Tomohiro Hayashida 研究室

AI 要約（直近 5 年の研究成果）

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

研究成果（51 件）

• [2026] Learner Prediction Using a Multi-stage Machine Learning Model and Analysis of Learner Characteristics with SHAP Values
  DOI: https://doi.org/10.1109/educon67543.2026.11574055
• [2026] Genetic Programming with Adaptive Population Restructuring for Dynamic Flexible Job Shop Scheduling
  DOI: https://doi.org/10.3390/math14061000
• [2025] Optimal Switch Placement in Distribution Systems for Constraints Satisfaction in Faulted Conditions
  DOI: https://doi.org/10.1002/eej.70003
• [2025] Multi-Stage Machine Learning Model for Classification and Prediction of Learners
  DOI: https://doi.org/10.1109/iwcia68353.2025.11479787
• [2025] ELPSO-C: A Clustering-Based Strategy for Dimension-Wise Diversity Control in Enhanced Leader Particle Swarm Optimization
  DOI: https://doi.org/10.3390/appliedmath5040159
• [2025] A Dual-stage Dual-population Evolutionary Algorithm for Distribution System Reconfiguration with Severe Constraints
  DOI: https://doi.org/10.1109/smc58881.2025.11343335
• [2025] Data sharing procedure for effective learnings in deep reinforcement learning of multiagent systems
  DOI: https://doi.org/10.1109/smc58881.2025.11343484
• [2025] Optimal Switch Placement in Distribution Systems for Constraints Satisfaction in Faulted Conditions
  DOI: https://doi.org/10.1541/ieejpes.145.573
• [2025] Data Sharing on Deep Reinforcement Learning for Multi-Agent Systems
  DOI: https://doi.org/10.5687/iscie.38.137
• [2025] Distribution System Reconfiguration using Multi-Stage Evolutionary Strategy for Constraints Satisfaction in Faulted Conditions
  DOI: https://doi.org/10.1541/ieejpes.145.457

科研費（0 件）

まだデータがありません（KAKEN 取り込み後に表示）。

所属学会・役職（0 件）

まだデータがありません（学会データ連携後に表示）。