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 objective of this study is to examine the relationship between tomato consumption and the risk of metabolicsyndrome (MetS) in Korean middle aged women. Data from the combined 2009-2011 Korean National Health andNutrition Examination Survey (KNHANES) was analyzed. Tomato intake was assessed using the algorithms devel-oped to analyze the demographic data of intakes of different tomato based food commodities such as “tomato, raw”,“tomato, tomato juice”, “tomato, tomato canned”, “tomato sauce”, and “tomato ketchup”. Korean women(n=11,251) were subgrouped according to the number of the MetS risk factor (RF 0, RF 1-2, RF 3). Anthropo-metric parameters, lipid profiles, fasting glucose, and tomato intake were analyzed. Corresponding to the number ofthe MetS RF, there was a decrease in tomato intake (18.90±1.78, 16.67±1.23 and 12.84±1.23; P<0.001). Tomatointake showed a negative correlation with systosolic blood pressure, BMI, waist, and triglyceride. HDL cholesterolalso showed a significant correlation with tomato intake (r=0.023, P<0.05). In summary, the results show a rela-tionship between tomato intake and MetS in Korean middle aged women.