• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.38-50
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• 2022

Purpose: The goal of this research is to create a numerical analytic database that may assist fire prevention managers and building officials in prioritizing items that need to be addressed in order to improve evacuation safety performance while working within a constrained budget and time frame. Method: It was carried out utilizing the CFD Tool, a quantitative evaluation approach, to assess evacuation safety. One direct staircase-type apartment houses and one corridor-type apartment were chosen to make it. Result: In the fire compartment category, Apartment A's evacuation time was around 130 percent longer than that of sprinkler facilities. Conclusion: Fire prevention managers and building officials feel that starting with a single level and implementing "dwelling unit separations" will increase evacuation safety, and that maintaining fire compartments and sprinkler systems at all times will be effective. Because of the limited characteristics of smoke propagation in corridor-type apartments compared to direct staircase-type flats, it is thought that fire extinguishing equipment should be addressed.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.233-240
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• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.259-267
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• 2022

With the rapid development and introduction of digital technologies, both everyday human life and technological processes of any production are changing, which stimulates the transformation of the economy and education. Digital technologies are not only a tool, but also a living environment of modern human, which opens up new opportunities: learning at any convenient time, continuing education, the ability to form individual educational learning trajectories and more. However, the digital environment requires teachers to take a modern approach to the organization of the educational process, the formation of new skills and abilities to work in the digital educational environment. As a result of the study, it was found that the system of vocational education should provide training for masters of industrial training who have a high level of digital competence. The purpose of the article is to single out, theoretically substantiate and determine the level of formation of structural components of digital competence of future masters of agricultural training. The structure of digital competence of agricultural master was analyzed on the basis of domestic and foreign scientists researches. Systematized research results indicate that digital competence consists of four components: motivational-value (combination of internal and external motives for the use of digital technologies in future professional activities), cognitive (a set of theoretical knowledge, skills and abilities of future master of industrial training to effectively build educational process with the use of digital technologies), activity-professional (expansion and deepening of knowledge, skills, necessary skills for effective implementation of digital technologies in the educational process) and evaluative-reflexive (ability to analyze and self-analyze own activities and its results taking into account professional characteristics, self-realization in professional activities through the use of digital technologies). These components are comparable with the indicators that describe the knowledge, skills and abilities needed by the future master of industrial training to organize the modern educational process. A questionnaire was conducted to determine the levels of this competence formation, which allows us to conclude that it is necessary to increase the level of formation of all components of digital competence of future masters of industrial training in agriculture. The results of the study can be used as a basis for the development of disciplines that form the special competencies of masters of industrial training in agriculture and programs of advanced training of teachers.

• Journal of Music and Human Behavior
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• v.19 no.1
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• pp.93-112
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• 2022

The purpose of this study was to compare participants' perceived emotion following harmonic changes in music. In this study, 144 participants, aged 19 to 29 years, listened to music online that included low to high harmonic progression in tonal music (major-minor). After listening to each piece of music, participants were asked to rate 4 items using a 7-point Likert scale: emotional potency, arousal, degree to which the harmony impacted the listener's emotions, and listener's preference for the music. There were significant differences between each of the four items upon the level of harmonic progression. When the participants were divided into two groups (i.e., those with a background in music and those with no background in music), there was a significant difference between the groups in terms of emotional potency, but there was no significant interaction effect. This study confirmed that various emotional responses in listeners can be induced by controlling the exogenous variables in musical excerpts. Based on this, it is expected that the harmonic progression level can be provided to the client to be used as an effective therapeutic tool in music therapy intervention.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.369-381
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• 2021

Artificial Intelligence(AI) technology can be used in arts exhibitions ranging from planning exhibitions, filed progress, and evaluation. AI has been expanded its scope from planning exhibition and guidance services to tools for creating arts. This paper focuses on chatbots that utilize exhibition and AI technology convergence to provide information and services. To study more specifically, I developed a chatbot for exhibition services using the Naver Clova chatbot tool and information from the National Museum of Modern and Contemporary Art(MMCA), Korea. In this study, information was limited to viewing and exhibition rather than all information of the MMCA, and the chatbot was developed which provides a scenario type to get an answering user want to gain through a button and a text question and answer(Q&A) type to directly input a question. As a result of evaluating the chatbot with six items according to ELIZA's chatbot evaluation scale, a score of 4.2 out of 5 was derived by completing the development of a chatbot to be used to deliver viewing and exhibition information. The future research task is to create a perfect chatbot model that can be used in an actual arts exhibition space by connecting the developed chatbot with continuous scenario answers, resolving text Q&A-type answer failures and errors, and expanding additional services.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.53-62
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• 2021

After the first to present projected moving pictures to audiences, the film industry has been reshaping along with technological advancements. Through the full-scale introduction of visual effects-oriented post-production and digital technologies in the film-making process, the film industry has not only undergone significant changes in the production, but is also embracing the cutting edge technologies broadly and expanding the scope of industry. Not long after the change to digital cinema, the concept of artificial intelligence, first known at the Dartmouth summer research project in 1956, before the digitalization of film, is expected to bring about a big transformation in the film industry once again. Large volume of clear digital data from digital film-making makes easy to apply recent artificial intelligence technologies represented by machine learning and deep learning. The use of artificial intelligence techniques is prominent around major visual effects studios due to automate many laborious, time-consuming tasks currently performed by artists. This study aims to predict how artificial intelligence technology will change the film industry in the future through analysis of visual effects production cases using artificial intelligence technology as a production tool and to discuss the industrial potential of artificial intelligence as visual effects technology.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.321-331
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• 2022

In this study, we tried to present a method for calculating the amount of regression using a watershed modeling method that can simulate the hydrological mechanism of water balance analysis and agricultural water based on watershed unit. Using the soil water assessment tool (SWAT), a watershed water balance analysis was conducted considering the simulation of paddy fields for the Manbongcheon Standard Basin (97.34 km²), which is a representative agricultural area of the Yeongsan river basin. Before evaluating return flow, the SWAT was calibrated and validated using the daily streamflow observation data at Naju streamflow gauge station (NJ). The coefficient of determination (R²), Nash-Sutcliffe Efficiency (NSE), Root-Mean-Square Error (RMSE) of NJ were 0.73, 0.70, 0.64 mm/day. Based on the calibration results for three years (2015-2017), the quick return flow and the return rate compared to the water supply amount for the irrigation period (April 1 to September 30) were calculated, and the average return flow rate was 53.4%. The proposed method of this study may be used as foundation data to optimal agricultural water supply plan for rational watershed management.

• Journal of Korean Elementary Science Education
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• v.40 no.4
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• pp.520-544
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• 2021

This study aims to develop a scientific creativity test for exploring the relationship between elementary students' creative process and product. For this, the researcher reviewed the literatures of scientific creativity and developed the items based on the constructs of creative process and product. After a review conducted by nine science education specialists, a pilot test, and additional revision and supplementation of observation test, the test, consisting of two sets-"animals" and "plants"-was finally conducted on 105 fifth-grade students. The test results were analyzed by using statistical analysis software. WinSteps, SPSS, and AMOS. The main findings from this study are as follows. First, when it comes to scientific creativity, creative process consists of science knowledge, inquiry skills, and creative thinking skills (divergent, convergent, and associative thinking skills). Creative product in science is a new and scientifically useful idea realized in a certain form. Second, observation, which was selected as a representative inquiry skill in this research, should not be related to creative thinking skills. Third, among the rest of the items, usefulness had the lowest averages, as it was, perhaps, difficult to satisfy the teachers' criteria for the scientific validity and usefulness. Fourth, the Spearman correlation coefficients between the items of "animals" and "plants" to find out the parallel-form reliability were significant, except for the item of originality. Fifth, the test was satisfactory with regard to the three aspects of construct validity-convergent, discriminant, and nomological. This study concludes by discussing the usefulness of this test, which has the possibility of exploring the relationship between creative process and product and of playing a role as an authentic evaluation tool in school.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.111-126
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• 2022

As operations that were only conducted in physical space in the past change to operations that include cyberspace, it is necessary to analyze how cyber attacks affect weapon systems using cyber systems. For this purpose, it would be meaningful to analyze a tool that analyzes the effects of physical weapon systems in connection with cyber. The ROK military has secured and is operating the US JMEM, which contains the results of analyzing the effects of physical weapon systems. JMEM is applied only to conventional weapon systems, so it is impossible to analyze the impact of cyber weapon systems. In this study, based on the previously conducted cyber attack damage assessment framework, a framework for analyzing the impact of cyber attacks on physical missions was presented. To this end, based on the MOE and MOP of physical warfare, a cyber index for the analysis of cyber weapon system effectiveness was calculated. In addition, in conjunction with JMEM, which is used as a weapon system effect manual in physical operations, a framework was designed and tested to determine the mission impact by comparing and analyzing the results of the battle in cyberspace with the effects of physical operations. In order to prove the proposed framework, we analyzed and designed operational scenarios through domestic and foreign military manuals and previous studies, defined assets, and conducted experiments. As a result of the experiment, the larger the decrease in the cyber mission effect value, the greater the effect on physical operations. It can be used to predict the impact of physical operations caused by cyber attacks in various operations, and it will help the battlefield commander to make quick decisions.