Exploring the frontiers of AI, Machine Learning, and Data Science
With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified ‘recommender system’, ‘wireless & IoT’, ‘computer vision’, and ‘text mining’ as increasingly important research fields related to HRS research, and confirmed that ‘personalization’ and ‘privacy’ are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions
The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO’s PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.
A text mining and machine learning approach to analyze consumer behavior in travel sector through live commerce chat data. Submitted to Journal of Hospitality Marketing & Management.
The advent of social media has transformed the stock investment landscape considerably. Individual investors are increasingly shifting from traditional financial institutions to platforms such as Facebook, X, and stock discussion forums to obtain information and exchange opinions, significantly affecting the stock market. Post-COVID-19, the influx of individual investors has accentuated their importance in the South Korean stock market, necessitating the development of prediction models utilizing data from stock discussion forums, a facet of social media. Research on predicting stock price fluctuations using social media data is lacking, and most studies have focused on news articles, with insufficient utilization of stock discussion forum posts. Considering the significant proportion of individual investors in the Korean stock market, it is imperative to conduct research utilizing posts from stock discussion forums, where individual investors’ opinions are directly formed, for accurate stock price predictions. This study specifically focuses on the secondary battery sector, which garnered significant attention during the pandemic, to examine the sentiments of individual investors. Employing data from stock price prediction models, this study compares the accuracy of predictions. The growth of the secondary battery sector can be attributed to global environmental changes such as worldwide energy transition policies, carbon neutrality goals, and an accelerated shift toward renewable energy technologies. This study concludes that using posts from stock discussion forums directly shaped by individual investors enhances the sophistication of predictions. Accordingly, theoretical and practical implications are put forth.
Review Analysis and Response Using Generative AI with Design Science Methodology. Ongoing research on LLM-based hotel review management system.
Knowledge Distillation and Aggregation in Graph-based federated learning architecture for privacy-preserving machine learning.