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相关概念视频

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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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Protein Networks

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Regression Analysis01:11

Regression Analysis

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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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Statistical Analysis: Overview01:11

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Classification of Leukocytes01:30

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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使用CNN功能和集合学习模型进行科学文本引文分析.

Khaled Alnowaiser1

  • 1Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.

PloS one
|May 28, 2024
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概括
此摘要是机器生成的。

这项研究引入了一个新的框架来分析研究文章中的引用情绪,超越了简单的重要性指标. 拟议的方法使用投票分类器和卷积神经网络准确地分类引用情绪,改善学术影响评估.

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科学领域:

  • 图书统计学 图书统计学
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 引用对于评估学术成就至关重要,但往往被平等对待,忽视了定性细微差别.
  • 目前的方法主要依赖于定量指标,忽视了引用所传达的情绪和重要性.
  • 现有的定性引用分析通常使用二进制分类 (重要/不重要),限制其范围.

研究的目的:

  • 开发一个新的框架,用于研究文章中文本引用的多类情绪分析.
  • 为了应对引用情绪分析中数据不平衡的挑战.
  • 在引用评估中与定量指标一起纳入定性方面.

主要方法:

  • 使用卷积神经网络 (CNN) 提取特征.
  • 使用组合后勤回归 (LR) 和随机梯度下降 (SGD) 的投票分类器进行分类.
  • 处理类不平衡与合成少数群体过量采样技术 (SMOTE).

主要成果:

  • 拟议的框架实现了0.99.9的完美得分 (准确性,精度,回忆,F1).
  • 与使用术语频率 (TF) 和TF-反向文档频率 (TF-IDF) 的传统方法相比,表现出卓越的性能.
  • 在不平衡的数据集上有效处理多类情绪分类.

结论:

  • 这项研究通过结合定性评估,推进了学术引用中的情感分析.
  • 拟议的框架为评估引文影响提供了一种更细致,更准确的方法.
  • 强调需要整合定性情绪分析来进行全面的引用评估.