• 한국운동역학회지
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• 제31권2호
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• pp.87-94
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• 2021

Objective: Identification of meaningful patterns and trends in large volumes of unstructured data is an important task in various research areas. In the present study, we gathered golf swing image data and did quantitative analysis of swing image. Method: We collected golf swing images of 30 novice players and 30 professional players in this study. Results: We selected important features of swing posture and employed data mining algorithm to classify whether a player is an expert or a novice. Moreover, our proposed method could offer quantitative advices for golf beginners for correcting their swing. Conclusion: Finally, we found a possibility that our proposed method can be expanded to golf swing correction system

• 한국산업융합학회 논문집
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• 제25권4_2호
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• pp.645-653
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• 2022

Recently, deep reinforcement learning has been applied to a variety of industries, such as games, robotics, autonomous vehicles, and data cooling systems. An algorithm called reinforcement learning allows for automated asset allocation without the requirement for ongoing monitoring. It is free to choose its own policies. The purpose of this paper is to carry out an empirical analysis of the performance of asset allocation strategies. Among the strategies considered were the conventional Mean- Variance Optimization (MVO) and the Proximal Policy Optimization (PPO). According to the findings, the PPO outperformed both its benchmark index and the MVO. This paper demonstrates how dynamic asset allocation can benefit from the development of a reinforcement learning algorithm.

• 한국산업융합학회 논문집
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• 제27권2_2호
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• pp.467-473
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• 2024

Metaheuristics' impact is profound across many fields, yet domestic financial portfolio optimization research falls short, particularly in asset allocation. This study delves into metaheuristics for portfolio optimization, examining theoretical and practical benefits. Findings indicate portfolios optimized via metaheuristics outperform the Dow Jones Index in Sharpe ratios, underscoring their potential to enhance risk-adjusted returns significantly. Tabu search, in comparison to Simulated Annealing, demonstrates superior performance by efficiently navigating the search space. Despite these advancements, practical application remains challenging due to the complexities in metaheuristic implementation. The study advocates for broader algorithmic exploration, including population-based metaheuristics, to refine asset allocation strategies further. This research marks a step towards optimizing portfolios from an extensive array of financial assets, aiming for maximum efficacy in investment outcomes.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.717-730
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• 2019

The era of digital transformation (or the fourth industrial revolution) has been triggered by the rapid development of software (SW) technologies. In this era, several studies suspected rapid changes in job structures occurring around the world. Thus, there is a growing need for acquiring the skill sets required for the future. However, there are no specific studies on how existing jobs are changing. To cope with this ambiguity of job changes, this paper aims to investigate how the current job structure is changing in response to digital transformation. To identify the dynamic nature of job change over time, we conducted an analysis based on job posting data. As a result, nine job occupations and fifteen jobs were found.

• The Journal of Asian Finance, Economics and Business
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• 제7권6호
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• pp.265-275
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• 2020

This study investigates the impact of board incentives as proxied by directors' remuneration on the financial performance of listed textile companies in Bangladesh. Using Generalized Method of Moments (GMM) and data pertaining to listed textile companies of Dhaka Stock Exchange (DSE) during the period from 2011 to 2017 (resulting in a total of 140 firm-year observations), we have estimated the firm performance equation involving directors' remuneration and board independence as the independent variables and some other control variables like firm age, size, leverage, and operating efficiency. The results reveal that there is a negative association between board remuneration and firm performance. In addition, this study finds no significant relationship between board independence and firm performance of the sample firms. Our findings suggest that higher pay to the board does not stimulate higher firm performance and, in turn, results in shareholders getting nothing in return from this and, hence, is a matter of great concern for them. Moreover, our results indirectly indicate that currently directors' remuneration in Bangladesh is not aligned with the firm performance, which has been emphasized in extant corporate governance literature. Besides, this paper further raises questions about the effectiveness of independent directors in the boards of textile firms in Bangladesh.

• 인터넷정보학회논문지
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• 제19권1호
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• pp.155-166
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• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

• 지식경영연구
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• 제19권4호
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• pp.59-76
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• 2018

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

• Journal of Korea Trade
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• 제24권8호
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• pp.39-62
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• 2020

Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

• 지능정보연구
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• 제28권3호
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• pp.161-183
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• 2022

여론조사는 유권자들의 투표행위를 예측하고, 그 행위에 영향을 준다는 점에서 선거운동의 강력한 수단이자, 언론의 가장 중요한 기사거리로 자리잡고 있다. 하지만, 여론조사가 활발할수록 후보자들의 공약과 정책을 검증하기 보다 당선 가능성이나 지지도에 관한 조사만 반복적으로 실시하는 등 선거 캠페인에 관한 효과 측정에서 유권자들의 마음을 제대로 반영하지 못하는 경우가 많다. 여론조사의 선거 결과에 대한 부실한 예측이 언론사의 권위를 실추시켰다 하더라도, 어느 후보가 최종 승리할지에 대해 인간의 본능적인 궁금증을 풀어줄 명백한 대안이 없기 때문에 사람들은 여론조사에 대한 관심을 쉽게 놓지 못한다. 이에, 온라인 빅데이터를 통해 인사이트를 발굴하는 환경을 제공하는 썸트렌드의 '유튜브 분석' 기능을 활용하여 20대 대선에 대한 여론을 회고적으로 파악해 보고자 한다. 본 연구를 통해 간단한 유튜브 데이터 분석 결과만으로도 실제 여론(혹은 여론조사 결과)에 근접한 결과를 쉽게 도출하고, 성능이 좋은 여론 예측모형을 구축할 수 있음을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.1-7
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• 2022

인공지능 기술이 발달하면서 뉴로사이언스 마이닝(NSM: NeuroScience Mining)과 AI를 접목하려는 시도가 증가하고 있다. 나아가 NSM은 뉴로사이언스와 비즈니스 애널리틱스의 결합으로 인해 연구범위가 확장되고 있다. 본 연구에서는 fNIRS 실험을 통해 확보한 뉴로 데이터를 분석하여 비즈니스 문제 해결 창의성(BPSC: business problem-solving creativity)을 예측하고 이를 통해 NSM의 잠재력을 조사한다. BPSC는 비즈니스에서 차별성을 가지게 하는 중요한 요소이지만, 인지적 자원의 하나인 BPSC의 측정 및 예측에는 한계가 존재한다. 본 논문에서는 BPSC 예측 성능을 높이는 방안으로 CNN, BiLSTM 그리고 어텐션 네트워크를 결합한 새로운 NSM 기법을 제안한다. 제안된 NSM 기법을 15만 개 이상의 fNIRS 데이터를 활용하여 유효성을 입증하였다. 연구 결과, 본 논문에서 제안하는 NSM 방법이 벤치마킹한 알고리즘(CNN, BiLSTM)에 비하여 우수한 성능을 가지는 것으로 나타났다.