• Journal of Internet Computing and Services
• /
• v.23 no.2
• /
• pp.45-52
• /
• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.339-345
• /
• 2022

Outcome-based education (OBE) is a tried-and-true teaching technique based on a set of predetermined goals. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs) are the components of OBE. At the end of each year, the Program Outcomes are evaluated, and faculty members can submit many recommended measures which dependent on the relationship between the program outcomes and its courses outcomes to improve the quality of program and hence the overall educational program. When a vast number of courses are considered, bad actions may be proposed, resulting in unwanted and incorrect decisions. In this paper, a recommender system, using collaborative filtering and association rules algorithms, is proposed for predicting the best relationship between the program outcomes and its courses in order to improve the attributes of the graduates. First, a parallel algorithm is used for Collaborative Filtering on Data Model, which is designed to increase the efficiency of processing big data. Then, a parallel similar learning outcomes discovery method based on matrix correlation is proposed by mining association rules. As a case study, the proposed recommender system is applied to the Computer Information Systems program, College of Computer Sciences and Information Technology, Al-Baha University, Saudi Arabia for helping Program Quality Administration improving the quality of program outcomes. The obtained results revealed that the suggested recommender system provides more actions for boosting Graduate Attributes quality.

• Journal of Information Processing Systems
• /
• v.18 no.1
• /
• pp.75-88
• /
• 2022

With the development of the sharing economy, existing recommender services are changing from user-item recommendations to user-user recommendations. The most important consideration is that all users should have the best possible satisfaction. To achieve this outcome, the matching service adds information between users and items necessary for the existing recommender service and information between users, so higher-level data mining is required. To this end, this paper proposes a user-to-user matching service (UTU-MS) employing the prediction of mutual satisfaction based on learning. Users were divided into consumers and suppliers, and the properties considered for recommendations were set by filtering and weighting. Based on this process, we implemented a convolutional neural network (CNN)-deep neural network (DNN)-based model that can predict each supplier's satisfaction from the consumer perspective and each consumer's satisfaction from the supplier perspective. After deriving the final mutual satisfaction using the predicted satisfaction, a top recommendation list is recommended to all users. The proposed model was applied to match guests with hosts using Airbnb data, which is a representative sharing economy platform. The proposed model is meaningful in that it has been optimized for the sharing economy and recommendations that reflect user-specific priorities.

• Journal of Internet Computing and Services
• /
• v.23 no.2
• /
• pp.1-7
• /
• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• Journal of Korea Multimedia Society
• /
• v.25 no.8
• /
• pp.1109-1121
• /
• 2022

As the demand for quality sleep increases in modern society, the importance of sleep technology has increased. Recently, development of sleep environment improvement products and research on the user's sleep improvement have been activated. Representatively, user sleep pattern analysis research is being conducted through the existing polysomnography, but it is difficult to use it in the sleep environment of daily life. Therefore, in this paper, we propose a system design that can provide a customized deep sleep service to users by detecting sleep disturbance factors in a motion bed environment. In order to improve the user's sleep satisfaction, a logistic regression-based sleep pattern analysis model is proposed and accuracy and significance are verified through experiments. And to improve user's sleep satisfaction, we propose a logistic regression-based sleep pattern analysis model and verify accuracy and significance through experiments. The proposed system is expected to improve the user's sleep quality and effectively prevent and manage sleep disorders.

• Korean Journal of Culture and Social Issue
• /
• v.22 no.4
• /
• pp.623-641
• /
• 2016

The psychological autopsy is a method to estimate causes of suicidal death by collecting data from interviewing suicide victim's family added with additional life records. Recently, local governments and suicide prevention centers have been trying to implement psychological autopsy. but there is paucity of efforts examining the validity and effectiveness of the psychological autopsy. Therefore, this paper reviewed psychological autopsy studies and risk factors in Western and Asia countries. and then a methodology for the Korean version of psychological autopsy was suggested. After investigating the specific risk factors for korean suicide through psychological autopsy, then it can be possible to grasp the intervention point for effective suicide prevention. We also propose future directions for psychological autopsy study and interventions in Korea.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.3
• /
• pp.435-439
• /
• 2022

Many of the industry was changed because of the pandemic of covid 19. It combined with the tendency of modern people to pursue convenience. The industry of Cosmetics also changed business channel from offline to online. Before, people can not get suggestions after they complete the survey. This paper research how to suggest some cosmetics products with their skin type and skin data. We will develop Beauty Concierge system that can get suggestion after the survey. It's will make people attend activity and can make more benefit to the people.

• Journal of Distribution Science
• /
• v.21 no.1
• /
• pp.1-12
• /
• 2023

Purpose: The primary purpose of this research is to understand the potential influence of various factors, namely, pharmacies' recommendations, families' and friend recommendations, price, country of origin, and past experience, on the purchasing decision of nonprescription medicines in the Jordanian context. Research design, data, and methodology: A survey was conducted among 220 Jordanian consumers through a self-administered questionnaire. Further, the authors utilized the mall intercept method as a convenience sampling technique to recruit the respondents who shop at different pharmacies. The data were analyzed using various statistical techniques, such as frequency and percentage for describing the demographics of the sample, Cronbach's alpha for testing the reliability of the data, skewness and kurtosis to check the normality of data, and further, multiple regression using SPSS version 25 was performed for examining the hypotheses. Results: The findings revealed that pharmacists' recommendation, recommendations from friends and family, and price positively influenced consumers' purchase decisions of OTC medicines in Jordan, whereas consumers' past experience and country of origin had no influence on consumers' purchasing decisions of OTC medicines. Conclusions: The paper examines the influence of various factors on customers' purchase decisions of OTC medicines, draws conclusions, and makes recommendations. Also, research limitations are mentioned.

• Nutrition Research and Practice
• /
• v.16 no.sup1
• /
• pp.47-56
• /
• 2022

This paper examines the process and evidence used to create the Dietary Reference Intake (DRI) of alpha-linolenic acid (ALA) and eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) for Koreans. ALA (18:3n3) is an essential fatty acid, and EPA and DHA are known to have beneficial effects on cardiovascular disease risk and reduction of triglyceride levels. Various international organizations have suggested dietary recommendations for n-3 polyunsaturated fatty acids (PUFAs), including ALA, EPA, and DHA. A DRI for Koreans was established for the first time in 2020, specifically for the adequate intake (AI) of ALA and EPA + DHA. This recommendation was based on the average intake of ALA and EPA + DHA from the Korea National Health and Nutrition Examination Survey 2013-2017. For Korean infants, the AI of ALA and DHA was based on the fatty acid composition of maternal milk. Estimated average requirement and a tolerable upper intake level have not been set for n-3 PUFA due to insufficient evidence. In addition, the intake level of n-3 PUFA for prevention of chronic disease has also not been determined. Future studies and randomized controlled trials are required to establish the UL and to define the level for disease prevention.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.563-565
• /
• 2021

The most important feature of the recently increasing content providing service is that the amount of content increase over time is very large. Accordingly, the importance of user curation is increasing, and various techniques are used to implement it. In this paper, among the techniques for video recommendation, the analysis technique using voice data and subtitles and the video comparison technique based on keyframe extraction are compared with the results of implementing and applying the video content of real big data. In addition, through the comparison result, a video content environment to which each analysis technique can be applied is proposed.