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関連する概念動画

Applications of Stress01:04

Applications of Stress

400
Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
The...
400
Stress Prevention and Stress Management Techniques V01:28

Stress Prevention and Stress Management Techniques V

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A social support system is a structured network of personal relationships that provides assistance to individuals facing various challenges, offering a buffer against psychological and physical stressors. This network may consist of family members, friends, neighbors, colleagues, or other community members who provide resources and companionship. Social support can take many forms, including advice, emotional comfort, practical help, and companionship. Research indicates that these networks can...
63
Physiological Foundation of Stress01:24

Physiological Foundation of Stress

157
Stress triggers a coordinated physiological response involving the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. This dual activation ensures that the body is prepared for both immediate and prolonged stress management. The process begins with the perception of a stressor. This initial phase activates the SNS, leading to the rapid release of adrenaline (epinephrine) from the adrenal glands.
Role of the Sympathetic Nervous System
Adrenaline triggers the...
157
Stress Prevention and Stress Management Techniques IV01:26

Stress Prevention and Stress Management Techniques IV

62
Stress often leads to unhealthy habits like smoking, excessive drinking, and overeating, which offer short-term relief but ultimately increase long-term health risks. These behaviors create a cycle that temporarily lowers stress levels but can result in severe long-term health consequences. Breaking these habits is essential to reduce the risk of chronic diseases and improve overall well-being. Three primary changes that support better health include quitting smoking, reducing alcohol intake,...
62
Stress Prevention and Stress Management Techniques I01:26

Stress Prevention and Stress Management Techniques I

92
Stress prevention and management are crucial for maintaining well-being and building resilience. Techniques to manage stress include cultivating qualities like conscientiousness, a sense of personal control, and self-efficacy. Each of these traits significantly reduces stress and promotes healthier lifestyle choices and outcomes.
Conscientiousness
Conscientious individuals tend to be organized, responsible, and disciplined. They prioritize completing tasks and following structured routines,...
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Stress Prevention and Stress Management Techniques II01:23

Stress Prevention and Stress Management Techniques II

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Personality types, particularly Type A and Type B, significantly influence how individuals respond to stress. These personality distinctions are marked by varying levels of ambition, competitiveness, and coping styles, all of which shape an individual's resilience to stressors.
Type A Personality: Driven and Easily Stressed
Individuals with Type A personalities are often highly competitive and ambitious and operate with a strong sense of urgency. Commonly labeled as...
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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
PubMed
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.

キーワード:
ディープモジファストテキストフォックス最適化残留ネットワークストーミングストレス検出トークン化

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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