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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

29.3K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
29.3K
Classification of Systems-I01:26

Classification of Systems-I

742
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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Classification of Systems-II01:31

Classification of Systems-II

651
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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Aggregates Classification01:29

Aggregates Classification

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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...
1.0K

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Updated: May 5, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

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孟加拉国甜点分类的综合数据集

Mushfiqur Rahman1, Jahid Hasan1

  • 1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

Data in brief
|November 25, 2024
PubMed
概括

这项研究引入了一个新的数据集,用于使用深度学习对孟加拉甜点进行分类. 开发的模型达到98%的准确性,有助于遗产的保存.

科学领域:

  • 计算机科学 计算机科学
  • 食品科学 食品科学 食品科学
  • 文化遗产研究 文化遗产研究

背景情况:

  • 由于不同的传统,对甜点的分类是复杂的.
  • 孟加拉国甜点缺乏一个全面的数据集.
  • 自动分类可以帮助保护文化遗产.

研究的目的:

  • 创建一个传统的孟加拉国甜点的高质量的图像数据集.
  • 开发和评估孟加拉国甜点分类的深度学习模型.
  • 为了建立一个图像识别的基准.

主要方法:

  • 策划了孟加拉国甜点的高分辨率图像的多样化收藏.
  • 采用图像处理技术和深度学习算法,包括MobileNet.
  • 使用标准化指标评估模型性能.

主要成果:

  • 在甜点分类方面取得了98%的整体测试准确度.
  • 在精选的数据集上证明了深度学习模型的有效性.
  • 为图像分类研究建立了宝贵的资源.

结论:

关键词:
孟加拉国美食 孟加拉国美食深度学习是一种深度学习.甜点的分类 甜点的分类图像数据集是一组图像数据集.图像处理 计算机视觉 计算机视觉

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  • 开发的数据集和模型在分类孟加拉国甜点方面取得了重大进展.
  • 这项工作支持通过技术应用来保护传统.
  • 这些发现鼓励进一步研究用于文化应用的机器学习.