Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the “drone” keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI’s power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.

The aim of this study is to research attributes of fishermen's occupational accidents for investigating the measure of risk control on situational condition in the Korean offshore and coastal fishing vessel. Using data of fishermen's occupational accidents are from National federation of fisheries cooperatives for 2013. The results were as belows; Occupational accident occurrence rate was 29.5‰, slips & trips and struck by object and contact with gear were shown severe occurrence pattern. Occupational accident occurrence rate of offshore fisheries was 130.2‰, coastal was 16.9‰, specially the risk rates were severely high in several type of danish seine, stow net and offshore trap. Death rate by accidents was 10.6‰ and by fall into the water in occurrence pattern was 5.5‰.

This research examines the alternative ways of estimating the coefficient of non-diversifiable risk, namely beta coefficient, in Capital Asset Pricing Model (CAPM) introduced by Sharpe (1964) that is an essential element of assessing the value of diverse assets. The nonparametric methods used in this research are the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MMestimator). The Jackknife, the resampling technique, is also employed to validate the results. According to finance literature and common practices, these coecients have often been estimated using Ordinary Least Square (LS) regression method and monthly return data set. The empirical results of this research pointed out that the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) performed much better than Ordinary Least Square (LS) in terms of eciency for large-cap stocks trading actively in the United States markets. Interestingly, the empirical results also showed that daily return data would give more accurate estimation than monthly return data in both Ordinary Least Square (LS) and robust Least Trimmed Square (LTS) and Maximum likelihood type of Mestimator (MM-estimator) regressions.