• 벤처창업연구
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• 제9권1호
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• pp.15-23
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• 2014

본 논문은 경영환경에 대한 불확실성 증대, 데이터 없는 최초 상황 발생, 합리적 의사결정이 불가능할 경우 등 기업환경이 변화할 때, 벤처 중소기업 경영자들은 의사결정 시 직관을 어떻게 활용하는가에 관한 사례연구다. 논문은 문헌 연구와 벤처 중소기업 경영자 16명을 심층 인터뷰하여 자료를 수집하였고, Klein, G, 의 "포괄적(generic) 멘탈 시뮬레이션 모델"로 분석하였다. 연구에 사용된 직관은 본인의 경험을 사용하는 전문가(expert) 직관과 타인의 경험도 활용하는 전략적(strategic) 직관으로 분류하였다. 사례연구 결과 경영자들은 전문가 직관과 전략적 직관을 다르게 활용하고 있었다. 전문가 직관의 특징은 별 노력 없이도 빠르게 작동하는데, 반해 전략적 직관은 시간이 많이 소요된다. 또 다른 특징은 전문가적 직관은 이미 발생된 일에 대한 의사결정에 많이 사용되고, 전략적 직관은 미래형 의사결정에 많이 활용되었다. 전략적 직관의 프로세스는 (1)전략적 관심단계 (2)매개물 발견단계 (3)1차 멘탈 시뮬레이션 단계 (4)핵심 매개변수 띄우기 단계, (4)2차 멘탈 시뮬레이션 단계 (5)내부 평가 단계 (6) 의사결정 단계를 거친다. 위에 단계를 모델링하여 벤처 중소기업 경영자들의 "전략적 직관에 의한 의사결정 모형(Strategic intuition decision-making model)"을 도출하였다. 사례분석 결과 나타난 중요한 결과는 첫째, 성공하는 의사결정 시 성공과 실패의 중요한 차이는 의사결정 프로세스 중 "2차 멘탈 시뮬레이션"에서 결정되었고, 둘째, 전문가 직관이 전략적 직관보다 많아질수록 경영의 어려움에 봉착하였으며. 셋째, 전략적 직관은 학습이 가능하다.

• 음성과학
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• 제7권4호
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• pp.49-57
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• 2000

This paper proposes an effective decision method focused on evaluating the 'stress position'. Conventional methods usually extract the acoustic parameters and compare them to references in absolute scale, adversely producing unstable results as testing conditions change. To cope with environmental dependency, the proposed method is designed to be model-based and determines the stressed interval by making relative comparison over candidates. The stressed/unstressed models are then induced from normal phone models by adaptive training. The experimental results indicate that the proposed method is promising, and that it is useful for automatic detection of stress positions. The results also show that generating the stressed/unstressed model by adaptive training is effective.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.58-65
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• 2023

Bankruptcy is a significant risk for start-up companies, but with the help of cutting-edge artificial intelligence technology, we can now predict bankruptcy with detailed explanations. In this paper, we implemented the Category Boosting algorithm following data cleaning and editing using OpenRefine. We further explained our model using the Shapash library, incorporating domain knowledge. By leveraging the 5C's credit domain knowledge, financial analysts in banks or investors can utilize the detailed results provided by our model to enhance their decision-making processes, even without extensive knowledge about AI. This empowers investors to identify potential bankruptcy risks in their business models, enabling them to make necessary improvements or reconsider their ventures before proceeding. As a result, our model serves as a "glass-box" model, allowing end-users to understand which specific financial indicators contribute to the prediction of bankruptcy. This transparency enhances trust and provides valuable insights for decision-makers in mitigating bankruptcy risks.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.243-246
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields Classifying a group into subgroups is one of the most important subjects in data mining Tree-based methods, known as decision trees, provide an efficient way to finding classification models. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variables should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present several methods for measuring the node impurity, which are applicable to data sets with multiple target variables. For illustrations, numerical examples are given with discussion.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 봄 학술발표회 논문집
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• pp.274-283
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• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• 한국지능시스템학회논문지
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• 제21권6호
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• pp.730-736
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• 2011

본 연구는 순차적인 시계열 자료들에서 가장 최근의 추세가 반영될 수 있는 패턴분류 모델을 설계하였다. 의사결정을 지원하는 데이터마이닝 패턴분류 모델을 설계할 때 통계 기법과 인공지능 기법을 융합한 모델들이 기존의 모델보다 우수함을 입증하였다. 특히 퍼지이론과 융합된 패턴분류 모델들의 적중률이 상대적으로 더 향상되었다. 예를 들어, 통계적 이론을 기반으로 한 SVM모델과 퍼지소속함수와의 결합, 혹은 신경망과 FCM을 결합한 모델들의 성능이 우수하였다. 실험에서 사용한 패턴분류 모델들은 BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, Regression Analysis 등이다. 그리고 데이터베이스는 시계열 속성을 지닌 금융시장의 경제지표 DB(한국, KOSPI200 데이터베이스)와 병원 응급실의 부정맥환자에 대한 심전도 DB(미국 MIT-BIH 데이터베이스)들을 사용하였다.

• Neurospine
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• 제15권4호
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• Journal of Platform Technology
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• 제11권4호
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• pp.62-70
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• 2023

이 논문은 농수산물 생산자가 도매시장에 상품을 출하하는 시기와 양을 결정하는 것을 돕기 위한 시스템을 구축하기 위한 일련의 과정 중 일부이다. 기존 농수산물 출하모델에서 사용하지 않은 품질변화효과를 모델링하고, 그 통계적 유의성을 확인한 후, 시스템에 도입하는 것이 이 연구의 목적이다. 이를 위해 연구자는 품질변화효과를 측정할 수 있는 네 가지 모델을 개발하였다. 시간이 지남에 따라 1) 품질이 일정하게 떨어지는 경우, 2) 품질이 처음에 급속히 떨어지다가 나중에는 천천히 떨어지는 경우, 3) 품질이 처음에 천천히 떨어지다가 나중에는 급속히 떨어지는 경우, 4) 품질이 낮았다가 시간이 흐른 후 높아지다가 다시 감소하는 경우를 모델링하였다. 각 모델의 품질변화효과가 출하량에 미지는 영향을 2014-2021년 사이에 가락도매시장에서 거래된 사과를 대상으로 실증분석 해 본 결과에 따르면 네 모델 모두 품질변화효과에 유의성을 발견하였다. 그리고 네 모델 간 설명력에 유의한 차이는 없었다. 따라서 네 개 모델 중 어느 하나를 선택해서 사과에 대한 출하시기의사결정시스템에 적용시킬 수 있는 것으로 나타났다.

• 산업공학
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• 제21권4호
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• pp.418-434
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• 2008

This paper studied issues in decision making on the production control policy of a cathode plate manufacturing process in zinc refining plant. The present production system has a long lead time from raw materials (aluminum plate) to products (cathode plate) due to many WIP inventories. Because WIP inventories are stocked at each process and moved from one place to another frequently, they are the main cause of inefficiency in the process. In this paper, to solve this problem, several production control policies have been identified and studied. Several simulation models are used to compare the performances of these production control policies. The output lead time and WIP (Work In Process) of real production system are compared with those of simulation models. PUSH, CONWIP, DBR, KANBAN and CONWIP-DBR models have been used to simulate and review the optimized production control policy that achieves the target output quantities with decreased lead time and WIP. The simulation results of each production control policy show that CONWIP and CONWIP-DBR models are the good production control policy under the present production system. Especially in present production system, CONWIP with one parameter is easier control policy than CONWIP-DBR with two parameters. Therefore CONWIP has been selected as the best optimum production control policy. With CONWIP, lead time has been reduced by 97% (from 6,653 to 187 minute) and WIP has been reduced from 1,488 to 53, compared to the present system.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.