• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.135-142
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• 2019

Given the ability and skills required by modern people, marketing can be divided into knowledge-related skill such as marketing plans, market segmentation, and marketing mix management and supportive skill such as communication, inter-organizational management, creativity, and decision making. Knowledge related skills can be nurtured in existing marketing classes, but it is recognized that special educational programs such as self marketing are needed to develop and train supportive skills regardless of education levels or major education. This paper is aimed to design for marketing educational program for the self marketing. In this study, a DACUM method job analysis to extract contents by specialists such as model setting of task and job, job statement, job analysis, education course development, and so on. In the first place, this report presents job analysis model by procedures for developing selection criteria of examination questions of the self marketing qualification. The first step is preparation for job analysis, the second step: the establishment of job models, the third step : the job specification and task analysis, the fourth step: the review of job model, the fifth step: the establishment of subjects for examination matrix table for making questions.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.605-618
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• 2021

The use of human resources determines the success of enterprises. This study applies the questionnaire design method to analyze the relationship between job stress, job satisfaction, and job performance, noting that few studies have comparatively examined these variables between industries, especially between high-tech and traditional industries. The proposed assessment model in this study can facilitate decision-makers' ability to make the optimal business decisions through their personnel systems, thereby improving employee satisfaction and increasing job performance. This study found that in the traditional and high-tech industries, some demographic variables have significant differences in the job stress, job satisfaction and job performance, but the demographic variables that can significantly affect the differences in these job's variables are differences between industries. This study acknowledges that job stress and performance have a significantly negative correlation, and traditional industries will have more stress factors than high-tech industries. In addition, support for traditional industries exist in job satisfaction and performance has a significantly positive correlation, but not in high-tech industries. Job stress for performance has a significantly negative correlation in two industries. This study reconfirmed the relationship between job stress, satisfaction and performance, found some differences in this relationship and the respective industrial characteristics.

• 융합경영연구
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• 제10권5호
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• pp.1-6
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• 2022

Purpose: To overcome the question that depends too much on expert's subjective judgment in traditional risk identification, this paper structure the multilevel generalized fuzzy comprehensive evaluation mathematics model of the risk identification of project, to research the risk identification of the project. Research design, data and methodology: This paper constructs the multilevel generalized fuzzy comprehensive evaluation mathematics model. Through iterative algorithm of AHP analysis, make sure the important degree of the sub project in risk analysis, then combine expert's subjective judgment with objective quantitative analysis, and distinguish the risk through identification models. Meanwhile, the concrete method of multilevel generalized fuzzy comprehensive evaluation is probed. Using the index weights to analyse project risks is discussed in detail. Results: The improved fuzzy comprehensive evaluation algorithm is proposed in the paper, at first the method of fuzzy sets core is used to optimize the fuzzy relation matrix. It improves the capability of the algorithm. Then, the method of entropy weight is used to establish weight vectors. This makes the computation process fair and open. And thereby, the uncertainty of the evaluation result brought by the subjectivity can be avoided effectively and the evaluation result becomes more objective and more reasonable. Conclusions: In this paper, we use an improved fuzzy comprehensive evaluation method to evaluate a railroad engineering project risk. It can give a more reliable result for a reference of decision making.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.95-99
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• 2023

Recently Machine Learning has been considered as one of the active research areas of Computer Science. The various Artificial Intelligence techniques are used to solve the classification problems of environmental sciences, biological sciences, and medical sciences etc. Due to the heterogynous and malfunctioning weather sensors a considerable amount of noisy data with missing is generated, which is alarming situation for weather prediction stockholders. Filling of these missing values with proper method is really one of the significant problems. The data must be cleaned before applying prediction model to collect more precise & accurate results. In order to solve all above stated problems, this research proposes a novel weather forecasting system which consists upon two steps. The first step will prepare data by reducing the noise; whereas a decision model is constructed at second step using regression algorithm. The Confusion Matrix will be used to evaluation the proposed classifier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.980-998
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• 2023

In Low Earth Orbit (LEO) satellite networks, satellites operate fast and the inter-satellite link change period is short. In order to sense the spectrum state in LEO satellite networks in real-time, a space-based satellite network intelligent spectrum sensing algorithm based on artificial neural network (ANN) is proposed, while Geosynchronous Earth Orbit (GEO) satellites are introduced to make fast and effective judgments on the spectrum state of LEO satellites by using their stronger arithmetic power. Firstly, the visibility constraints between LEO satellites and GEO satellites are analyzed to derive the inter-satellite link building matrix and complete the inter-satellite link situational awareness. Secondly, an ANN-based energy detection (ANN-ED) algorithm is proposed based on the traditional energy detection algorithm and artificial neural network. The ANN module is used to determine the spectrum state and optimize the traditional energy detection algorithm. GEO satellites are used to fuse the information sensed by LEO satellites and then give the spectrum decision, thereby realizing the inter-satellite spectrum state sensing. Finally, the sensing quality is evaluated by the analysis of sensing delay and sensing energy consumption. The simulation results show that our proposed algorithm has lower complexity, the sensing delay and sensing energy consumption compared with the traditional energy detection method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• 융합경영연구
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• 제4권2호
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• pp.1-19
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• 2016

The purpose of this study was to examine the issues involved in offering superior customer experience on fashion retail stores in Brazil. The approach used to access CEM (Customer Experience Management) issues was a special questionnaire with 23 questions, through a research with managers of three important brazilian fashion retail chains (focused on class A clients). Some statistical techniques were used for data processing. It was possible to analyze the aspects that impact on the customer experience and their relevance. it was possible to realize that CEM is effective in increasing productivity and, so, it can be used as a guideline matrix management in decision making to promote superior customer experiences. The classical management is usually conservative and avoids to deal with strategies that do not necessarily involve numbers. Dealing with intangible and so subtle experience is unusual and a huge challenge, but sometimes it is necessary to look beyond the obvious and accessible statistics. If CEM is a strategy to focus on operations and processes of a business around the customers experiences with the company, it is essential to structure it and find out its effectiveness.

• 한국식생활문화학회지
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• 제21권3호
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• pp.270-279
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• 2006

This study was designed to : (a) analyze the menus of the French restaurant in tourism hotel using the menu analysis techniques of Kasavana & Smith and Pavesic, (b) compare the characteristics of the two analysis techniques. The calculations for the menu analysis were done using the MS 2000 Excel spreadsheet program. The menu mix % and unit contribution margin were used as variables by Kasavana & Smith and weighted contribution margins (WCM) and potential food cost % (PFC%) by Pavesic. In two cases, a four-cell matrix was created and menu items were located in each according they achieved high or low scores with respect to two variables. The items that scored favorably on both variables were rated in the top category (e.g., star, prime) and those that scored below average on both were rated in the lowest category (e.g., dog, problem). While Kasavana & Smith's method focused on customer's viewpoints, Pavesic's method considered the manager's viewpoints. Therefore, it is more likely to be desirable for decision-making on menus if the menu analysis techniques chosen is suited to its purpose.

• 지능정보연구
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• 제22권3호
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• pp.129-142
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• 2016

전자상거래에서 소비자들의 구매 의사결정에 판매 제품을 이미 구매하여 사용한 고객의 리뷰가 중요한 영향을 미치고 있다. 전자상거래 업체들은 고객들이 제품 리뷰를 남기도록 유도하고 있으며, 구매고객들도 적극적으로 자신의 경험을 공유하고 있다. 한 제품에 대한 고객 리뷰가 너무 많아져서 구매하려는 제품의 모든 리뷰를 읽고 제품의 장단점을 파악하는 것은 무척 힘든 일이 되었다. 전자상거래 업체들과 연구자들은 텍스트 마이닝을 활용하여 리뷰들 중에서 유용한 리뷰들의 속성을 파악하거나 유용한 리뷰와 유용하지 않은 리뷰를 미리 분류하는 노력을 수행하고 있다. 고객들에게 유용한 리뷰를 필터링하여 전달하는 방안이다. 본 연구에서는 문서-단어 매트릭스에서 단어의 제거 기준으로 온라인 고객 리뷰가 유용한 지, 그렇지 않은지를 구분하는 문제에서 단어들이 유용 리뷰 집합과 유용하지 않은 리뷰집합에 중복하여 등장하는 정도를 측정한 중립도를 제시한다. 제시한 중립도를 희소성과 함께 분석에 활용하여 제거할 단어를 선정한 후에 각 분류 알고리즘의 성과를 비교하였다. 최적의 성과를 보이는 중립도를 찾았으며, 희소성과 중립도에 따라 단어를 선택적으로 제거하였다. 실험은 Amazon.com의 'Cellphones & Accessories', 'Movies & TV program', 'Automotive', 'CDs & Vinyl', 'Clothing, Shoes & Jewelry' 제품 분야 고객 리뷰와 사용자들의 리뷰에 대한 평가를 활용하였다. 전체 득표의 수가 4개 이상인 리뷰 중에서 제품 카테고리 별로 유용하다고 판단되는 1,500개의 리뷰와 유용하지 않다고 판단되는 1,500개의 리뷰를 무작위로 추출하여 연구에 사용하였다. 데이터 집합에 따라 정확도 개선 정도가 상이하며, F-measure 기준으로는 두 알고리즘에서 모두 희소성과 중립도에 기반하여 단어를 제거하는 방안이 더 성과가 높았다. 하지만 Information Gain 알고리즘에서는 Recall 기준으로는 5개 제품 카테고리 데이터에서 언제나 희소성만을 기준으로 단어를 제거하는 방안의 성과가 높았으며, SVM에서는 전체 단어를 활용하는 방안이 Precision 기준으로 성과가 더 높았다. 따라서, 활용하는 알고리즘과 분석 목적에 따라서 단어 제거 방안을 고려하는 것이 필요하다.

• 산업경영시스템학회지
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• 제44권3호
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• pp.176-191
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• 2021

Non-face-to-face lectures have become a necessity rather than an option since COVID-19, and in order to improve the quality of university education, it is necessary to explore the properties of non-face-to-face lectures and make active efforts to improve them. This study, focusing on this, aims to provide basic data necessary for decision-making for non-face-to-face lecture design by analyzing the relative importance and execution satisfaction of non-face-to-face lecture attributes for professors and students. Based on previous research, a questionnaire was constructed by deriving 4 factors from 1st layer and 17 from 2nd layer attributes of non-face-to-face lectures. A total of 180 valid samples were used for analysis, including 60 professors and 120 students. The importance of the non-face-to-face lecture properties was calculated by obtaining the weights for each stratified element through AHP(Analytic Hierachy Process) analysis, and performance satisfaction was calculated through statistical analysis based on the Likert 5-point scale. As a result of the AHP analysis, both the professor group and the student group had the same priority for the first tier factors, but there was a difference in the priorities between the second tier factors, so it seems necessary to discuss this. As a result of the IPA(Importance Performance Analysis) analysis, the professor group selected the level of interaction as an area to focus on, and it was confirmed that research and investment in teaching methods for smooth interaction are necessary. The student group was able to confirm that it is urgent to improve and invest in the current situation so that the system can be operated stably by selecting the system stability. This study uses AHP analysis for professors and students groups to derive relative importance and priority, and calculates the IPA matrix using IPA analysis to establish the basis for decision-making on future face-to-face and non-face-to-face lecture design and revision. It is meaningful that it was presented.