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体育教育における人工知能アルゴリズムの選択のためのハイブリッド q-rung 線形ダイオパントのぼんやりとした WASPAS アプローチ
Yuanzhen Ni1, Fei Wang2, Hongzhen Zhang3
1Sports and Artificial Intelligence, School of Physical Education, Shandong University, Jinan, 250061, China. yuanzhen_ni@163.com.
Scientific reports
|September 1, 2025
まとめ
この研究は,体育教育 (PE) で人工知能 (AI) を選択するためのハイブリッドの意思決定モデルを導入します. このモデルは,PEアプリケーションのトップAIアルゴリズムとして,コンボリューションニューラルネットワークベースのモーション分析 (CNN-MA) を効果的に識別します.
科学分野:
- 教育技術
- 人工知能
- 意思決定科学
背景:
- 身体教育 (PE) に人工知能 (AI) の統合が拡大する中で,アルゴリズム選択の強力な意思決定 (DM) の方法論が必要になっています.
- 既存の方法は,PEの複数の,潜在的に矛盾する基準を介してAIモデルを評価する際に固有の不確実性と曖昧さで苦労しています.
研究 の 目的:
- ウェイト・アグレゲート・サム・プロダクト・アセスメント (WASPAS) テクニックとq-rung 線形ダイオファインテイン・フージ・セット (q-RLDFS) フレームワークを組み合わせた新しいハイブリッドDMモデルを提案する.
- PE領域におけるAIアルゴリズムを評価するための構造化され,不確実性に対応する方法の必要性を解決する.
- PE環境におけるAIモデルの評価の解釈性と信頼性を向上させる.
主な方法:
- 柔軟なスコア付け (WSMとWPMを組み合わせる) のためのWASPASと,高次元のぼんやりしたモデリングと専門家の躊躇に対処するためのq-RLDFSを統合した2層のフレームワーク.
- 5つのAIアルゴリズム (CNN-MA,RL-TO,ES-EP,HAI-NLP,WSDMA) を8つの主要なPE関連基準に対して評価した.
- モデルの強度と信頼性を検証するための感度と比較分析.
主要な成果:
- コンボリュショナルニューラルネットワークベースの運動分析 (CNN-MA) は,PEで実施する最も効果的なAIアルゴリズムとして特定されました.
- 補強学習ベースのトレーニング最適化器 (RL-TO) は2番目に効果的なソリューションとしてランク付けされました.
- 提案されたモデルは,厳格な感度と比較分析によって,堅実性と信頼性を証明した.
結論:
- 開発されたハイブリッドDMモデルは,PEにおけるAI採用のためのスケーラブルで,透明で,実用的な意思決定支援ツールを提供します.
- 教育者,管理者,政策立案者向けに,体育における戦略的なAI導入に関する実行可能な推奨事項を提供します.
- PEセクターにおけるAI技術の情報に基づいた選択と応用を推進することに大きく貢献します.
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