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Quantum Numbers02:43

Quantum Numbers

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It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
34.8K
Classification of Signals01:30

Classification of Signals

485
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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Aggregates Classification01:29

Aggregates Classification

329
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...
329
Quantitative Analysis01:12

Quantitative Analysis

312
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
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Classification of Systems-I01:26

Classification of Systems-I

192
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:
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
415

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Updated: Jul 13, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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量子计算和机器学习用于阿拉伯语言的社交媒体情绪分类在社交媒体中的情绪分类.

Ahmed Omar1, Tarek Abd El-Hafeez2,3

  • 1Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt. ahmed.omar@mu.edu.eg.

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概括

量子计算和机器学习显示了阿拉伯文文档分类的高精度. 量子计算在大型数据集上的准确性和速度略高于经典的ML,而ML在较小的数据集上更快.

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科学领域:

  • 自然语言处理自然语言处理.
  • 计算语言学 计算语言学
  • 量子计算是一种量子计算.

背景情况:

  • 越来越多的阿拉伯数字数据需要先进的文档分类.
  • 量子计算和机器学习 (ML) 对文档分类具有前景.
  • 对于阿拉伯语言数据的这些技术的研究是有限的.

研究的目的:

  • 为了比较量子计算和经典机器学习对阿拉伯文文档分类的性能.
  • 为了评估两个方法的准确性,精度,回忆和F1分数.
  • 分析不同大小的数据集的处理时间.

主要方法:

  • 量子计算和经典ML算法的比较分析.
  • 用阿拉伯语推文的两个数据集进行情绪分析.
  • 使用准确度,精度,回忆和F1分数等指标评估性能.

主要成果:

  • 量子计算和机器学习都在阿拉伯情绪分析中取得了很高的准确性.
  • 量子计算在一个大数据集 (213,465条推特) 上,在准确性和速度方面略高于ML.
  • 在较小的数据集 (44,000条推文) 上,ML显示出更高的准确性,处理时间相似.

结论:

  • 量子计算对于大数据集上的高精度阿拉伯情绪分析是有效的.
  • 经典ML为较小的阿拉伯语数据集提供了更快的处理速度.
  • 这项研究突出了量子计算对阿拉伯文档分类挑战的潜力.