns through checking the web log files. Herein, we propose a way to distinguish between normal and abnormal SQL commands. We have analyzed the value entered by the user in real time using the automated SQL Injection attacks tools. We have proved that it is possible to ensure an effective defense through analyzing the execution plan of the SQL command.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.139-159
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• 2022

As interest in health functional foods has increased since COVID-19, the importance of imported food safety inspections is growing. However, in contrast to the annual increase in imports of health functional foods, the budget and manpower required for inspections for import and export are reaching their limit. Hence, the purpose of this study is to propose a machine learning model that efficiently detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export inspections data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, in order to select features for the machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. In particular, the ensemble model was the best, and it was confirmed that the derived variables and models proposed in this study can be helpful to the system used in import/export inspections.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.

• Convergence Security Journal
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• v.16 no.2
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• pp.25-34
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• 2016

This study aims to prevent the serious threat from dangerous person or group by responding or blocking or separating illegal activities by use of SNA: Social Network Analysis. SNA enables to identify the complex social relation of suspect and individuals in order to enhance the effectiveness and efficiency of investigation. SNS has rapidly developed and expanded without restriction of physical distance and geo-location for making new relation among people and sharing large amount of information. As rise of SNS(facebook and twitter) related crimes, terrorist group 'ISIS' has used their website for promotion of their activity and recruitment. The use of SNS costs relatively lower than other methods to achieve their goals so it has been widely used by terrorist groups. Since it has a significant ripple effect, it is imperative to stop their activity. Therefore, this study precisely describes criminal and terrorist activities on SNS and demonstrates how effectively detect, block and respond against their activities. Further study is also suggested.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.19-24
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• 2020

Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.1
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• pp.9-18
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• 2018

Enhancing performance of big data analytics in distributed environment has been issued because most of the big data related applications such as machine learning techniques and streaming services generally utilize distributed computing frameworks. Thus, optimizing performance of those applications at Spark has been actively researched. Since optimizing performance of the applications at distributed environment is challenging because it not only needs optimizing the applications themselves but also requires tuning of the distributed system configuration parameters. Although prior researches made a huge effort to improve execution performance, most of them only focused on one of three performance optimization aspect: application design, system tuning, hardware utilization. Thus, they couldn't handle an orchestration of those aspects. In this paper, we deeply analyze and model the application processing procedure of the Spark. Through the analyzed results, we propose performance optimization schemes for each step of the procedure: inner stage and outer stage. We also propose appropriate partitioning mechanism by analyzing relationship between partitioning parallelism and performance of the applications. We applied those three performance optimization schemes to WordCount, Pagerank, and Kmeans which are basic big data analytics and found nearly 50% performance improvement when all of those schemes are applied.