一个新的数据集的古普塔弓箭手类型的硬币用于基于机器学习的分类
Ishtiak Al Mamoon1, Zakaria Shams Siam2, Abdul Akhir Al Galib1
1Urology and Transplantation Foundation, Bangladesh.
Data in brief
|October 16, 2024
概括
这项研究引入了古古普塔弓箭手硬币的新数据集,这对机器学习在数字学中至关重要. 该资源有助于对这些具有历史意义的文物进行分类.
科学领域:
- 数字学 数字学是一门学科.
- 考古学的考古学
- 计算机科学 计算机科学
背景情况:
- 由于信息和文化遗产的多样性,对古代硬币的分类具有挑战性.
- 机器学习 (ML) 的进步需要相关的数据集来完成诸如硬币分类等任务.
- 古古普塔弓箭手类型的硬币很少,设计复杂,造成识别困难.
研究的目的:
- 为了展示古古普塔弓箭手类型硬币图像的新,高质量的数据集.
- 为数码学研究和机器学习应用建立可靠的资源.
- 通过硬币分析促进对古代印度考古学的研究.
主要方法:
- 从经过验证的私人收藏和拍卖行收集的硬币图像.
- 确保所有图像都是真实的古古普塔弓箭手类型硬币.
- 使用视觉检查和数字文献进行注释的硬币图像.
主要成果:
- 一个精心策划的数据集真实的古古普塔弓箭手类型的硬币图像已经创建.
- 该数据集遵守了数字学研究的高标准.
- 该数据集为基于ML的分类和考古洞察提供了基础.
结论:
- 这一新型数据集解决了对数码学和机器学习领域可靠资源的需求.
- 这项工作支持对古普塔弓箭手类型硬币的识别和研究.
- 该数据集可以为更深入地了解古代印度历史和考古学做出贡献.
相关概念视频
Aggregates Classification
305
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...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
305
How Data are Classified: Numerical Data
27.8K
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...
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
27.8K
Classification of Systems-I
176
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:
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:
176
Methods of Classification and Identification
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
Classification of Systems-II
136
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,
136
Quantifying and Rejecting Outliers: The Grubbs Test
1.5K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K


