Human-Centered Experience Engineering for Healthcare AI Governance
The rapid adoption of artificial intelligence (AI) in healthcare has intensified the "principle-practice gap," where existing governance frameworks provide ethical principles but lack actionable implementation guidance. This study establishes the concept of Human-Centered Experience Engineering (HCEE), systematizes it through the SER-M (Subject-Environment-Resource-Mechanism) model, and proposes an HCEE-based Healthcare AI Governance Architecture. Applying Design Science Research (DSR) methodology, we developed a four-layer governance architecture and validated it through agent-based simulation. The architecture integrates Patient Experience Index (PXI) and Employee Experience Index (EXI) as core governance variables, with trigger-control rules that adjust AI automation levels based on experience thresholds. Simulation results show that full HCEE implementation improves PXI by 34.5% and EXI by 43.6%. This study makes three contributions to the MIS field. First, it establishes HCEE, reconceptualizing human experience as a governance input variable and extending Human-Centered AI discourse to the operational level. Second, it applies the SER-M model to Healthcare AI Governance, extending the Mechanism-Based View to IS. Third, it demonstrates that AI governance can be implemented as a designable IS artifact through trigger-control rules.
Copyright (c) 2026 AI Journal of BUsiness https://creativecommons.org/licenses/by-nc-nd/4.0
Practical Applicability of an AI-Based Automated Classification Model in Elderly Care Institutions
This study evaluates the practical applicability of an artificial intelligence (AI)-based automated text classification model in elderly care institutions. While prior research has primarily focused on improving algorithmic performance, limited empirical attention has been given to whether automated outputs demonstrate sufficient alignment with existing manual classification practices in real operational environments. This study reanalyzes a categorized dataset of 1,224 elderly care records to examine predictive agreement from an organizational applicability perspective. A logistic regression classifier with hyperparameter optimization was implemented, and performance was assessed using accuracy, precision, recall, and F1-score metrics. The optimized model achieved an accuracy of 0.790 and a weighted F1-score of 0.780. Category-level analysis indicated stable performance across primary operational classes, although variation in recall was observed in specific categories. The findings suggest that AI-assisted classification may function as a structured support mechanism within institutional documentation workflows. This study contributes empirical evidence regarding feasibility of practical implementation without asserting direct organizational performance transformation.
The Role of Foundry for High-Performance AI Semiconductors
Recently, the AI semiconductor market has been experiencing rapid advancements across multiple dimensions, including computational performance, energy efficiency, and integration density. As a result, not only the design capabilities of fabless companies but also the manufacturing capabilities of foundry companies that implement these designs have become more critical than ever. In particular, achieving high yield, possessing advanced process technologies, and securing advanced packaging capabilities have emerged as key factors for realizing high-performance AI semiconductors. This study selects TSMC, the world’s largest foundry company, as a case and analyzes how the company secures manufacturing competitiveness in AI semiconductors through yield management, process control, and advanced packaging technologies. The findings indicate that TSMC enhances AI semiconductor performance through integrated management across the entire value chain—from design and manufacturing to packaging and mass production—while maintaining stable production capabilities and making proactive R&D investments in advanced packaging. These results demonstrate that the integrated management of yield, process, and packaging constitutes a core mechanism for improving AI semiconductor performance.
Comparative Analysis of Medium-Range (72-hour) Temperature Time Series Forecasting Using Korea Meteorological Administration Surface Observation Data
This study presents a comparative evaluation of medium-range (72-hour) air temperature forecasting performance among recent deep learning–based time series models—Neural Hierarchical Interpolation for Time Series Forecasting (NHITS), Temporal Fusion Transformer (TFT), Patch Time Series Transformer (PatchTST)—relative to a traditional statistical model (SARIMA) and a climatology-based baseline. The analysis is conducted using hourly surface observation data provided by the Korea Meteorological Administration, with a focus on a 72-hour multi-step forecasting task for the Seoul station. Empirical results show that all three deep learning models substantially outperform the traditional baselines. Among them, the TFT achieves the best overall performance, with a Mean Squared Error (MSE) of 4.39, a Root Mean Squared Error (RMSE) of 2.10, a Mean Absolute Error (MAE) of 1.65, and a coefficient of determination ( ) of 0.775. This corresponds to an approximate 54% reduction in RMSE compared with the SARIMA model. NHITS also demonstrates a significant improvement over the statistical baseline, achieving an RMSE of 2.61. These results indicate that the Temporal Fusion Transformer architecture, which combines a Long Short-Term Memory (LSTM) based sequence encoder with multi-head attention, is particularly effective in capturing the nonlinear dynamics and combined seasonal and diurnal structures inherent in air temperature time series. This study provides quantitative evidence supporting the applicability of advanced deep learning models to short- to medium-range temperature forecasting based on domestic meteorological observation data. The findings have practical implications for applications requiring accurate temperature prediction, including smart grid energy demand management and weather-related disaster prevention. Future work should extend this framework to multivariate and multi-station settings, as well as to probabilistic forecasting approaches for explicit quantification of predictive uncertainty
Evaluating Short-Term Regime Prediction in the Korean Equity Market
This study presents a large-scale benchmark and a decision-rule-aware evaluation protocol for short-term regime classification in the Korean stock market. Reliable evaluation in this setting is difficult because reported performance is sensitive to sample construction, look-ahead bias, class imbalance, and the decision rule used to convert model scores into predicted classes. The benchmark comprises 8,732,898 stock-day observations of 4,225 KOSPI and KOSDAQ common stocks from January 2012 to February 2026, with six three-class regime targets defined by ±5% and ±10% thresholds on 1-, 5-, and 10-trading-day forward returns. The protocol combines survivorship-bias-mitigating sampling, chronological splits with a 20-trading-day purge gap, training-only preprocessing, leakage checks, and full class-distribution reporting. Given severe class imbalance, models are evaluated using macro F1 and up/down directional F1 rather than accuracy alone. We report reference results for two tree-based baselines, LightGBM and XGBoost, and six tabular deep learning models. Under the default argmax rule, the deep learning models are more recall-oriented on extreme regimes, whereas LightGBM is more conservative. However, after validation-based threshold tuning, LightGBM’s directional F1 improves substantially, and the relative ranking narrows or reverses on several targets. These results show that apparent model superiority depends materially on the decision rule. The contribution of this study is therefore a reproducible benchmark and evaluation protocol, not a claim of universal model superiority or tradable profitability.
Enhancing Stock Price Prediction using StockGPT
This study explores the enhancement of stock price prediction using StockGPT, a transformer-based generative model, by integrating macroeconomic indicators and feature engineering techniques. Using data from U.S. tech stocks and the S&P 500 index, the proposed model demonstrated approximately a 1.16% improvement in RMSE over the baseline. Feature importance analysis using Permutation Importance revealed that the lagged USD Index and the moving average of WTI oil prices significantly contributed to prediction accuracy. The findings underscore the practical impact of incorporating structured external variables into generative models and offer insights into interpretable financial forecasting.
Copyright (c) 2025 AI Journal of BUsiness https://creativecommons.org/licenses/by-nc-nd/4.0
Causal Policy Analysis in Financial Time Series Using Deep Learning X-Learner
This study employs the X-Learner algorithm with deep learning models (MLP, LSTM, GRU, CNN) to causally estimate performance differences between cyclical (T=1) and defensive (T=0) asset classes in financial time-series data. The results show that the MLP-based policy achieved a Sharpe ratio of 0.440, while the LSTM-based policy improved to 0.990, outperforming T1 (0.880) and T0 (0.493). These findings indicate that incorporating temporal dependence through LSTM enhances causal policy learning. By reframing asset allocation as a causal decision-making problem rather than a predictive task, this study provides a foundation for interpretable and adaptive investment strategies in financial markets.
Research on how to increase user engagement of Korean AI tutoring service using token economy
While AI tutoring has strengths in personalization, it is limited by a lack of emotional and social interaction to keep learners engaged. To address this issue, this research proposes a new model that fuses AI tutoring with blockchain-based “token economy” and “gamification”. The token economy provides rewards for learning activities and community contributions (extrinsic motivation), while gamification provides the fun and sense of accomplishment of the learning process itself (intrinsic motivation). The synergy of these two elements aims to build a sustainable learning ecosystem by transforming learning into a “value-creating activity” rather than a mere obligation.
A Hybrid Ensemble Framework for Privilege Anomaly Detection in Multi-Cloud Environments
With the rapid expansion of cloud computing, privilege abuse and escalation attacks in multi-cloud environments are emerging as a core security threat for organizations. Existing research shows limitations due to a lack of context from reliance on single data sources and the constraints of simple ensemble structures. To overcome these limitations, this study proposes a large-scale, multi-modal hybrid ensemble framework that integrates a total of 884,912 rows of data from the IBM Cloud Dataset, Microsoft Cloud Monitoring Dataset, and flaws.cloud CloudTrail data. The research methodology is as follows: (1) Large-Scale Data Integration: A total of 2,204,017 rows of raw data collected from three platforms were refined into an analysis dataset of 769,454 rows through time synchronization and 5-minute window aggregation. (2) Hybrid Ensemble Architecture: Isolation Forest, which is robust for outlier detection in high-dimensional data, and LSTM, specialized for learning sequential event patterns, are combined on a weight-based basis to simultaneously learn spatial and temporal anomaly patterns. (3) Multi-modal Feature Fusion: Infrastructure metrics, time-series service data, and privilege events were integrated to construct 15 standard feature vectors. In the experimental results, the proposed hybrid ensemble model recorded an ROC-AUC score of 0.721, showing superior overall performance compared to individual models. In particular, it demonstrated overwhelming performance in the Precision-Recall Curve analysis compared to other models, proving that it achieved a balance between reducing false positives and detection efficiency, which is crucial in real-world security environments. Furthermore, by explaining the model's prediction results through SHAP (SHapley Additive exPlanations) analysis, it secured transparency that allows security analysts to trust and act upon the findings.
A Study on Anomaly Detection and Hybrid Forecasting Model Using Underground Stormwater Pipe Water Level Time-Series Data for Urban Flood Prediction
As urban flooding risks intensify due to climate change, developing accurate and real-time predictive systems has become critical for disaster preparedness and response. This study proposes an AI-based flood prediction model that leverages real-time rainfall and inflow water level time-series data. The system consists of two core modules: an anomaly detection and auto-correction module to ensure data reliability and quality, and a deep learning-based time-series forecasting module for predicting inflow levels based on rainfall trends. The anomaly detection module employs a reconstruction error method based on Long Short-Term Memory (LSTM) networks to identify abnormal data points that deviate from normal time-series patterns. Detected anomalies are corrected via linear interpolation, where the anomalous data points are removed and replaced with new values estimated from surrounding normal data points. The forecasting module, built upon the LSTM architecture, captures complex rainfall-runoff relationships and temporal dynamics to provide accurate water level predictions. Additionally, hybrid models including ConvLSTM and LSTM-Transformer were designed and evaluated for comparative performance. Experimental results demonstrated that the proposed system achieved over 95% accuracy in anomaly detection and correction. On real-world datasets, the LSTM model outperformed baseline methods, achieving a Mean Squared Error (MSE) of 0.000089, a Mean Absolute Error (MAE) of 0.006778, and a high R2 score of 0.972614. These results suggest the model's potential to enhance the reliability and efficiency of urban flood response decision-making and to contribute to the strengthening of disaster management capabilities.
The Ethical Impacts of AI on Industries and Governance Frameworks for Overcoming Them
This paper As the AI industry continues to flourish today, the ethical implications of AI on various industries are expected to manifest in diverse ways. It is anticipated that AI will impact not only specific sectors but also entire industries and government systems, significantly influencing many aspects of human life. In particular, as the AI industry advances, issues such as excessive data collection, data bias, and misuse of information are becoming increasingly prevalent. Additionally, the ambiguity of accountability is emerging as a social issue due to unclear responsibilities. Furthermore, with the digital divide and the acceleration of job displacement, the threat posed by AI to human employment is becoming a growing concern. While the adoption of AI can bring highly positive impacts by enhancing efficiency and productivity, it is a crucial time to recognize potential ethical challenges in advance and address them through appropriate regulatory governance. To achieve this, industries, governments, local authorities, and academia must collaborate to establish sustainable AI governance and ethical guidelines. This collective effort will help minimize the problems that arise from the development and use of AI.
Research on ethical issues and improvement tasks related to Big Data analysis and utilization of Artificial Intelligence
As Artificial Intelligence(AI)-based data analysis technology develops, it fails to keep up with general ethical standards and gaps arise between them, leading to ethical issues. When AI technology is used to analyze and utilize Big Data, it is no longer a threat to ethical influence, but can be a good turning point that turns a crisis into an opportunity. It is necessary to examine the ethical influence and other issues that these technologies have on the Big Data analysis process. In this paper, it is first examined the relationship between AI and Big Data, and looked at how AI uses Big Data to learn data through Machine Learning algorithms and classify or predict new data. Next, it is investigated how data is collected, what algorithms are used, and how the recommendation system is improved through real-time learning through a case study on Netflix. In relation to this, it is studied issues such as bias and lack of transparency that arise when examining ethical influences of AI technology on the Big Data analysis and utilization process. Here, when the AI ​​decision-making process is opaque, problems such as lack of trust, responsibility issues, difficulty in risk management, and conflicts of interest arise. As a solution to these problems, both legal and technical solutions were presented. As such, Artificial Intelligence is in the process of developing very good technology in itself, but the problem of using Artificial Intelligence for Big Data cannot but be considered. Therefore, if issues such as ethical impact are reviewed and evaluated in advance, and solutions and development tasks are sufficiently prepared, it can be expected to minimize the risks that may arise and maximize the advantages of Artificial Intelligence technology in using Big Data.
Development of a Machine Learning-based Corporate Management Strategy Prediction Model Reflecting ESG Rating Information
In this study, the model reflected not only financial data but also corporate ESG rating information to predict corporate management strategies. Five Machine learning classifiers such as RF, SVM, XGBoost, LightGBM, and CatBoost were used to develop a model that predicts management strategies using machine learning classification techniques. The research results of this paper are as follows. First, the model that used financial data and ESG rating information together showed better prediction performance than the prediction model that used only financial data. In particular, the best performance was shown when the ESG rating was included, followed by the high performance in the management strategy prediction model in the order of governance, environmental, and social ratings. This means that ESG rating information is important in predicting corporate management strategies. Second, among the five machine learning classifiers, LightGBM performed the best when predicting corporate management strategies, followed by CatBoost, XGBoost, SVM, and RF. These results suggest that the Boost classifier is effective in predicting management strategies. The corporate management strategy prediction model developed in this study can be a useful tool for promoting the sustainable development of a company and is expected to provide important insights to managers and investors. In addition, these models are expected to contribute to improving a company's long-term performance by supporting the establishment of sustainable management strategies.
Identifying Key Elements for Future Education
This study was conducted to identify key elements of future education in relation to generative artificial intelligence (AI), particularly in light of rapid technological advancements and the upcoming implementation of AI digital textbooks in 2025. Using text mining techniques, this research systematically analyzed keywords related to generative AI in education. The study examined 6,170 academic dissertations across various educational research fields from 2022 to 2024. The research methodology employed key text mining techniques, specifically keyword frequency analysis and keyword network analysis. The results revealed several significant findings. First, the keyword frequency analysis identified high-frequency terms related to generative AI in education, including 'utilization', 'education', 'design', 'prompt', 'class', and 'learning'. Second, the keyword network analysis demonstrated that 'design' exhibited strong correlations with various educational elements, indicating its central role in the educational application of generative AI. The significance of this study lies in its methodological approach, employing objective text mining techniques to identify key elements of future education. The findings are expected to provide practical implications for establishing educational policies and developing curriculum in the era of generative AI.
Development of Machine Learning Models for Predicting Academic Stress in Adolescents
In the field of social science research, traditional statistical methods have been predominantly used to analyze the major causes of adolescent stress. Most existing studies focus on this approach. In relation to academic stress, this study aims to develop an analysis model based on artificial intelligence and machine learning-driven statistical methods, utilizing big data analysis. Academic stress has a significant impact on the mental health of adolescents, and predicting it can provide valuable insights into students' stress levels, ultimately suggesting effective ways to alleviate their stress. The collected data was sourced from the 2021 Korea Children and Youth Data Archive survey, where 225 common variables were selected. To identify specific variables influencing adolescent academic stress and to choose the most effective machine learning method, five regression models—Random Forest, Linear Regression, Ridge, Lasso, and Gradient Boosting—were applied to determine significant variables. The performance of these regression models was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² Score as assessment metrics. Based on these evaluations, Gradient Boosting was selected as the most effective predictive model. Additionally, an analysis of variable importance was conducted to identify key factors affecting academic stress
A Study on the LLM-Based Software Architecture for Multi Robot Control
This paper examines the latest developments in the convergence of robotics and AI, with a particular focus on analyzing key cases of robot control research using large language models (LLMs). Recent AI technologies such as computer vision, neural networks, reinforcement learning, and LLMs have significantly improved robots' visual recognition, motion control, perception, and decision-making abilities. This study presents an LLM-based robot control architecture that can be applied to small, medium-sized, and home/educational robots without requiring large-scale robot data. This architecture proposes a 'self-describing' method that automatically generates system prompts from robot control programs, allowing non-experts to easily control robots. Additionally, it offers various LLM model selection options to reduce API usage costs and presents a multi-agent architecture for future robot functionality expansion and control of multiple robots. The feasibility of the proposed method is verified through robots tests using a robot simulator. As robot popularization expands beyond manufacturing sites into everyday life, this research is expected to lower the barriers to applying AI technology to small and medium-sized robots.
Copyright (c) 2024 AI Journal of BUsiness https://creativecommons.org/licenses/by-nc-nd/4.0
Reducing Training Time and Enhancing Efficiency in Large Language Models
This study examines the background of recent advancements in artificial intelligence technology and the need for large-scale language models (LLM) and multimodal models (LMM), proposing the Ternary Output Quantization (TOQ) technique to reduce computational complexity and shorten training times for these models. TOQ simplifies the final outputs of models to three discrete values (-1, 0, +1), significantly reducing data processing requirements and enhancing the efficiency of model training and inference processes. Experimental results show that applying TOQ to BERT and DistilBERT models significantly reduces training times and improves both training and validation loss compared to traditional models. This paper presents the potential for wide application of TOQ across various forms of AI models, anticipating that it will facilitate easier training and deployment of models, especially in resource-constrained environments.
The Prioritization of Evaluation Indications for Artificial Intelligence (AI) Business Capabilities in the Financial Sector: Focusing on the Global AI Index
The financial sector is actively considering the adoption of artificial intelligence (AI) in various areas. AI technologies are being introduced not only in key areas of finance, such as marketing, product development, credit evaluation, and risk management, but also in human resources and performance management. As a result, the need to understand business capabilities to achieve successful investment outcomes, including evaluating investment effects and strategies, is increasing. From this perspective, this study aims to identify the capabilities that should be prioritized when investing in AI businesses in the financial sector, based on the Global AI Index, which is a benchmark used for evaluating AI capabilities in 62 countries worldwide. By applying the Analytic Hierarchy Process (AHP) to a group of financial sector experts, the study selected priority rankings for each specific indicator and ultimately identified the priorities for capabilities across seven areas. This study is expected to contribute to minimizing errors and preventing risks when investing in AI within the financial industry.
Study on the Effect of Data Augmentation on Image Learning
This paper aims to demonstrate the effective use of data in AI research by examining the impact of data augmentation on AI learning. This study utilized a subset of the AI Hub’s Ocean Deposition Garbage Image dataset. The accuracy of image classification was measured using CNN’s VGG16 and VGG19. After increasing the data volume through augmentation, the accuracy was re-measured and re-compared. The results of the study indicated that accuracy improved in most cases, leading to the conclusion that data augmentation positively affects image learning. This paper is revision of the author’s master degree thesis. and This research Ocean Deposition Garbage Image used datasets from 'The Open AI Dataset Project (AI-Hub, S. Korea)'. All data information can be accessed through 'AI-Hub (www.aihub.or.kr)'.
AI-Driven Stock Prediction and Investment Strategy Analysis for the Magnificent 7 Tech Giants
This study aims to predict stock prices and develop optimized investment strategies for the "Magnificent 7" companies using AI. By employing Linear Regression, Random Forest, XGBoost, and LSTM models, the study analyzed stock price data for Apple, Amazon, Google, Meta, Microsoft, Nvidia, and Tesla from 2015 to the first half of 2024, along with VIX index and bond yield data. The accuracy of predictions for each model was assessed, and simulations were conducted to observe which model would achieve the highest returns when applied to actual investments. The results showed varying levels of predictive accuracy and performance for each stock, leading to the conclusion that a hybrid approach combining AI and traditional investment strategies could be effective.
A Study on Methods for Summarizing Information from YouTube Channels
This study explored the possibility of summarizing information from YouTube channels. To achieve this, the research compared and analyzed speech-to-text (STT) data from top-viewed videos and comment data from recent videos. The keyword network analysis revealed that comment data could represent the main content of the videos, thereby validating the feasibility of information summarization using only comments. To enhance the reliability of the study, text similarity analysis and statistical verification were conducted. The results of this research are expected to contribute to the utilization of influencer marketing by businesses and to help individual subscribers quickly grasp the main content of channels. Furthermore, it is anticipated that this study will make a practical contribution to the development of efficient processing and analysis methodologies for digital media data.
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