Abstract In the past, the emergency responses to disasters such as fire outbreak accidents, accidents that require first aid were slow and not optimal. With human intellect, it was impractical to analyze vast amounts of data regarding the continuity of the numerous environmental changes and the correlation there may be with emergency responses based on past experiences with similar situations. Today, artificial intelligence is presented as a powerful tool to various organizations. Many have already made various attempts to apply this technology as an advisor for emergency response. This research expands on the practicality and effectiveness of utilizing AI as an advisory platform for disaster response based on the big‐data, and also it designs an AI advisor platform for disaster response with big data‐based algorithms. Finally AI advisor function are defined as part of the AI advisor platform, the voice recognition function, natural language processing function, big data coordination function.

Summary Academic discussions on cultural intelligence (CQ) are now paying attention to their potential utilization from various angles. The field of study is expanded not only in business administration but also in psychology, education, tourism, communication, and arts. This is due to the widespread study of global communication competence in multicultural situations because of the deepening of globalization. In this paper, we try to find a way to utilize cultural intelligence model proposed by David Livermore. The aim is to develop education contents for the improvement of cultural intelligence of university students. The target is limited to university students and aims to develop education contents to enhance their cultural intelligence. The main purpose of the study was to measure and analyze the cultural intelligence of university students. For that, the level of cultural intelligence of Korean university freshmen was measured and analyzed. The individual level of the four areas constituting the cultural intelligence was identified, and the difference between the male and female was examined. At the same time, the differences in cultural intelligence were analyzed according to the duration of multicultural contact and experience in the case of foreign language lectures taught by foreigners. Finally, we analyzed how the correlation between the four areas that comprise cultural intelligence is occurring, and as a result, the content and results of this study are expected to be an important foundation for the direction of future development of education contents in universities.

Summary Evaluation information generated by various users is processed using various requirements and data to make recommendations for solving the problems, and it analyzes satisfaction with the results. Despite people normally utilizes the processed information for decision making, not all information, however, brings positive outcomes to users. There are some users who perceived it negatively. In order to minimize the occurrence of such negative effects, the analysis of various user requirements is essential as well as diversifying user inputs for each requirement. Consequently, the results from individual inputs must be predicted. In the past, since the system relies on a single‐expert system, it is necessary to accept and process various limitations of recommendation and multiple requirements. Therefore, the results of the recommendation also have various problems. In order to solve this problem, this study applied an analytic hierarchy process to multiadvisor configuration. In the proposed system, one or multiple advisors are defined, and after analyzing the predefined requirements, the system accepts only the requirements that can be processed and calculates the individual recommendation results. A recommendation system was going to be studied by learning all situation.

Summary Each person has unique bio‐information, and this information rarely would be overlapped to other people. Because of this feature, many researchers have been working on a user's identification. However, the problem is that it is not certain if the feature is exactly matched at any time and in any place for the dynamic signal. It is very hard to match bio‐information whenever it is measured; however, all natural signals have an individual pattern. In this paper, it uses an ECG (electrocardiogram) as bio‐information, and it tries to find each pattern, which will be located within the threshold. With the pattern in the threshold, it detects the user's identification. To analyze the patterns, it analyzes them as measured for 120 seconds. Next, it divides them every 1‐2 seconds to 5 seconds. Then, it could recognize the users' identification with this study, and then, finally, the accuracy is 83.3618%.