ディープラーニングモデルを用いた 新しく効率的なパーソナライズされたストレス検出技術
Ulligaddala Srinivasarao1, Gopisetty Rathnamma2, M Satish Kumar3
1Department of CSE, GITAM (Deemed to be) University, Rudraram Village, Hyderabad, India. ulligaddalasrinu@gmail.com.
Scientific reports
|August 21, 2025
まとめ
この研究は ソーシャルメディアのテキストから ストレスを検出するための 効果的な方法を紹介しています この新しいアプローチは,高度なテキスト表現とディープラーニングモデルを統合し,ストレスを検出する上で高い精度を達成します.
科学分野:
- コンピュータ言語学
- 人工知能
- 心理学
背景:
- ストレスは 大人や高齢者の健康に 深刻な影響を及ぼし 慢性的な病気につながります
- ソーシャルメディアのテキストからストレスを検出することは 複雑さと計算上の要求により 課題を提示します
- 既存の機械学習とディープラーニングモデルは 長いトレーニング時間や機能の制限などに 直面しています
研究 の 目的:
- ソーシャルメディアのテキストから ストレスを検出するための 効率的で正確な技術を開発する.
- 訓練時間や機能利用の観点から,既存の方法の限界を克服する.
- 新しいモデル統合と最適化を使用してストレスの検出の精度を向上させる.
主な方法:
- 先進的なテキスト表現技術の統合:FastText,ワード表現のためのグローバルベクター (Glove),DeepMoji,XLNet.
- 精密なストレスを検出するために,残余ネットワーク (DSC-ResNet) による深度別れコンボリューションを使用します.
- ハイパーパラメータチューニングのカオティックフェネックフォックス最適化アルゴリズム (CFFO) を採用.
主要な成果:
- 提案された技術は98.42%の高い精度を達成しました.
- 精度,リコール,特異性,F1スコアはそれぞれ97. 58%,98. 12%,98. 28%,98. 38%と報告されました.
- このモデルは既存の技術と比較して優れた性能を示した.
結論:
- テキスト表示とDSC-ResNetの新しい統合は,ストレス検出のための効率的なソリューションを提供します.
- 提案された方法は,以前のアプローチの限界を効果的に解決し,高い精度と性能を提供します.
- このテクニックは ソーシャル・メディアの分析による メンタル・ヘルス・モニタリングの 実用的な応用が期待されています
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