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相关概念视频

Data Collection by Experiments01:13

Data Collection by Experiments

23.8K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
23.8K
Data Collection by Observations01:08

Data Collection by Observations

11.7K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
11.7K
Data Collection II01:29

Data Collection II

7.9K
The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
7.9K
Data Collection I01:30

Data Collection I

6.0K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
6.0K
Data Collection III01:05

Data Collection III

2.7K
The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
2.7K
Aggregates Classification01:29

Aggregates Classification

301
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...
301

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相关实验视频

Updated: Jun 3, 2025

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions
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BidCorpus:为公共采购提供多方面的学习数据集.

Weslley Lima1, Victor Silva1, Jasson Silva1

  • 1Federal University of Piauí. Campus Universitário Ministro Petrônio Portella. Teresina, Piauí, Brazil.

Data in brief
|January 10, 2025
PubMed
概括

我们创建了BidCorpus,这是一个用于分析公共采购文件的新数据集. 这一数据集有助于自动检测招标公告中的欺诈行为,提高公共管理的效率和透明度.

关键词:
贝尔特 (BERT) 公司招标公告 招标公告在NLP中,我们使用NLP.监督的弱点 监督的弱点

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科学领域:

  • 信息科学 信息科学 信息科学
  • 公共管理 公共管理
  • 计算机科学 计算机科学

背景情况:

  • 数字化转型提高了公共采购的效率,透明度和竞争.
  • 在公共管理中,数据分析和监督的自动化至关重要.
  • 手动分析非结构化采购文件耗时且效率低下.

研究的目的:

  • 介绍BidCorpus,这是一个关于公共采购招标公告的全面数据集.
  • 促进公共采购文件的自动化分析和欺诈检测.
  • 为公共采购领域的研究人员提供宝贵的资源.

主要方法:

  • 收集了数千份巴西公共采购招标公告.
  • 利用弱监督,手动标签和基于BERT的数据注释模型.
  • 在注释数据集上训练和评估机器学习模型.

主要成果:

  • 在BidCorpus上训练的模型在各种实验中实现了超过80%的准确性.
  • 这些模型证明了针对旨在逃避欺诈检测的故意修改的稳定性.
  • 开发并验证了用于分析公共采购数据的机器学习模型.

结论:

  • BidCorpus是推动公共采购研究的宝贵资源.
  • 对招标公告的自动化分析可以显著提高欺诈检测和效率.
  • 公开可用的资源支持数字公共采购领域的进一步发展.