• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• 대한공업교육학회지
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• v.34 no.2
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• pp.103-127
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• 2009

The purpose of this study is to offer the basic data regarding the problems of the employment training activities and their solutions by way of the research and analysis of the awareness of career development of university students and their preparation for employment opportunities. The results of the study are as follows. First, it is necessary that the students themselves make plans for future jobs and their preparation for them, from the start of their university work. This includes taking employment preparation courses as liberal arts requirements. It also needs to have a systematic association with some organizations such as employment preparation centers. Second, it is necessary that the career portfolios of university students be accepted as materials for objective evaluation so that the companies use them at the time of hiring new employees. If those materials are stored and managed in a database even after their graduation, they will be the strong foundation for the competitive power of the university.Third, it is necessary that university students establish the orientation of employment training in advance, according to their personal and disciplinary possibilities by diagnosing the level of basic employment ability they possess and that they find out the appropriate programs, both personal and disciplinary, to enforce the abilities they need to develop further. Accordingly, it is necessary to have an evaluation system in order to assess student's basic employment abilities, so as to increase the degree of their employment preparation and its support strategy based on the evaluation. Fourth, in the higher education level, university students' lower awareness (M=2.86) of their discipline satisfaction, their major selection, and the university's employment opportunity service shows that it is necessary that there be close connection between learning and work. For short-term purpose, the quantitative and qualitative evaluation must be preceded about the various employment training programs and self-development programs offered by the university. From the long-term perspective, it is urgently necessary that the university ensure the human resources development experts for the purpose of diagnosing employment services within the university.

• Journal of the Korean Society of Food Culture
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• v.18 no.4
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• pp.333-345
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• 2003

We conducted a survey on Japanese women's consciousness of food culture and their traditional food consumption by self filling-out questionnaire during January, 2000 for the period of a month, For the survey we selected 250 women residing in Kyoto, Japan. For the statistic work we used SAS package system, and t-test, $\cal{X}^2-test$ and Duncan's multiple range test were also used to verify the results significance. The purpose of this survey lies in gathering a basic data on the comparative direction of Korean and Japanese women's food culture in the future 1. Comparing the preferred food purchase place, In case of Korean women, traditional market was comparatively more preferred while Japanese women relatively preferred convenience store (p<0.001). 2. In case of Japanese women, they answered there is no difference from ordinary days on New Year's Day (71%) and Christmas (40%) while 38% answered they prepare food at home. 40% said they prepare food on parents-in-law's birthday, and 41% said no difference from ordinary days. 52% said they prepare food at home on husband's birthday. For their own birthday, 32% said yes to preparing food at home while 45% said no difference and 22.3% said eating out. For children's birthday 65% said preparing at home, 16.3% said no difference and 14.9% said eating out. 3. Comparing the conception on traditional food, Korean women answered 'complicated' (77%) most while 'simple' (5%) least, which indicates their demands for simplified recipes. In case of Japanese women, 'complicated' (44%) was most while 'scientific' (6%) was least which indicates their demands for scientific way of recipes. There were differences shown by age (p<0.001) and the older the more said 'simple' or 'logical' (p<0.01). 4. As the reason for the complicity of traditional food recipes, Koreans said 'too many hand skill' (60%) most while 'too many spices' (8%) least. For Japanese, 'various kind of the recipe' (55%) was most while 'too many hand skill' (7%) was least. There were significant differences shown by academic background (p<0.01) and income(p<0.01), and the lower the academic background, the more said 'too many spices' as the reason for the complicity in making traditional food. Generally, the lesser the income, the more tendency to say 'various kinds of the recipe'. 5. In case of Koreans, 'the recipe is difficult' (56%) was high while 'uninterested' (9%) was low in answer which showed differences by academic background (p<0.05), and in case of Japanese, 'no time to cook' (44%) was high while 'uninterested' (7%) was low. 6. The following is the reasons for choosing traditional food as a snack for children. In case of Koreans, they answered as 'traditional food' (34%), 'made from nutrious and quality materials' (27%), 'for education' (22%) and 'suites their taste' (17%) revealing 'traditional food' is highest. In case of Japanese, it was revealed in the order of 'made from nutrious and quality materials' (36.3%), 'traditional food' (25.2%), 'suites their taste' (22.6%), 'for education' (12.8%) and 7. Comparing the most important thing for the popularization of traditional food in the world, Koreans answered 'taste and nutrition' (45%) most while 'shape and color' (6%) least. In case of Japanese, 'taste and nutrition' (75%) was answered most while 'hygienic packaging' (4%) was least. Both considered 'taste and nutrition' as most important thing for the popularization of traditional food in the world. 8. In case of Koreans, they answered they learn how to make traditional food 'from mother' (47%), 'media' (18%), 'school' (15%), 'from mother-in-law' (14%), 'private cooking school' (4%) and 'close acquaintances' (2%). In case of Japanese, they said mostly learn 'from mother', but it was also shown that the lower the academic background the lesser the tendency of learning 'from mother' but 'from school' (p<0.001). 9. About the consumption of traditional fermented food, Koreans said they make kimchi (90%), pickled vegetables (39%), soy sauce (33%), bean paste (38%), salted fishery (12%) and traditional liquors (14%) at home while 67% for salted fishery and 48% for traditional liquors answered they buy rather than making at home. On the other hand, Japanese answered they mostly buy kimchi (60%), soy sauce (96%), bean paste(91%), natto(92%), salt fermented fish foods (77%) and traditional alcoholic beverage (88%) to eat. This difference was shown very distinct between Korean and Japanese women (p<0.001). 10. About the most important thing in food, Koreans answered in the order of 'liking and satisfaction' (33%), 'for health' (32%), 'for relieve hunger' (18%) and 'convenience' (17%). In case of Japanese, it was revealed in the order of 'for health' (61%), 'liking and satisfaction' (20%), 'to relieve hunger' (16%) and 'convenience' (3%). This shows that Japanese women take comparably more importance to health than Korean women. The conception of food was shown different between Korean and Japanese women (p<0.001), and Koreans showed level 4-5 of food culture while Japanese showed level 5.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.