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相关概念视频

Aggregates Classification01:29

Aggregates Classification

306
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...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

299
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Classification of Signals01:30

Classification of Signals

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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...
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Classification of Systems-II01:31

Classification of Systems-II

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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,
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Force Classification01:22

Force Classification

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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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使用花优化驱动的3D-CNN-GRU分类来进行增强的股票市场预测.

B N Jagadesh1, N V RajaSekhar Reddy2, Pamula Udayaraju3

  • 1School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India. nagajagadesh@gmail.com.

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概括

这项研究引入了用于股票市场预测的先进机器学习,通过新的3D-CNN-GRU模型实现了99.14%的准确性. 该方法使用优化的特征选择和超参数调整来进行强大的市场预测.

关键词:
血液凝固算法 血液凝固算法卷积神经网络是一种卷积神经网络.花优化算法 花优化算法有门的经常性单位.股票市场 股票市场 股票市场波段变换的波段变换是什么

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科学领域:

  • * 计算金融学
  • * 人工智能 * 人工智能
  • * 数据科学数据科学

背景情况:

  • * 传统的统计模型不足以满足当前市场预测需求.
  • *全球对市场预测的兴趣推动了先进技术的采用.
  • *机器学习和深度学习为财务预测提供了强大的工具.

研究的目的:

  • * 探索用于股票市场预测的机器学习和深度学习.
  • * 提出包括特征选择,数据预处理和分类在内的综合方法.
  • * 开发和评估一种新的混合深度学习模型,以提高预测准确度.

主要方法:

  • *波形变换用于数据清理和降噪.
  • * 子优化算法 (DOA) 用于高效的特征选择.
  • * 一个新的混合3D-CNN-GRU模型用于股票市场数据分析.
  • * 血液凝固算法 (BCA) 用于超参数调节.

主要成果:

  • * 取得了99.14%的惊人的预测准确度.
  • * 在股票市场预测方面表现出稳健性和有效性.
  • *使用花优化算法识别了关键功能.

结论:

  • * 3D-CNN-GRU混合模型在股票市场预测方面显示出重大前景.
  • * 通过优化功能选择和超参数调整来增强模型性能.
  • *未来的研究应该探索更广泛的数据集和各种金融背景,以实现概括性.