• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.133-139
• /
• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.23 no.1
• /
• pp.87-102
• /
• 2021

The purpose of this study is to consider effective digital marketing strategies through analysis of luxury fashion brands. This study conducted both quantitative analysis and case studies of the brands Louis Vuitton, Gucci, Burberry, and Chanel. To measure the brand image of the luxury fashion brands, the survey was distributed to Millennials, and total of 277 responses were used for the final analysis by using SPSS 25.0 statistical program. Other than survey, this paper analyzed digital marketing strategies of luxury fashion brands through brand-related papers, website and social media of each brand, Samsung Designnet's database, and news posted on search engines. The results of this study are as follows: First, according to the result of examining brand image of luxury fashion brands, there was no significant difference between brands, except Gucci. Second, this study analyzed each luxury fashion brand to understand the characteristics of digital marketing, and common characteristics were identified. Third, by analyzing the brand image and digital marketing strategies of luxury fashion brands, it was confirmed that Gucci's brand image and digital marketing strategies were consistent, while there was a difference between Burberry's brand image and digital marketing strategy. Therefore, this article proposes the following digital marketing strategies that are suitable for luxury fashion brands. First, is the connection of on/offline channels. Second, is the use of AI technology. Third, is a blockchain-based platform.

• The Journal of the Korea Contents Association
• /
• v.21 no.8
• /
• pp.1-9
• /
• 2021

Deep learning in computer vision has made accelerated improvement over a short period but large-scale learning data and computing power are still essential that required time-consuming trial and error tasks are involved to derive an optimal network model. In this study, we propose a similar image classification performance improvement method based on CR (Confusion Rate) that considers only the characteristics of the data itself regardless of network optimization or data reinforcement. The proposed method is a technique that improves the performance of the deep learning model by calculating the CRs for images in a dataset with similar characteristics and reflecting it in the weight of the Loss Function. Also, the CR-based recognition method is advantageous for image identification with high similarity because it enables image recognition in consideration of similarity between classes. As a result of applying the proposed method to the Resnet18 model, it showed a performance improvement of 0.22% in HanDB and 3.38% in Animal-10N. The proposed method is expected to be the basis for artificial intelligence research using noisy labeled data accompanying large-scale learning data.

• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.77-90
• /
• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.6
• /
• pp.659-665
• /
• 2023

The exciter of a ship generator adjusts the magnetic flux through excitation current control to maintain the output terminal voltage constant. The voltage controller inside the exciter typically uses a proportional integral control method. however, the response characteristics determined by the gain and time constant produce unwanted output owing to an inappropriate setting value that can reduce the quality and stability of power within the ship. In this study, a neural network circuit is learned using stable input/output data that can be obtained through the AC4A type exciter model provided by IEEE, and the simulation is performed by replacing the existing proportional integral control type voltage controller with the learned neural network circuit controller. Consequently, overshooting was improved by up to 9.63% compared with that of the previous model, and excellence in stable response characteristics was confirmed.

• Convergence Security Journal
• /
• v.23 no.5
• /
• pp.65-72
• /
• 2023

This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.

• Journal of Distribution Science
• /
• v.22 no.4
• /
• pp.11-22
• /
• 2024

Purpose: This research delves into the various factors that influence the performance of restaurant businesses on social commerce platforms in Bangkok, Thailand. The study considers both internal and external factors, including but not limited to business characteristics and location. Moreover, this research also analyzes the effects of employing multiple social commerce platforms on business efficiency and explores the underlying reasons for such effects. Research design, data, and methodology: Restaurants can be classified into different price ranges: low, medium, and high. To further investigate, we employed natural language processing AI to analyze online reviews and evaluate algorithm performance using machine learning techniques. We aimed to develop a model to gauge customer satisfaction with restaurants across different price categories effectively. Results: According to the research findings, several factors significantly impact restaurant groups in the low and mid-price ranges. Among these factors are population density and the number of seats at the restaurant. On the other hand, in the mid-and high-price ranges, the price levels of the food and drinks offered by the restaurant play a crucial role in determining customer satisfaction. Furthermore, the correlation between different social commerce platforms can significantly affect the business performance of high-price range restaurant groups. Finally, the level of online review sentiment has been found to influence customer decision-making across all restaurant types significantly. Conclusions: The study emphasizes that restaurants' characteristics based on their price level differ significantly, and social commerce platforms have the potential to affect one another. It is worth noting that the sentiment expressed in online reviews has a more significant impact on customer decision-making than any other factor, regardless of the type of restaurant in question.

• Journal of Digital Convergence
• /
• v.20 no.4
• /
• pp.93-100
• /
• 2022

This study aimed to understand the current status of science and engineering university(SEU) R&D operations depending on the research project characteristics(e.g., stages and characteristics), then provide implications for future university R&D support systems and related policies. Hence, an online survey targeting SEU R&D recipients was conducted between October 4th to November 5th, 2021. Analyzing 445 valid data using the Apriori algorithm, 16 association rules for R&D operation according to the research project characteristics show that regardless of research characteristics, SEU's R&D projects, particularly in applied research, were funded or operated under the leadership of government or public institutions. For basic research, individual researchers had a higher level of autonomy in determining research topics; yet, they had a short duration (3 years) and a unit of evaluation period of more than 3 years. These findings can be empirical evidence for revealing the relationship among various variables in operating SEUs' R&D.

• Asian-Australasian Journal of Animal Sciences
• /
• v.24 no.5
• /
• pp.707-712
• /
• 2011

The objective of the present study was to determine gender-related and month-related behavioral differences in captive alpine musk deer. The study was conducted at Xinglongshan Musk Deer Farm (XMDF) of Xinglongshan National Nature Reserve in Gansu Province of western China. The integrated method of focal sampling and all occurrence recording was utilized to quantify the behavioural patterns of 45 captive alpine musk deer (Moschus sifanicus) during non-mating season (from August $1^{st}$ to October $25^{th}$), and the behavioural durations of 12 behavioural patterns such as standing-gazing were recorded. The behavioural modes were compared to explore the potential differences between females and males, and the monthly behavioural modes for males and females were analyzed. Our results showed that the captive female deer in XMDF could compensate the energy lost in pregnancy, parturition and lactation through improving its ingestive efficiency. In order to be more sensitive to the changing environment, females expressed more standing-gazing (SG: $67.38{\pm}12.69\;s$) and moving (MO; $27.41{\pm}5.02\;s$), but less bedding (BE: $42.32{\pm}11.35\;s$) than male deer (SG: $56.43{\pm}9.19\;s$; MO: $19.23{\pm}4.64\;s$; BE: $96.14{\pm}15.71\;s$). Furthermore, females perform more affinitive interaction (AI: $7.89{\pm}4.81\;s$) but less ano-genital sniffing (AS: $0.24{\pm}0.13\;s$) and agonistic behaviour (CI: $0.57{\pm}0.26\;s$) than males (AI: $1.45{\pm}1.09\;s$; AS: $0.45{\pm}0.29\;s$; CI: $1.42{\pm}0.67\;s$). The females expressed ingestion more in October ($132.31{\pm}27.47\;s$) than in August ($28.80{\pm}18.44\;s$) and September ($45.1{\pm}10.84\;s$), and the males performed Ano-genital sniffing (AS: $1.79{\pm}1.14\;s$) and self-directed behaviour (SD: $12.61{\pm}5.03\;s$) significantly more in October than in August (AS: 0 s; SD: $0.62{\pm}0.17\;s$) and September (AS: $0.02{\pm}0.01\;s$; SD: $0.17{\pm}0.15\;s$). Moreover, male musk deer increased the intension of ano-genital sniffing, agonistic behaviour and tail rubbing behaviour, which were related to sexual activities.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.16
• /
• pp.7117-7121
• /
• 2015

Background: The effects of home nursing intervention on the quality of life in patients with nasopharyngeal carcinoma (NPC) after radiotherapy and chemotherapy are unclear. According to the characteristics of nursing home patients with nasopharyngeal carcinoma, we should continuously improve the nursing plan and improve the quality of life of patients at home. Materials and Methods: We selected 180 patients at home with NPC after radiotherapy and chemotherapy. The patients were randomly divided into experimental and control groups (90 patients each). The experimental group featured intervention with an NPC home nursing plan, while the control group was given routine discharge and outpatient review. Nursing intervention for patients was mainly achieved by regular telephone follow-up and home visits. We use the quality of life scale (QOL-C30), anxiety scale (SAS) and depression scale (SDS) to evaluate these patients before intervention, and during follow-up at 1 month and 3 months after the intervention. Results: Overall health and quality of life were significantly different between the groups (p<0.05), Emotional function score was significantly higher after intervention (p<0.05), as were cognitive function and social function scores after 3 months of intervention (p<0.05). Scores of fatigue, nausea and vomiting, pain, appetite and constipation were also significantly different between the two groups (p<0.05). Rates of anxiety and depression after 3 months of intervention were 11.1%, 22.2% and 34.4%, 53.3%, the differences being significant (p<0.05). Conclusions: NPC home nursing plan could effectively improve overall quality of life, cognitive function, social function (after 3 months) of patients, but improvement regarding body function is not suggested. Fatigue, nausea and vomiting, pain, appetite, constipation were clearly improved. We should further pursue a personalized, comprehensive measurements for nursing interventions and try to improve the quality of life of NPC patients at home.