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Once a ligand binds to a receptor, the signal is transmitted through the membrane and into the cytoplasm. The continuation of a signal in this manner is called signal transduction. Signal transduction only occurs with cell-surface receptors, which cannot interact with most components of the cell, such as DNA. Only internal receptors can interact directly with DNA in the nucleus to initiate protein synthesis. When a ligand binds to its receptor, conformational changes occur that affect the...
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序列级联混合适应型基于深度网络的歌词文字分类使用优化方法.

R L Jasmine1, Saswati Mukherjee2, C R Rene Robin3

  • 1Teaching Fellow, Department of Information Science and Technology, College of Engineering, Guindy Campus, Guindy, Chennai, 600025, Tamil Nadu, India. mahil.jasmine@gmail.com.

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|February 12, 2026
PubMed
概括

一个新的深度学习模型,SCHADNet,有效地根据情感内容对歌词进行分类. 这种先进的自然语言处理 (NLP) 方法提高了音乐的发现和内容的适合性,以满足不同受众的需求.

关键词:
改进了海洋捕食者算法.歌词的文字分类 文字的分类序列级联混合动力自适应深度网络基于变压器的双向长期短期存储器

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科学领域:

  • 人工智能的人工智能
  • 音乐信息检索 音乐信息检索
  • 自然语言处理自然语言处理.

背景情况:

  • 数字音乐的转变增加了歌曲的多样性,需要进行内容分析以发现和适合年龄.
  • 情感内容分析是音乐发现的关键方法,但对歌词的分类仍然具有挑战性.
  • 深度学习在自然语言处理 (NLP) 中显示出希望,但其用于过不适当的音乐歌词的应用有限.

研究的目的:

  • 提出基于深度学习的歌词文本分类过程,用于识别和过不合适的音乐.
  • 开发和评估一种新的深度学习模型,根据歌曲的内容对歌词进行分类.

主要方法:

  • 文本数据进行了预处理,并被输入到序列级联混合适应深度网络 (SCHADNet) 模型中.
  • SCHADNet集成了基于变压器的双向长期短期存储器 (Trans-BiLSTM) 与门式循环单元 (GRU).
  • 模型参数使用改进的海洋捕食者算法 (IMPA) 进行了优化.

主要成果:

  • 该SCHADNet模型实现了高分类性能.
  • 准确率达到93.4%,回忆率为93.47%,负预测值 (NPV) 为99.2%.
  • 数字分析证明了该模型在经典文本分类技术上的优势.

结论:

  • 拟议的深度学习模型显著增强了歌词文本分类.
  • SCHADNet提供了一种有效的解决方案,用于过不适当的音乐内容.
  • 该模型的高性能表明其在改进音乐推和内容调节系统方面的潜力.