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相关概念视频

Classification of Signals01:30

Classification of Signals

556
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
556
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

3.1K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Classification of Systems-I01:26

Classification of Systems-I

221
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
221
Classification of Systems-II01:31

Classification of Systems-II

183
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
183
Aggregates Classification01:29

Aggregates Classification

350
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
350
Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

Updated: Jul 25, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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使用基于演化优化的模糊反复神经网络的进化优化,SMS情绪分类.

Ulligaddala Srinivasarao1, Aakanksha Sharaff1

  • 1Department of Computer Science and Engineering, National Institute of Technology Raipur, Chhattisgarh, 492010 India.

Multimedia tools and applications
|June 26, 2023
PubMed
概括

这项研究介绍了一种基于模糊的新型循环神经网络与哈里斯优化 (FRNN-HHO) 以改进垃圾邮件和火腿电子邮件的分类. FRNN-HHO模型通过进行分类后的情绪分析来提高准确性,在多个数据集中获得高AUC分数.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 对于有效的电子邮件管理来说,对垃圾邮件和火腿邮件的分类至关重要.
  • 现有的方法可以错误地将垃圾邮件分类为火腿,从而降低整体准确性.
  • 情感分析提供了一个潜在的途径来改进消息分类.

研究的目的:

  • 开发一个先进的架构,准确地对垃圾邮件和恶意消息进行分类.
  • 通过将情绪分析集成到过程中来提高分类准确性.
  • 引入基于模糊的循环神经网络,使用哈里斯霍克优化 (FRNN-HHO) 进行优化.

主要方法:

  • 使用内核极端学习机器 (KELM) 分类器进行初始垃圾邮件和恶意消息分类.
  • 实现了一个基于模糊的循环神经网络与哈里斯霍克优化 (FRNN-HHO) 进行分类后情绪分析.
  • 使用标准指标评估性能,包括准确性,回忆,精度,F测量,RMSE和MAE.

主要成果:

  • 拟议的FRNN-HHO架构在区分垃圾邮件和垃圾邮件方面表现出卓越的性能.
  • 实现了高的曲线下面面积 (AUC) 值:SMS为0.9699;电子邮件为0.958;垃圾邮件杀手数据集为0.95.
关键词:
模糊的循环神经网络 模糊的循环神经网络哈里斯·霍克优化的优化核心极端学习机器的核心.在SMS中,SMS是SMS.情绪分析是一种情绪分析.牛排和火腿和火腿.

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  • 通过解决错误分类,情绪分析集成显著提高了分类准确性.
  • 结论:

    • 通过情绪分析,FRNN-HHO模型有效地提高了垃圾邮件和火腿分类的准确性.
    • 这种方法为改进文本挖掘和消息过系统提供了强大的解决方案.
    • 该研究验证了FRNN-HHO在各种数据集中的有效性,突出了其实际适用性.