• 지능정보연구
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• 제27권3호
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• pp.231-252
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• 2021

인공지능(Artificial Intelligence)은 미래를 가장 크게 변화시킬 핵심 동력으로 산업 전반과 개인의 일상생활에 다양한 형태로 영향을 미치고 있다. 무엇보다 활용 가능한 데이터가 증가함에 따라 더욱더 많은 기업과 개인들이 인공지능 기술을 이용하여 데이터로부터 유용한 정보를 추출하고 이를 의사결정에 활용하고 있다. 인공지능에 관한 기존 연구는 모방 가능한 업무의 자동화에 초점을 두고 있으나, 인간을 배제한 자동화는 장점 못지않게 알고리즘 편향(Algorithms bias)으로 발생되는 오류나 자율성(Autonomy)의 한계점, 그리고 일자리 대체 등 사회적 부작용을 보여주고 있다. 최근 들어, 인간지능의 강화를 위한 증강 지능 (Augmented intelligence)으로서 인간과 인공지능의 협업에 관한 연구가 주목을 받고 있으며 기업도 관심을 가지기 시작하였다. 본 연구는 의사결정을 위해 조언(Advice)을 제공하는 조언자의 유형을 인간, 인공지능, 그리고 인간과 인공지능 협업의 세 가지로 나누고, 조언자의 유형과 의사결정자의 성격 특성이 의사결정에 미치는 영향을 살펴보았다. 311명의 실험자를 대상으로 사진 속 얼굴을 보고 나이를 예측하는 업무를 진행하였으며, 연구 결과 의사결정자가 조언활용을 하려면 먼저 조언의 유용성을 높게 인지하여하는 것으로 나타났다. 또한 의사결정자의 성격 특성이 조언자 유형별로 조언의 유용성을 인지하고 조언을 활용하는 데에 미치는 영향을 살펴본 결과, 인간과 인공지능의 협업 형태인 경우 의사결정자의 성격 특성에 무관하게 조언의 유용성을 더 높게 인지하고 적극적으로 조언을 활용하는 것으로 나타났다. 인공지능 단독으로 활용될 경우에는 성격 특성 중 성실성과 외향성이 강하고 신경증이 낮은 의사결정자가 조언의 유용성을 더 높게 인지하고 조언을 활용하는 것으로 나타났다. 본 연구는 인공지능의 역할을 의사결정과 판단(Decision Making and Judgment) 연구 분야의 조언자의 역할로 보고 관련 연구를 확장하였다는데 학문적 의의가 있으며, 기업이 인공지능 활용 역량을 제고하기 위해 고려해야 할 점들을 제시하였다는데 실무적 의의가 있다.

• 한국멀티미디어학회논문지
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• 제25권9호
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• pp.1367-1374
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• 2022

In this paper, we propose an automatic cosmetic foundation recommendation system that suggests a good foundation product based on the user's skin color. The proposed system receives and preprocesses user images and detects skin color with OpenCV and machine learning algorithms. The system then compares the performance of the training model using XGBoost, Gradient Boost, Random Forest, and Adaptive Boost (AdaBoost), based on 550 datasets collected as essential bestsellers in the United States. Based on the comparison results, this paper implements a recommendation system using the highest performing machine learning model. As a result of the experiment, our system can effectively recommend a suitable skin color foundation. Thus, our system model is 98% accurate. Furthermore, our system can reduce the selection trials of foundations against the user's skin color. It can also save time in selecting foundations.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.57-66
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• 2022

Claims and disputes are major causes of cost and schedule overruns in the construction business. In order to manage claims and disputes effectively, it is necessary to analyze various types of contract documents punctually and accurately. Since volume of such documents is so vast, analyzing them in a timely manner is practically very challenging. Recently developed approaches such as artificial intelligence (AI), machine learning algorithms, and natural language processing (NLP) have been applied to various topics in the field of construction contract and claim management. Based on the systematic literature review, this paper analyzed the goals, methodologies, and application results of such approaches. AI methods applied to construction contract management are classified into several categories. This study identified possibilities and limitations of the application of such approaches. This study contributes to providing the directions for how such approaches should be applied to contract management for future studies, which will eventually lead to more effective management of claims and disputes.

• Journal of Digestive Cancer Research
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• 제11권3호
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• pp.130-140
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• 2023

Gastric cancer (GC) is one of the most common malignant tumors worldwide, with a 5-year survival rate of < 40%. The diagnosis and treatment decisions of GC rely on human experts' judgments on medical images; therefore, the accuracy can be hindered by image condition, objective criterion, limited experience, and interobserver discrepancy. In recent years, several applications of artificial intelligence (AI) have emerged in the GC field based on improvement of computational power and deep learning algorithms. AI can support various clinical practices in endoscopic examination, pathologic confirmation, radiologic staging, and prognosis prediction. This review has systematically summarized the current status of AI applications after a comprehensive literature search. Although the current approaches are challenged by data scarcity and poor interpretability, future directions of this field are likely to overcome the risk and enhance their accuracy and applicability in clinical practice.

• Clinical Endoscopy
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• 제55권4호
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• pp.473-479
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• 2022

Over the last few years, capsule endoscopy has been established as a fundamental device in the practicing gastroenterologist's toolbox. Its utilization in diagnostic algorithms for suspected small bowel bleeding, Crohn's disease, and small bowel tumors has been approved by several guidelines. The advent of double-balloon enteroscopy has significantly increased the therapeutic possibilities and release of multiple devices (single-balloon enteroscopy and spiral enteroscopy) aimed at improving the performance of small bowel enteroscopy. Recently, some important innovations have appeared in the small bowel endoscopy scene, providing further improvement to its evolution. Artificial intelligence in capsule endoscopy should increase diagnostic accuracy and reading efficiency, and the introduction of motorized spiral enteroscopy into clinical practice could also improve the therapeutic yield. This review focuses on the most recent studies on artificial-intelligence-assisted capsule endoscopy and motorized spiral enteroscopy.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권8호
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• pp.471-481
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• 2003

The acts of biological immune system are similar to the navigation for autonomous mobile robots under dynamically changing environments. In recent years, many researchers have studied navigation algorithms using artificial immune networks. Conventional artificial immune algorithms consist of an obstacle-avoidance behavior and a goal-reaching behavior. To select a proper action, the navigation algorithm should combine the obstacle-avoidance behavior with the goal-reaching behavior. In this paper, the neural network is employed to combine the behaviors. The neural network is trained with the surrounding information. the outputs of the neural network are proper combinational weights of the behaviors in real-time. Also, a velocity control algorithm is constructed with the artificial immune network. Through a simulation study and experimental results for a autonomous mobile robot, we have shown the validity of the proposed navigation algorithm.

• 한국인공지능학회지
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• 제10권1호
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• pp.15-19
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• 2022

This study is to understand the relationship between turnover and various conditions. Turnover refers to workers moving from one company to another, which exists in various ways and forms. Currently, a large number of workers are considering many turnover rates to satisfy their income levels, distance between work and residence, and age. In addition, they consider changing jobs a lot depending on the type of work, the decision-making ability of workers, and the level of education. The company needs to accept the conditions required by workers so that competent workers can work for a long time and predict what measures should be taken to convert them into long-term workers. The study was conducted because it was necessary to predict what conditions workers must meet in order to become long-term workers by comparing various conditions and turnover using regression and decision trees. It used Microsoft Azure machines to produce results, and it found that among the various conditions, it looked for different items for long-term work. Various methods were attempted in conducting the research, and among them, suitable algorithms adopted algorithms that classify various kinds of algorithms and derive results, and among them, two decision tree algorithms were used to derive results.

• 한국컴퓨터정보학회논문지
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• 제26권1호
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• pp.37-44
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• 2021

이 연구는 대학생들의 인공지능 소양을 신장하기 위한 교육 콘텐츠의 설계와 개발에 관한 것이다. 우선 인공지능 교육 콘텐츠를 설계하고 교육 프로그램을 구성하였다. 콘텐츠는 8개의 인공지능 영역에서 총 15차시로 구성되었다. 콘텐츠는 지식-기능-태도의 내용을 함께 담고 있으며 학습단계는 5단계로 구성하였다. 콘텐츠의 개발은 온라인 자료의 형태로 구성하고 시뮬레이션과 워크시트를 포함하였다. 또한 교수학습방법을 제공하고 각 콘텐츠별로 평가 문항을 개발하였다. 콘텐츠의 적합성을 살펴보기 위해 전문가 대상으로 타당도 검사를 실시하였다. 설계 내용에 대한 내용타당도 검사 결과 전체 평균은 .71이상을 나타냈고, 개발된 콘텐츠의 수업 적합도의 CVI값은 .82로 타당성이 높게 나왔다. 본 연구에서 개발된 콘텐츠들이 대학 교양교육에서 인공지능 소양을 향상시키기 위한 효과적인 프로그램으로 활용될 것으로 기대된다.

• Asia pacific journal of information systems
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• 제30권1호
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• Advances in concrete construction
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• 제9권3호
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• pp.257-265
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• 2020

Many failures of concrete structures are related to steel corrosion. For this reason, it is important to recognize how the carbonation can affect the durability of reinforced concrete structures. The repeatability of the carbonation depth measure in a specimen of concrete sprayed with a phenolphthalein solution is consistently low whereby it is necessary to have an impartial method to measure the carbonation depth. This study presents two automatic algorithms to detect the non-carbonated zone in concrete specimens. The first algorithm is based solely on digital processing image (DPI), mainly morphological and threshold techniques. The second algorithm is based on artificial intelligence, more specifically on an array of Kohonen networks, but also using some DPI techniques to refine the results. Moreover, another algorithm was developed with the purpose of measure the carbonation depth from the image obtained previously.