• Title/Summary/Keyword: AI preference

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Web Recommendation Mechanism Based on Case-Based Reasoning and Web Data Mining

  • Kim, Jin-Sung
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2002.12a
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    • pp.443-446
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    • 2002
  • In this research, we suggest a Web-based hybrid recommendation mechanism using CBR (Case-Based Reasoning) and web data mining. Data mining is used as an efficient mechanism in reasoning for relationship between goods, customers' preference and future behavior. CBR systems are normally used in problems for which it is difficult to define rules. We use CBR as an AI tool to recommend the similar purchase case. A Web-log data gathered in real-world Internet shopping mall was given to illustrate the quality of the proposed mechanism. The results showed that the CBR and web data mining-based hybrid recommendation mechanism could reflect both association knowledge and purchase information about our former customers.

Development of a data analysis system for preventing school violence based on AI unsupervised learning (AI 비지도 학습 기반의 학교폭력 예방 데이터 분석 시스템 개발)

  • Jung, Soyeong;Ma, Youngji;Koo, Dukhoi
    • Journal of The Korean Association of Information Education
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    • v.25 no.5
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    • pp.741-750
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    • 2021
  • School violence has long been recognized as a social problem, and various efforts have been made to prevent it. In this study, we propose a system that can prevent school violence by analyzing data on the frequency of conversations between students, friendship and preference to be in the same group. This data was quantified using a Likert scale questionnaire, and also grouped into the appropriate number of clusters using the K-means algorithm. Additionally, the homeroom teacher observed the frequency and nature of conversations between students, and targeted specific individuals or groups for counseling and intervention, with the aim of reducing school violence. Data analysis revealed that the teachers' qualitative observations were consistent with the quantified data based on student questionnaires, and therefore applicable as quantitative data towards the identification and understanding of student relationships within the classroom. The study has potential limitations. The data used is subjective and based on peer evaluations which can be inconsistent as the students may use different criteria to evaluate one another. It is expected that this study will help homeroom teachers in their efforts to prevent school violence by understanding the relationships between students within the classroom.

Game Elements Balancing using Deep Learning in Artificial Neural Network (딥러닝이 적용된 게임 밸런스에 관한 연구 게임 기획 방법론의 관점으로)

  • Jeon, Joonhyun
    • Journal of the HCI Society of Korea
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    • v.13 no.3
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    • pp.65-73
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    • 2018
  • Game balance settings are crucial to game design. Game balancing must take into account a large amount of numerical values, configuration data, and the relationship between elements. Once released and served, a game - even for a balanced game - often requires calibration according to the game player's preference. To achieve sustainability, game balance needs adjustment while allowing for small changes. In fact, from the producers' standpoint, game balance issue is a critical success factor in game production. Therefore, they often invest much time and capital in game design. However, if such a costly game cannot provide players with an appropriate level of difficulty, the game is more likely to fail. On the contrary, if the game successfully identifies the game players' propensity and performs self-balancing to provide appropriate difficulty levels, this will significantly reduce the likelihood of game failure, while at the same time increasing the lifecycle of the game. Accordingly, if a novel technology for game balancing is developed using artificial intelligence (AI) that offers personalized, intelligent, and customized service to individual game players, it would bring significant changes to the game production system.

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Development of AI-based Cognitive Production Technology for Digital Datadriven Agriculture, Livestock Farming, and Fisheries (디지털 데이터 중심의 AI기반 환경인지 생산기술 개발 방향)

  • Kim, S.H.
    • Electronics and Telecommunications Trends
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    • v.36 no.1
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    • pp.54-63
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    • 2021
  • Since the recent COVID-19 pandemic, countries have been strengthening trade protection for their security, and the importance of securing strategic materials, such as food, is drawing attention. In addition to the cultural aspects, the global preference for food produced in Korea is increasing because of the Korean Wave. Thus, the Korean food industry can be developed into a high-value-added export food industry. Currently, Korea has a low self-sufficiency rate for foodstuffs apart from rice. Korea also suffers from problems arising from population decline, aging, rapid climate change, and various animal and plant diseases. It is necessary to develop technologies that can overcome the production structures highly dependent on the outside world of food and foster them into export-type system industries. The global agricultural industry-related technologies are actively being modified via data accumulation, e.g., environmental data, production information, and distribution and consumption information in climate and production facilities, and by actively expanding the introduction of the latest information and communication technologies such as big data and artificial intelligence. However, long-term research and investment should precede the field of living organisms. Compared to other industries, it is necessary to overcome poor production and labor environment investment efficiency in the food industry with respect to the production cost, equipment postmanagement, development tailored to the eye level of field workers, and service models suitable for production facilities of various sizes. This paper discusses the flow of domestic and international technologies that form the core issues of the site centered on the 4th Industrial Revolution in the field of agriculture, livestock, and fisheries. It also explains the environmental awareness production technologies centered on sustainable intelligence platforms that link climate change responses, optimization of energy costs, and mass production for unmanned production, distribution, and consumption using the unstructured data obtained based on detection and growth measurement data.

Qualitative Consumer Preference Studies on Korean-style Kimchi in Chinese Living in Korea (한국 거주 중국인을 대상으로 한 한국 김치에 대한 정성적 기호도 조사)

  • Lee, Mi-Ai;Choi, Yun-Jeong;Kim, Mina K.
    • Journal of the East Asian Society of Dietary Life
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    • v.27 no.2
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    • pp.185-193
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    • 2017
  • The objective of this research was to determine the current consumer interest toward Korean Kimchi and identify the preferred sensory characteristics of Kimchi using qualitative consumer studies on Chinese consumers. Five different sessions of focus group interview (FGI) were conducted (n=39). The FGI session was designed to determine 1) current knowledge and interest as well as usage level of Korean-style Kimchi, 2) interests toward different Korean Kimchi based on appearance and tasting evaluation. Based on the results, radish was the most accepted ingredient for Kimchi among Chinese consumers, as it resembles the sensory characteristics of Chinese-style Kimchi. The sensory characteristics driving consumer preferences towards radish-based Kimchi included crunchy texture, and just-about-right sweet and spicy flavor. Thinly sliced radish was the most accepted shape of radish-based Kimchi. The current study provides practical information for product development of Kimchi targeted for Chinese.

Design and Implementation of a Health Care System using Tangible Interface (텐저블 인터페이스를 이용한 건강관리 시스템 설계 및 구현)

  • Kim Kyu-Jong;Shin Ki-Bo;Lee Byung-Joo;Choi Kyung-Sub;Choi Young-Mee;Choo Moon-Won
    • Proceedings of the Korea Contents Association Conference
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    • 2005.11a
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    • pp.523-528
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    • 2005
  • In this paper, we designed and made a bicycle with tangible interface using a game to make a tangible interface for health-care system. In particular, through a survey of the preference of users, we have showed a tangible machine which is possible to recognize users' control as a form of tangible interface, have presented the controllable method of a game contents, have provided the contents reality maximized using game AI and game physics.

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Re-examination of Response Compatability Hypothesis in Decision-Making (결정에서 반응 조화설의 재검증)

  • Ahn, Sang-Ji;Lee, Young-Ai
    • Korean Journal of Cognitive Science
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    • v.20 no.2
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    • pp.197-223
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    • 2009
  • Three studies re-examined Shafer's(1993) response compatibility hypothesis in decision making. This hypothesis proposes that participants choose or reject an option when its features are compatible with either a selection or a rejection response. By changing the description of options into sentences and by the prior presentation of either a selection or a rejection question, we obtained results fairly consistent with the predictions of the response compatibility hypothesis. Based on the analysis of both previous and present results, we discussed the importance of preference elicitation methods when constructing options. Our results were compared to those of recent studies.

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Cody Recommendation System Using Deep Learning and User Preferences

  • Kwak, Naejoung;Kim, Doyun;kim, Minho;kim, Jongseo;Myung, Sangha;Yoon, Youngbin;Choi, Jihye
    • International Journal of Advanced Culture Technology
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    • v.7 no.4
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    • pp.321-326
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    • 2019
  • As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

Application of ChatGPT text extraction model in analyzing rhetorical principles of COVID-19 pandemic information on a question-and-answer community

  • Hyunwoo Moon;Beom Jun Bae;Sangwon Bae
    • International journal of advanced smart convergence
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    • v.13 no.2
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    • pp.205-213
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    • 2024
  • This study uses a large language model (LLM) to identify Aristotle's rhetorical principles (ethos, pathos, and logos) in COVID-19 information on Naver Knowledge-iN, South Korea's leading question-and-answer community. The research analyzed the differences of these rhetorical elements in the most upvoted answers with random answers. A total of 193 answer pairs were randomly selected, with 135 pairs for training and 58 for testing. These answers were then coded in line with the rhetorical principles to refine GPT 3.5-based models. The models achieved F1 scores of .88 (ethos), .81 (pathos), and .69 (logos). Subsequent analysis of 128 new answer pairs revealed that logos, particularly factual information and logical reasoning, was more frequently used in the most upvoted answers than the random answers, whereas there were no differences in ethos and pathos between the answer groups. The results suggest that health information consumers value information including logos while ethos and pathos were not associated with consumers' preference for health information. By utilizing an LLM for the analysis of persuasive content, which has been typically conducted manually with much labor and time, this study not only demonstrates the feasibility of using an LLM for latent content but also contributes to expanding the horizon in the field of AI text extraction.

Age differences of preference for humanoid AI speakers (얼굴형 인공지능 스피커에 대한 선호의 나이 효과)

  • Oh, Songjoo;Hwang, Jihyun;Yew, Jiho;Hahn, Sowon
    • Korean Journal of Cognitive Science
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    • v.29 no.1
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    • pp.1-16
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    • 2018
  • In this study, we investigated age differences of preference and trust ratings when the appearance of an artificial intelligent speaker resembles a human face. The appearance of the artificial intelligent speaker was presented in seven levels from robot face to human face. In addition, face stimuli were divided into gender (male and female) and age (20s / 60s). Participants evaluated the reliability and likability of each face stimulus on a 7-point scale. The results show that younger adults tend to prefer the face that was halfway between the robot and the human face, while older adults evaluated that the perceived reliability and likability were higher when the stimuli resembled the human face. When asked to choose the most preferred of the four face categories, all participants chose a younger face. However, with additional conditions including emoticon face and empty condition, older adults still preferred human face, while younger adults preferred emoticon face and empty condition. Taken together, older adults are more receptive to human faces than robotic faces in the context of artificial intelligence speakers. Because artificial intelligent speakers can play an important role in the elderly living alone, the present study will be a good reference in the design and development of artificial intelligent speakers for the elderly users.