The number of new students at many domestic universities is declining. In particular, private universities, which are highly dependent on tuition, are experiencing a crisis of existence. Amid the declining school-age population, universities are striving to fill new students by improving the quality of education and increasing the student employment rate. Recently, there is an increasing number of cases of using the accumulated big data of universities to prepare measures to fill new students. A representative example of this is the analysis of factors that affect student employment. Existing employment-influencing factor analysis studies have applied quantitative models such as regression analysis to university big data. However, since the factors affecting employment differ by major, it is necessary to reflect this. In this paper, the factors affecting employment by major are analyzed using the data of University C and the decision tree model. In addition, based on the analysis results, a roadmap for student employment by major is recommended. As a result of the experiment, four decision tree models were constructed for each major, and factors affecting employment by major and roadmap were derived.

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

Internet of Things is an infrastructure of the interconnected devices. In Internet of Things environment, many smart devices are used in daily life. It requires a new retrieval method using multiple devices. We propose an information retrieval method using both device characteristics and device usage characteristics in multi-device environments. Firstly, information retrieval is performed using a general purpose device. And then, it is performed using dedicated devices. Our method uses both characteristics of the devices and usage characteristics of them. Moreover, it considers queries on the general purpose device. This paper proposes a new retrieval method and describes algorithms. Then, it presents smart home scenarios. Performance evaluation is performed using the scenarios. The evaluation results show higher precision and efficiency than previous researches. The proposed method gets information more accurately and quickly in IOT multiple device environments.

Highly informative heuristics in AI planning can help to a more efficient search a solutions. However, in general, to obtain informative heuristics from planning problem specifications requires a lot of computational effort. To address this problem, we propose a Partial Planning Graph(PPG) and Mixed Heuristics for solving planning problems more efficiently. The PPG is an improved graph to be applied to can find a partial heuristic value for each goal condition from the relaxed planning graph which is a means to get heuristics to solve planning problems. Mixed Heuristics using PPG requires size of each graph is relatively small and less computational effort as a partial plan generated for each goal condition compared to the existing planning graph. Mixed Heuristics using PPG can find partial interactions for each goal conditions in an effective way, then consider them in order to estimate the goal state heuristics. Therefore Mixed Heuristics can not only find interactions for each goal conditions more less computational effort, but also have high accuracy of heuristics than the existing max and additive heuristics. In this paper, we present the PPG and the algorithm for computing Mixed Heuristics, and then explain analysis to accuracy and the efficiency of the Mixed Heuristics.

This study aims to examine the effect of the Mindfulness for YouTube users. Recently, video sharing platforms have grown rapidly with YouTube, and YouTube has grown rapidly despite the emergence of various video sharing platforms. Which YouTube represents these changes, provides an environment where anyone can make and distribute videos. And through these advantages, many viewers are coming in and more creators are making videos. The purpose of this study is to understand the influence factors of YouTube service in Korea and to clarify the causal relationship between the factors that affect the continued use intention through usefulness, confirmation, and satisfaction. For this reason, presented a research model that applied key variables of the Mindfulness theory, which emphasized that users should be aware of their emotions. In order to empirical verification the research model of this study, a questionnaire was conducted for university students and general office workers who had experience using YouTube. As a result, first decentered attention has been shown that has no positive effect on perceived usefulness and confirmation. Second, non-judgmental acceptance has benn shown that has a postitive effect on perceived usefulness and confirmation. Third, current self-perception has been shown that has no positive effect on perceived usefulness and confirmation. Fourth, concentration has been shown that has no positive effect on perceived usefulness and confirmation. And Mindfulness has been shown that has a positive effect on satisfaction and perceived usefulness. Through this, it is important to use YouTube to use what users feel is important, and it is important to determine whether the video is helpful to them when accepting content and make them expect the next video.

The appearance of education is also rapidly changing in social changes represented by social networks. And the development of information and communication technology is also having a widespread effect on the education field. In the era of untact caused by Covid-19, education through smart learning is having a greater effect on students as well as adult learners more quickly and broadly. In addition, smart learning is not just limited to learning content, but is developing into personalized, convergence, and intelligent. The purpose of this study is to identify the factors of ARCS motivation theory that can determine the learning motivation of smart learning users, and to empirically study the casual relationship between these factors on education achievement through practical value and hedonic value. Specifically, I would like to examine how the independent variables ARCS motivation factors (attention, relevance, confidence, and satisfaction) affect learners' education achievement through the parameters of practical value and hedonic value. To this end, a research model was presented that applied the main variables of attention, relevance, confidence, and satisfaction, which are four elements of ARCS motivation theory, a specific and systematic motivational strategy to induce and maintain learners' motivation. In order to empirically verify the research model of this study, a survey was carried out on learners with experience using smart learning. As a result of the study, first attention was found to have a positive effect on the hedonic value. Second, relevance was found to have a positive effect on the hedonic value. Third, it was found that confidence did not have a positive effect on the practical value and the hedonic value. Forth, satisfaction was found to have a positive effect on the practical value and the hedonic value. Fifth, practical value was found to have a positive effect on the education achievement. Sixth, hedonic value was found to have a positive effect on the education achievement. Through this, it can be seen that the intrinsic motivation of learners using smart learning affects the education achievement of users through intrinsic and extrinsic value. A variety of smart learning that combines advanced IT technologies such as AI and big data can contribute to improving learners' education achievement more effectively and efficiently. Furthermore, it can contribute a lot to social development.

Entrepreneurship is recognized as an essential education in start-up education. However, domestic studies on entrepreneurship in developing countries are extremely rare. The purpose of this study is to compare the importance of entrepreneurship and the perception of implementation of Korean and Nepalese farmers, and to seek a start-up education strategy based on the results. As a result of the study, first, there was no difference in the importance of entrepreneurship in the 6th industry between two countries, but the degree of entrepreneurial spirit performance was high in Nepalese farmers. Second, in terms of the difference between the importance and execution of the 6th industry entrepreneurship, Korea had lower performance than importance in all factors, and Nepal had higher importance than implementation in innovation, initiative, cooperation, and networking capability. Third, in the difference between the importance and the degree of implementation in each country, the level of implementation was uniformly lower than that of importance in Korea, but the height of the Nepalese farmers was not uniform. The results of this study, which studied entrepreneurship in Korea and Nepal, contribute greatly in terms of promoting the national economy by creating new added values.

The purpose of this study is to examine the organizational politics, resistance change, turnover intention, and organizational commitment of organizational members during mergers and acquisitions. Recently, many companies are interested in mergers and acquisitions for business diversification and market extension. A merger is a legal consolidation of two entities into one, whereas an acquisition occurs when one entity takes ownership of another entity's stock, equity interests, or assets. This research model establishes a structural equation model. This model is set in a causal relationship between manager's organizational politics, peer organizational politics, and change resistance and the change resistance has a causal relationship of turnover and tissue immersion. In particular, this study will test different of organizational politics by position. Research results, the organizational politics of managers and colleagues have shown increasing change resistance. The change resistance has resulted in a reduction of organizational commitment and an increase of turnover intention. Next, the position analysis showed that top management level, middle management level, and working-level officials showed different organizational politics. The working-level officials are influenced by their manager politics and are influenced in organizational commitment and turnover intention by change resistance. The middle manager level is influenced by the organizational politics of bosses and colleagues, and organizational commitment is weakened by change resistance. The CEO level is not affected by organizational politics in the company, but the turnover intention is strengthened and the organizational commitment is weakened by the change resistance. This study has contributed to further updating the theory of organizational politics based on mergers and acquisitions. As a practical implication, we suggest an organizational integration strategy for a new organization.

The purpose of this study is to explore ways to improve the quality of real-time video lectures in a non-face-to-face environment using IPA (Importance-Performance Analysis). Recently, due to the impact of COVID-19 in universities, all remote classes are being implemented, so research is needed to raise learner awareness. Accordingly, factor analysis, mean analysis, correspondence analysis, and IPA analysis were performed based on the data of 632 students who responded from March 21 to June 30, 2021 for learners of K University in Chungbuk. First, overall satisfaction was low compared to importance, and the difference in system perception was the largest. Second, the difference in learner perception of real-time video lectures through the IPA matrix showed that the system error and screen cutoff were the largest. Third, the difficulty of lecture content, task and test feedback, etc. are classified. Accordingly, the satisfaction of real-time video lectures in non-face-to-face environments is low, suggesting that school-level support for quality improvement to improve learner satisfaction in non-face-to-face environments and the role of instructors are needed to improve learners' academic achievement.