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Data Collection by Experiments01:13

Data Collection by Experiments

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 clinical trial...
Data Collection by Survey01:07

Data Collection by Survey

The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
Data Collection by Observations01:08

Data Collection by Observations

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...
Data Collection I01:30

Data Collection I

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 data...
Data Collection II01:29

Data Collection II

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 family,...
Data Collection III01:05

Data Collection III

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 patient.

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Related Experiment Videos

Big Data Fusion Method Based on Internet of Things Collection.

Tianrong Zhang1, Hongying Li2, Tian Jin2

  • 1Zhejiang Shuren University, Hangzhou, Zhejiang 310000, China.

Computational Intelligence and Neuroscience
|May 5, 2022
PubMed
Summary

This study introduces a novel deep learning model for fusing heterogeneous time series and text data. The FC-SAE model enhances data fusion, significantly improving prediction accuracy for complex datasets.

Related Experiment Videos

Area of Science:

  • Data Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Increasing data complexity and volume necessitate advanced data fusion techniques.
  • Traditional methods struggle with heterogeneous data types like time series and text.
  • Deep learning offers a promising approach for effective data fusion in big data environments.

Purpose of the Study:

  • To address the challenge of fusing heterogeneous time series and text data.
  • To improve the accuracy of time series prediction by incorporating text data insights.
  • To propose a novel data fusion model, FC-SAE, for enhanced data utilization.

Main Methods:

  • Utilized GloVe and Convolutional Neural Networks (CNN) for text data feature extraction.
  • Employed a Fully Connected (FC) neural network for time series data potential feature extraction.
  • Implemented a Stacked Autoencoder (SAE) model for data fusion and relationship discovery.

Main Results:

  • The proposed FC-SAE model effectively fuses heterogeneous time series and text data.
  • Feature extraction using GloVe, CNN, and FC networks captured relevant data characteristics.
  • The data fusion process significantly improved prediction accuracy compared to traditional methods.

Conclusions:

  • The FC-SAE model demonstrates superior performance in fusing time series and text data.
  • Incorporating text data features enhances the predictive power of time series models.
  • Deep learning-based data fusion is crucial for handling complex, heterogeneous big data.